This paper presents matheuristics for routing a heterogeneous group of capacitated unmanned air vehicles (UAVs) for complete coverage of ground areas, considering simultaneous minimization of the coverage time and locating the minimal number of refueling stations. Whereas coverage path planning (CPP) is widely studied in the literature, previous works did not combine heterogeneous vehicle performance and complete area coverage constraints to optimize UAV tours by considering both objectives. As this problem cannot be easily solved, we designed high-level path planning that combines the multiobjective variable neighborhood search (MOVNS) metaheuristic and the exact mathematical formulation to explore the set of nondominated solutions. Since the exact method can interact in different ways with MOVNS, we evaluated four different strategies using four metrics: execution time, coverage, cardinality, and hypervolume. The experimental results show that applying the exact method as an intraroute operator into the variable neighborhood descent (VND) can return solutions as good as those obtained by the closest to optimal strategy but with higher efficiency. As mobile sensing systems, unmanned aerial vehicles (UAVs) are a fast and efficient option to perceive the environment due to their quick response capabilities. The UAVs can assume autonomous behaviors performing complex tasks at a low operational cost, providing strong motivations to adopt these vehicles for various activities such as environmental monitoring, search and rescue activities, and precision agriculture.Despite the remarkable benefits of using UAVs in such applications, it is important to highlight inherent limitations of the different UAV platforms, which must be considered when planning paths to cover a target area. Nowadays, the UAVs are classified as fixed-wing, rotary-wing (single and multirotor), and hybrids. Fixed-wing UAVs are characterized by their long endurance and fast flight speed, suitable for extensive area coverage applications such as monitoring power lines, roads, and pipelines. This UAV type requires some setup time to be launched, as this process usually has to be performed manually . Some moIn a different direction, some current solutions for route planning in coverage tasks tend to mitigate the limitations of using a particular UAV. An area decomposition strategy to generate paths with the minimum number of turns for fixed-wing UAVs, aiming to maximize coverage capability, was explored by Avellar et al. . Since tA solution for planning routes to complete coverage using multirotors is presented in . Unlike The possibility of recharging the UAVs in different cells of the grid is presented in . HoweverAssunção et al.  indicateThus, to improve the Brazilian environmental command and control strategy, the development of strategies, which can be used to plan fast and effective monitoring operations supplying the governmental authorities with precise information to identify and control small-scale deforestation and forest degradation, must be promoted . PlanninIn this paper, we adopt the same area decomposition strategy as explored earlier by our team  and propDue to the high costs of installing the recharging stations in the preservation areas, this high-level planner aims to minimize both the longest route among all the routes planned for the team and the number of necessary recharging stations, locating the places to open such facilities. Note that both minimization objectives (the longest flight time and the number of recharge stations) can be conflicting. Decreasing the number of recharging stations can force a UAV to make a detour to recharge, thereby increasing its coverage time.The applied decomposition method represents each coverage row by a pair of vertices at its extremities. As the vehicle must visit these vertices, we use their positions as references to the recharging station locations. A conventional methodology to find the optimal solution for this multiobjective problem is to reduce it to a sequence of mono-optimization problems that can be modeled and theoretically solved by a commercial solver. However, the costs to compute a proven optimal solution is prohibitive in practice since this routing problem is known to be nondeterministic polynomial-time (NP)-hard.We propose and evaluate four matheuristics distinguished by different forms of interactions between the multiobjective variable neighborhood search (MOVNS) metaheuristic and the exact method to solve this problem. The first matheuristic implemented uses the MOVNS to divide the coverage area among the UAVs and apply the exact method to build their routes. Every modification performed by the MOVNS is rerouted and evaluated to check if an improvement is obtained, which results in a high call rate of the exact method. This issue was mitigated by adopting heuristics to rearrange the route on the MOVNS and perform the route evaluation regardless of the exact method. One of these uses the exact method to build only the initial solution, and the other two strategies apply it to improve the solution at specific steps in the method. To evaluate the effect of these modifications, we analyzed the algorithm’s performance considering the computation time to return its solutions and their quality regarding the coverage, cardinality, and hypervolume metrics.The main contributions of this paper are: (1) presenting a multiobjective approach for routing long-term complete area coverage mission with multiple heterogeneous UAVs, simultaneously considering the time to complete the mission and the number of recharging station to optimize the coverage tours; (2) locating the recharging stations that can be shared among the vehicles; (3) developing, evaluating, and comparing different heuristics that return the set of nondominated solutions to minimize the longest time among all the vehicles and the number of recharging stations.The remainder of this work is organized as follows: In Coverage path planning (CPP) has been addressed by several studies, usually for robot applications in monitoring/surveillance missions that require the coverage of some determined target points in the environment. In particular cases, these monitoring missions must persist for a long time, following a path that maximizes each target’s visit frequency. Persistence is usually solved by implementing speed controllers for the robots to stay perpetually monitoring the environment, as proposed in . The autA mixed-integer linear program (MILP) to find the fastest trajectory to cover a preassigned set of waypoints considering the collision avoidance constraints for UAV is presented in . This soDille and Singh  proposedGuerriero et al.  presenteAn important issue in practice that emerged from monitoring missions is the limited capacity of the vehicles. In this case, long-term coverage applications must consider the vehicle’s capacity constraints and possible tour interruptions for replenishment. As observed from the literature, several related works have investigated routing strategies using multiple depots to replenish the vehicles in logistic operations ,29,30,31A particular case of this locating problem considers the depots as recharging stations due to the vehicle fuel constraint. This approach is usually applied for routing electric vehicles (EVs), which may require regular recharging, which can be a potential issue for EV applications. In this sense, Chung et al.  presenteFor UAVs with VTOL capability, a different charging station technology was proposed ,42, enabThe strategic deployment of such stations has been explored for package delivery services ,45,46. HThe routing considering the UAVs energy constraint was approached in some works as the fuel-constrained UAV routing problem (FCURP). Sundar and Rathinam  presenteA more specific robotic mission in CPP is the complete coverage problem, which requires the decomposition of the area into cells that the robot must entirely visit. Galceran and Carreras  presenteApproximate decomposition is used by grid-based methods to represent the area with a set of uniform cells. This grid representation is only an approximation of the real area shape. In this case, the coverage is completed by visiting all cells, but the coverage completeness depends on the grid resolution. Albeit simple, the grid-based representation is not scalable due to the exponential growth in memory usage . It alsoExact cellular decomposition  splits tAvellar et al.  presenteSimilar area-coverage approaches using UAVs are reported in . HoweverLi and colleagues ,60 combiAcevedo et al. , albeit Although some studies focused on the route optimization problems for multiple UAVs in the complete area in terms of coverage operations and others locating the recharging stations to extend the vehicle range in the mission, none of the above works considered all the constraints together in a multiobjective approach in a heterogeneous fleet. To the best of our knowledge, this work, for the first time, presents a multiobjective solution for routing long-term missions for complete area coverage with multiple heterogeneous UAVs, simultaneously considering the minimization of the total time of the mission and the localization of the minimum number of recharging stations. Note that the minimization of the number of recharge stations is a relevant criterion due to the costs associated with these facilities. However, the objectives may be conflicting, as reducing the number of recharging stations may force the UAV to travel a longer path to recharge, thereby increasing the time to complete the mission.In our work, we are dealing specifically with a locating-route problem to guarantee complete area coverage by UAVs, which is not only restricted to area-monitoring tasks. The proposed methodology can be incorporated into the solution to other problems, such as using UAVs to supply data communication in wireless sensor networks (WSNs). The UAVs can also be used for data collection to support the transmission of the data gathered by sensor nodes on the ground. In this case, the vehicle capacity to transmit data is limited by the power consumption in the data transmission, flight speed, the available energy on the UAVs, the deployment location, and various other factors investigated in the literature, as presented in ,63,64,65Given an area to be monitored, it must be decomposed in several coverage rows considering sweep direction, camera parameters, and the UAV flight altitude . The disk UAVs equipped with on-board cameras pointing downward. The UAV flies at different heights to avoid collisions, which is compensated by adjusting the camera focus to keep the same footprint width for all vehicles. The UAVs have limited endurance and can reach at least one recharging station along their paths. They can recharge as many times as needed and must be able to cover at least one coverage row.The area is covered by a team of E is the set of edges, and i and j. The distances are time-invariant, symmetric ; k, starting the timing at any vertex in D, which defines the time that the UAV reaches j after passing through vertex i; and Let the decision variables be: k to recharge given the time consumed to traverse the edge. The second objective The objective in  is to mik arrives at the vertex, it must leave it. Constraint (5) guarantees that each target is visited by only one UAV. To enforce all UAVs leaving the base The degree constraint in (4) controls the flow through the vertices, ensuring whenever a UAV N vertices .The constraint (7) guarantees the open status of the recharging stations k from a vertex in Constraint (8) prevents subtours when recharge is required, keeping the solution connected. Let, for any UAV vided in .k visits a vertex j from the target i. This restriction guarantees the order of visits, preventing subtours over the targets. The constraints (10)–(13) guarantee that the tour time performed by a vehicle until reaching any vertex in D (recharging station or depot) does not exceed its flight capability, i. As the tour timing starts from a vertex in D, the flow value is defined by the time the UAV spends to traverse the edge given by The constraint (9) defines the flow variables k, the flow value j must guarantee a sufficient battery charge (flight time) to visit at least one vertex in D after visiting the target j. In this case, the constraint (11) upper bounds the j to reach its nearest vertex in D, given by D from any vertex, as the UAV can only spend its maximum flight time D. The constraint (13) lower bounds the flow value to the minimum flight time required for the vehicle to reach the vertex j, considering the minimal time required to arrive at target i. This is defined by the flight time from the nearest vertex of i in D, given by As Constraint (14) enforces the coverage row task. This is achieved by ordering target visits given its numerical identification. Lastly, the constraints (15) and (16) impose the decision variables As the computational cost to solve large instances with the exact method is restrictive for practical applications, it is acceptable to use some heuristic strategies to efficiently provide satisfactory solutions. Thus, this section presents four matheuristic approaches based on MOVNS , since aThe proposed matheuristics divide the original problem into smaller subproblems that are easier to solve, and the exact method is used, depending on the strategy, to find routes on subgraphs (clusters) or improve a solution. Here, the coverage rows, UAVs, and the recharging stations are grouped in clusters, which should be distributed appropriately, considering both objectives of this work.The MOVNS works on the clustering problem, exploring the search space by making systematic changes in the clusters. These changes are performed by the proposed neighborhood functions, generating different routes by changing the distribution of coverage rows, UAV models, and the recharging stations.The different matheuristics were designed to check the effect over the quality and time to compute the returned solutions. In the first approach, the neighborhood functions perform simple movements like changing coverage rows and UAV models among routes or shutting down some recharging stations. After that, the exact method is called for routing every changed cluster. As this strategy is characterized by the intensive use of the exact method, other approaches were designed to investigate the effect of different neighborhood functions that also use heuristics for routing to mitigate the exact method calls. Such calls were significantly decreased in the second and third approaches and reduced to only once in the last one. In the next sections, we provide the details of the proposed approaches.k UAVs, where i is composed of the subset of coverage rows A candidate solution is represented by the set of routes The vertices where the recharging stations can be installed are represented by Algorithm 1: Multiobjective variable neighborhood search (MOVNS).The MOVNS is a metaheuristic that returns the set of nondominated solutions exploring the neighborhood changes systematically. In this case, the variable neighborhood descent (VND) is performed as a local search with perturbation to escape from local minimum . The genThe MOVNS receives the maximum number of iterations , resulting in the sequence l to every l,l–E, returns the sequence A in Algorithm 2: Coverage rows assignment.Next, each cluster receives the nearest rows from the one previously assigned, while the sum of their lengths does not exceed the current r while the set A) is assigned to The remaining rows are incrementally assigned to the clusters with the highest The last procedure of the clustering phase is to assign a recharging station to every target’s location. Given the set of clusters The route is calculated by a two-stage approach aiming to speed up the optimization process with a start solution. In the first stage, each row set Given the row set of each cluster Algorithm 3: Splitting into subclusters.The number of the row subsets . In the example shown in B and E, which are the first coverage rows visited by the UAVs in their respective subroutes c (line 4). In the example, these steps assign to the current empty route The cluster subroutes computed in Stage I are linked by Algorithm 4, building an approximated route for each UAV. These routes are used as a starter solution to build the UAV optimal route in Stage II. Initially, the cluster Algorithm 4: Linking subroutes to build a start solution.Next, the other subroutes in the cluster are selected and integrated into the route being built, following its proximity (lines 5–13). The subroute In the proposed MOVNS-based matheuristic, the VND is the method that iteratively alternates the functions developed to modify the current solution. These functions are denoted as neighborhood functions, and the solution generated by a neighborhood is called a neighbor. The neighborhood function is used to map the current solution to its neighbor, which is another feasible solution. If the neighbor satisfies the update rules, then it is accepted as a new incumbent solution .In this work, we implemented four neighborhood functions, as shown in Algorithm 5: Structure of the variable neighborhood descent (VND) search.The general structure of the VND is shown in Algorithm 5. Initially, the set of routes in the input solution improved is set to true. Next, the current best solution is updated (line 7), the index i that controls the sequence of the neighborhood function is reset (line 8), and the VND iteration is broken (line 9) and continues with the next VND iteration applying the Shift function again. Otherwise, if the improved = false), the index i is incremented (line 11), and in the next VND iteration, the next neighborhood function is applied. The iteration of VND stops when all neighborhood functions obtain no improvement.At any given iteration, a neighborhood function is applied to search for a better solution. If the function The perturbation designed for this work implements two functions: one explores the coverage row changes between routes, and the other function works to open recharging stations. These functions are randomly applied, with it being possible to apply one or both functions in any order for the last case.The perturbation performed in the coverage row sets is based on the Shift neighborhood function represented in The matheuristics proposed in this paper combine the MOVNS and mathematical programming methods to find high-quality solutions. In addition to using the MILP formulation to obtain the initial solution, the same two-stage method presented in the initial routing phase interoperates with the VND in different ways in the first three strategies presented in this subsection.The designed modifications in the VND approach impact the matheuristic performance regarding the quality of the generated fronts and the computation time. Each matheuristic is classified according to the application of the exact method in the associated VND. First, the MOVNS with the exact method for routing (EMR) is presented, which uses the exact method to route all cluster changes. After, other approaches are proposed: the method that applies the exact method selectively (exact method applied selectively (EMAS)), the one in which the exact method acts as a neighborhood function (exact method as neighborhood function (EMNF)), and the method that does not use the MILP formulation in the VND (VND without exact method (WEM)). The EMR is a matheuristic characterized by solving the MILP model to route every change in a cluster performed by the neighborhood functions. Every new cluster designed by the row exchange, UAV model change, or shutting down of some recharging stations requires the exact method for routing. Next, we provide more details on the applied neighborhood functions.l from Algorithm 6: Shift neighborhood function performed on exact method for routing (EMR).The change in the coverage row sets made by the EMR Shift function is described by Algorithm 6. Initially, the longest route (highest time cost) The Shift function not only performs the inter-route changes but also returns the first improved solution obtained. Since this function tries to minimize the longest route, the comparison between As this work deals with the multiobjective problem, the neighborhood functions designed to minimize the longest route cost may also interfere with the other objective modifying the number of recharging stations. In this case, these neighborhood functions only change the incumbent solution if the route cost is improved and inserted into the structure of all the nondominated solutions. The update function on MOVNS evaluates the nondominated solutions found by the neighborhood functions on VND (see line 8 in Algorithm 1).The Swap function is structurally similar to the Shift. However, it acts to exchange the coverage rows between The UAV Swap procedure exchanges the vehicles between two routes with different models. As summarized in Algorithm 7, the routes with different UAVs Algorithm 7: Unmanned air vehicles (UAV) Swap performed on EMR.Algorithm 8: Recharging Station Shutdown performed on EMR.To shut down a recharging station on EMR, the function described in Algorithm 8 is applied. This function has as the input the current solution As EMR calls the exact method to route each change performed by its neighborhood functions on the clusters, we propose three other approaches that aim to reduce these calls. Unlike the EMR, which first builds the clusters using heuristics and then applies the exact method to the route , these new approaches are classified as improvement heuristics, as stated in . In thisThe first approach proposed is the exact method applied selectively (EMAS), which uses the exact method to improve only the solution returned by the routing heuristic. In this case, its neighborhood functions build new routes, evaluate them, check their feasibility, and then select one to be improved by the exact method.Algorithm 9: Swap performed in EMAS.The Shift and Swap functions use the routing heuristic to compute the routes and select the best route among the longest routes to be improved by the exact method. The Swap function is described in Algorithm 9. After exchanging rows between routes (lines 8–11), a heuristic is applied to recalculate such routes (lines 12–13). If the row swap produces a better solution than the incumbent one, then the exact method is applied to improve only these two routes, and the solution The ComputeRouteHeuristic (lines 12–13) is applied to compute the changed route in the EMAS neighborhood functions (Shift and Swap) and is used in the other two approaches, EMNF and WEM. This heuristic builds the new route based on the current one, rearranging each coverage row using removal and insertion procedures.The removal operation deletes the vertices and all links associated with a selected coverage row, adding a new path linking its predecessor and successor vertices. This new path must be a feasible route, ensuring that the UAV has enough charge to reach the next recharging station or depot from the successor vertex . A complete subgraph is built by connecting its initial and final vertices to all available recharging stations in both cases. A search algorithm is then applied to find a path that connects both vertices considering the UAV flight time capacity, and recharges closer to the final vertex, aiming to reach the next route segment with the maximum amount of charge. Once found, its feasibility is evaluated, checking if the UAV can reach the next recharging station or the depot after the inserted path.The first step of the insertion procedure, intended to preserve the route’s original structure by not changing its coverage row directions, can be a suitable strategy to generate routes with approximate costs of the original route, especially when exchanging rows similar in lengths and vertex alignments. However, as the routes can have quite different rows, a second step to the insertion procedure is proposed to evaluate the route cost by changing its row directions, as shown in For all routes generated by the insertion procedure, only the best one is returned. If no route is generated , the heuristic continues evaluating the next coverage row change performed by the current neighborhood function .Algorithm 10: UAV Swap performed on EMAS, EMNF, and WEM approaches.The UAV Swap function applied to EMAS is described in Algorithm 10. In this case, after the UAV model change (lines 4–5), the route edge costs (flight time) are updated considering the vehicle performance, and the route feasibility is verified by checking the capacity of the vehicle to cover its new route (lines 6–7). The improved solution, if found, is returned together with the value Algorithm 11: Recharging Station Shutdown performed on EMAS.The Recharging Station Shutdown on the EMAS neighborhood function is shown in Algorithm 11. As explained for EMR, each iteration must check the feasibility of removing a station from all routes containing them. However, in EMAS, instead of applying the exact method to reroute, a procedure checks whether it is possible to deviate the recharging to the nearest station in relation to the one under consideration for shutdown (line 8). The station is shut down if all vehicles directed to it can perform this deviation (lines 10–16). In this case, the MILP model is applied to improve the feasible solution (line 12).All these modifications designed to the EMAS neighborhood functions are also used on EMNF and WEM. The difference is that the EMAS uses the exact method on most neighborhood functions to improve the selected routes. In the EMNF, the exact method is removed from the neighborhood functions and added to the VND as a neighborhood function, acting as an intraroute operator performed on all incumbent solution routes. Conversely, the WEM does not apply the exact method in any part of its neighborhood structure. It is a matheuristic classified as a one-shot approach that applies the exact method only in the initialization .This section presents the experimental results from the evaluation of the matheuristics proposed in this paper. The algorithms and MILP formulations were implemented using C++ and compiled with GCC 4.8.5. The MILP was solved with Gurobi version 8.1.1 , using ti to vertex j, considering the distance between these vertices and the flight speed of the UAV ) calculates the number of B solutions that are weakly dominated by another A front given B’s cardinality. If C is equal to 1, all B solutions are dominated by A front solutions. If the value returned is equal to 0, it indicates the opposite situation in which A dominates none of B’s solutions . A generThe relative performance of these approaches was assessed using the following metrics:Both the hypervolume and coverage metrics return values representing percentages, whereas the cardinality metric provides absolute values.We employed statistical tests designed to detect significant differences among the proposed approaches and to estimate their magnitude considering each quality metric. The data used for this comparison included the values for the given metrics calculated for the final Pareto fronts obtained on 15 independent trial runs of each algorithm on each problem.For each metric, the experiment was designed as a randomized complete block design (RCBD) with the algorithms as the levels of the experimental factor and the problems as blocking factors . By treaTo avoid assumptions of the F-test, the Friedman test was used ,79. In tAfter the Friedman test indicates significance, the Nemenyi’s post-hoc test was applied to identify differences between the algorithms . Then, fThe results obtained for the experimental comparison are summarized in As observed in The best results for the coverage metric observed in The performances of EMR, EMAS, and EMNF were quite similar in terms of the cardinality metric. For the hypervolume, all algorithms presented consistent performance in most problems, as shown in As the cost of generating the exact Pareto-optimal fronts to all instances used in this work was prohibitive, we chose one of the smallest instances, type D with 20 targets and three UAVs, to illustrate the performance of the exact approach and the proposed methods . The froA common method used to generate the Pareto-optimal front is transforming the multiobjective problem into several mono-objective problems, where the optimal solutions of these problems represent the Pareto front solutions. In this work, we used two strategies to generate the optimal points belonging to the set of efficient solutions: the weighted problem trict Pϵ . The Pλ First, the The other points in the Pareto front were calculated by the Given the high computational costs of the exact method, we implemented the EMR (cluster first-route second) matheuristic that combines the MOVNS with an MILP formulation to generate the approximated front. This method splits the original problem into small problems, one for each UAV, and applies the exact method for routing into the neighborhood functions. The results shown in The decrease in the number of calls of the exact method in EMAS, EMNF, and WEM was achieved by designing a heuristic to route and evaluating each change performed by their neighborhood functions. The strategy adopted in EMNF and EMAS to mitigate the trade-off between execution time and the quality of the computed fronts was a wise application of the exact method to improve some solutions. The EMAS calls the exact method into the neighborhood functions only to the best solution returned by the routing heuristic. Conversely, EMNF adds an intraroute operator on its VND that uses the exact method to improve each route of the current solution separately.The statistical tests showed that EMNF was faster than EMR and superior to WEM considering cardinality, coverage, and hypervolume metrics. The WEM approach was the fastest as the exact method is not used on its VND. The statistical analysis indicated a significant degradation in all metrics used to evaluate the returned fronts compared to EMR and EMNF. However, between WEM and EMAS, no significant differences were detected in coverage and cardinality.Although the results in This paper proposed strategies for the multiobjective location-routing problem in complete area coverage missions using heterogeneous UAV fleets. Here, the decision on the recharging station locations and the number of stations to be opened are jointly considered with UAVs routing criteria. As the cost to compute the Pareto-optimal front to this problem is usually prohibitive, four matheuristics based on MOVNS were presented, evaluated, and compared using four performance metrics .The proposed EMR, a cluster first-route second matheuristic, returned most of the best results considering the fronts quality metrics. However, it performed the worst in execution time. The other approaches, EMAS, EMNF, and WEM, significantly reduced the intensity of calls of the exact method using heuristics in the routing of clusters. Our experimental comparison showed that the EMNF presented relatively high-quality performance regarding the front’s quality metrics compared to EMAS and WEM. The statistical analysis results indicated no significant degradation compared to EMR, standing out for applying the exact method only as a neighborhood function in the VND, where it acts like an intraroute operator.Conversely, as expected, WEM, a one-shot approach where the MILP is only solved in the beginning, produced superior performance in terms of the execution time. Despite the results showing a significant degradation in the quality of its fronts compared to EMR and EMNF, no evidence of significant difference from EMAS in coverage or cardinality was observed. A modest loss in hypervolume was verified. Thus, WEM can be indicated for applications that tolerate the observed degradation in the quality of its solution in order to speed up computation.In the future, we intend to consider the recharging station’s capability to attend a limited number of UAVs. In this case, the recharging stations could be modeled as a queuing system, as proposed in . We also


Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.  With the rapid progress of Internet of Things (IoT) techniques, location is critical information for many fields and location-based services (LBSs) are widespread and prevalent in people’s daily lives ,2,3. GloAmong the indoor localization techniques, the use of WiFi signal strength for fingerprinting-based methods has attracted much attention since WiFi is widely deployed as a wireless communication infrastructure. WiFi technology can achieve more complex large-scale positioning, but at the same time, has to deal with many other interferences . The RFIThe inertial measurement for indoor localization suffers from cumulative errors in the integral operations, which cannot satisfy long-time localization applications. Most current investigations focus on the algorithms of noise cancellation and displacement calculations in advancing the accuracy of localization, which is limited by the performance of hardware . TherefoThis investigation aimed to provide a cost-effective hardware solution with data fusion algorithms for wearable indoor localization and motion analysis. By comparing the alternative techniques, the UWB-IMU integrated solution was selected as the solution to take advantage of both UWB localization and IMU inertial measurement localization. Further investigations on identifying the underlying problems of the selected techniques and the integration of the two techniques were conducted with experiments and quantitative evaluations. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight sensing device integrating a low-cost UWB module-DWM1000 and a micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and to calculate the attitude of moving objects, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) experimental studies were carried out and quantitative evaluations were conducted to comprehensively evaluate the proposed solutions, and the results demonstrated the feasibility and the performance.The structure of this paper is organized as follows: The huge demands of LBS devices in the future wearable IoT (WIoT) market has promoted investigations in the related areas, and a large number of investigations on indoor localization techniques, methods, and systems are reported in the literature ,16. New Many technical solutions are found to be used for indoor localization, which can be classified into five categories: (1) wireless communication, (2) optical/visual, (3) acoustic, (4) electromagnetic, and (5) inertial measurement ,17. Desp(1) WiFi Fingerprinting Indoor LocalizationWiFi has been a widely used approach for indoor localization due to its advantages in NLoS, low cost, wide possession of WiFi enabled electronics among people, and convenient deployment by using existing WiFi infrastructure. WiFi fingerprinting, without knowing the exact access points (APs) locations, leads to a high feasibility in indoor deployment . As showWe have witnessed significant technical progress and plenty of applications in recent years. Zegeye et al.  implemen(2) RFID-Based Indoor LocalizationAs an object identification technique, RFID can be used for indoor localization due to the RSSI and phase of the radio signal, which can be collected by the RFID readers. Plenty of pioneer studies have been carried out and rapid technical progress has been made in recent years. One simple solution is to paste the RFID tags in fixed locations and let the moving object carry an RFID reader for localization. When the object moves, it receives the RFID tags, which can be used to lookup a pre-established tag-path position database and to calculate the corresponding location ,30. Anot(3) UWB-Based Indoor LocalizationUWB can be considered one of the most promising wireless technologies for localizing people or objects, with a high accuracy of about 10 cm. The methods used for UWB positioning are time of arrival (ToA), time difference of arrival (TDoA), and angle of arrival (AoA) ,37, whic(4) Inertial Measurement-Based Indoor LocalizationThe MEMS IMU, due to its compact size and low power consumption, is attracting increasing attention for localization and motion analysis purposes. It estimates the location and tracks the motions through the in-built three-axis acceleration, three-axis gyroscope, and three-axis magnetometer. Plenty of related investigations have been reported in the literature. Yuan and Chen  presente(5) Combined Indoor Localization SolutionsIn addition to the above approaches, some investigations integrate two or more localization solutions to take advantage of the different techniques and optimize the performance. Inertial measurement is the most popular choice that is integrated with other solutions. Corrales et al.  used a KAccording to the commonly used indoor localization techniques discussed above, each technique may have its advantages and weaknesses for different indoor localization purposes. The WiFi fingerprinting is an economical and easily deployable choice that suffers from low accuracy due to time-varying RSSI. RFID is suitable for NLoS, while normally showing low accuracy and requiring a complex hardware system and inconvenient maintenance. Although UWB is competitive in accuracy, it suffers from NLoS interference and requiring multiple anchor deployment. Inertial measurement is usually lightweight and wearable, while the error accumulates with time, which may result in a very large error after long operation time. Some tentative investigations combining two or more techniques have been found, but most solutions suffer from bulky or expensive devices and low accuracy. Cost-effective and light-weight devices for unobtrusive wearable sensing is necessary for indoor localization. Due to the compact size and versatile functionality, IMU has become a popular choice for integration with other technical solutions for indoor localization. Although there have already been pioneer investigations reported in the literature, it still lacks a solution for indoor localization and motion tracking using the commonly used cost-effective electronics with detailed error cancellation and data fusion algorithms. This investigation aimed to provide a solution for indoor localization and human motion tracking by integrating UWB and IMU, which takes advantage UWB’s high localization accuracy and IMU’s NLoS localization and motion sensing. To validate the proposed solution, a UWB-IMU integrated compact and energy-efficient module was developed and the corresponding data fusion algorithms were designed and implemented. Finally, the whole system was verified with experimental studies. This section describes the principle and method of the positioning algorithms for both UWB and IMU solutions and the UWB-IMU integrated system regarding the hardware design and software algorithm. Then, the synchronized signal processing of the two kinds of sensor modules and the overall workflow of the system are presented in detail. (1) Fundamentals of UWB PositioningThe UWB localization system realizes the positioning by measuring the distance between the tag and multiple anchors. The distance between the tag and anchor can be calculated by multiplying a signal’s time of flight (ToF) and the speed of light . TherefoAs shown in In Equation (1), the  anchors . This UWB localization system contains at least three UWB anchors used for transmitting the ranging message from the tag, which are as shown in (2) Fundamentals of IMU localizationFor IMU localization, we define two coordinate systems: the navigation coordinate N and the body coordinate B. The navigation coordinate N refers to the coordinate system referenced by the earth, and the body coordinate B is the coordinate system referenced by the IMU itself. Velocity and displacement are calculated by integrating acceleration in the navigation coordinate system. The acceleration in the navigation coordinate system is obtained by converting the quaternion with direction information obtained by the complementary filtering algorithm. The complementary filtering algorithm combines multiple sets of data and performs filter processing, and then outputs the final quaternion. The acceleration data has good static stability for attitude estimation, while it is relatively unreliable during dynamic motions. The gyroscope has better dynamic stability, but there might be a bias error, which results in a drift for long stationary times. Therefore, good dynamic performance with a small drift could be obtained by integrating the gyroscope and accelerometer outputs, which is shown in In The prediction ordinate :(4)e = ae using their cross product. In the above equations, the normalized acceleration vector In addition, calculating the displacement using the IMU requires a double-integration of the acceleration in the navigational coordinate system. The quaternion obtained in the previous step can help to convert the acceleration in the body system into the acceleration in the navigation coordinate system. In Equation (7), −2 in this investigation). For acceleration values greater than the threshold range, the double-integration is used to calculate the position variation. The integration operations of acceleration for the position estimation is given in Equations (8) and (9), where v for the velocity, p for the displacement, and In this system, the IMU is mounted on the pedestrian’s foot, and the acceleration and angular velocity of the foot are measured in real time. One gait step is divided into foot touching and swing. When the foot touches the ground, the acceleration and velocity of the foot are considered to be zero. However, due to the noise of the IMU, the actual acceleration is not zero, which may introduce an error. We use the ZUPT algorithm  to separIn this section, the change of position is obtained by accumulating the displacement in the X- and Y-axes. The following subsection presents the method for calculating the position with IMU data, and the data fusion model to integrate IMU and UWB data with a Kalman filter.Suppose the sampling interval is At the time  and (17):In this section, we integrate the position data obtained by the UWB and IMU. The position data of the IMU is obtained using integral calculation. The IMU calculation is relatively reliable for a short period, but its error accumulates over time. The UWB positioning may cause deviations due to problems, such as clock skew between the anchor and the tag. However, the measurement results do not drift over time. Therefore, we use the Kalman filter algorithm to integrate the two methods by using an iterative compensation of the two location results, and thereby achieve higher precision indoor positioning. The data fusion model is shown in Based on the IMU observation data ((1) The current state is estimated based on the previous state using:Q matrix is the covariance of the motion model and it represents the error between the prediction model and the actual motion. The prediction model of this system is a non-uniformly accelerated motion. In a short interval, the interference caused by other influencing factors, including friction resistance and air resistance, is relatively small. The values of matrix Q are (2) Calculation of the system covariance is done using:(3) The Kalman gain transfer coefficient is updated using:(4) The transfer status is updated using:(5) The transfer state covariance matrix is updated using:k are obtained. The state estimation based on the IMU observation data is used as the prediction quantity in the UWB system, combined with the observations in the UWB system, to get the best position estimate. The state quantity After updating the IMU observation equation, the state quantity (6) The Kalman gain is updated using:k − 1.(7) The status is updated using:(8) The state covariance matrix is updated using:The state quantity The integrated UWB and IMU positioning sensor module was designed with a STM32  micro control unit MCU as the central controller. The hardware circuit of the system included a STM32 MCU, a DWM1000 UWB module , a MPU9250 IMU , a JDY-32 Bluetooth module , and DC–DC module for the power supply. The designed sensor device and its functional diagrams are as shown in The DWM1000 is an ultra-wide band wireless module from DecaWave that complies with the IEEE802.15.4-2011 standard. In this system, the DWM1000 is responsible for marking the information transmission and reception time stamps and the communication between the base station tags and the sensor tag. The tag uses a polling method to communicate with the base station to complete the ranging function. The MPU9250 is a MEMS IMU module with a gyroscope, an accelerometer, and a magnetometer inside, which can measure the three-axis angular velocity, three-axis acceleration, and three-axis magnetic field. In this system, the acceleration and angular velocity of the sensor device are obtained using the MPU9250, and the corresponding displacement is used to complete the localization functions. Each time the DWM1000 and MPU9250 complete a set of data acquisition and calculations, the sensor device completes the calculation of the IMU position and the data fusion of the two sensing modules, and then sends the results to the host computer through Bluetooth. The host computer records the uploaded data and displays the graphics accordingly.The synchronized data collection and processing of the two sensing modules is critical to the designed sensor device. When a motion is detected by the sensor device, the UWB obtains the displacement data via the DS-TWR method, and the angular velocity and acceleration from the IMU can be calculated to obtain the displacement and attitude. However, the displacement value calculated using the two sensors have certain deviations from the true value, so we compensate the two sets of data through a Kalman filter to obtain an optimal estimation of the displacement, which were presented in In order to verify the feasibility and effectiveness of the proposed solutions in terms of both hardware and algorithm, we implemented the data processing algorithm with the designed sensor device. The data was collected through Bluetooth with a host PC recording and displaying the results using a MATLAB  graphical interface. Practical experiments of one-step walking, arbitrary path continuous walking, and abnormal gait detection were conducted for evaluating the proposed solution.In this section, the positioning accuracy of the IMU module and the UWB module are evaluated. The sensor device was fixed on the surface of the foot and a one-step walking was performed 20 times to measure the step size. The experimental setup is shown in A meeting room sized 7.0 × 5.5 m including a few pieces of furniture was chosen as the indoor experimental scene for the further experimental studies, and the diagram of the room is shown in (1)For the IMU measurement, there was an evident systemic error, as seen by the deviation between the black and blue lines in (2)For the UWB system, it was susceptible to NLoS occlusion of experimenter’s ankles, resulting in a bias error. As shown in (3)For the UWB-IMU integrated system, the advantages of the above two systems were combined. The UWB data was used to compensate for the inertial localization, and the inertial data was used to correct the UWB positioning data to make it more stable. The two sets of displacement data were iteratively compensated by the fusion algorithm, and as shown in By walking around the rectangular path once, the IMU-measured location, UWB-measured location, and fusion results are given in For the arbitrary walking path, the location was obtained by marking the position of the foot, which is shown in θ in In addition to the indoor localization, the sensor device can also measure the attitude variation of the pedestrian’s foot during the walk. Since walking is a very common form of activity in human daily activities, it is a very important indicator for clinical rehabilitation. The gait shows how people walk and exhibit gait patterns that change periodically. A standard gait cycle can be divided into four phases, namely heel strikes on the ground, touching the ground, the toes touch the ground, and swinging, which are shown in In order to prove the usability of measuring the angles for gait analysis, a test of simulated foot twisting during walking was performed, which is shown in The results shown in (1)For the IMU measurement, it was evident that the inertial measurement suffered from error accumulation although a ZUPT algorithm was implemented. The accumulated error finally turned to a system error in the inflection points, which decreased the accuracy of the final results. The possible solutions for correcting this system error might be to identify the inflection point and correct the inertial measurement result with a UWB measurement. This method may be able to effectively reduce the IMU accumulation error in the inflection point and promote the accuracy of localization results.(2)The UWB measurement showed evident random errors, and there was also evident greater measurement errors when there was a NLoS occlusion between the sensor node and the UWB anchors. Being incapable of dealing with this error resulted in a lower localization accuracy. The promising solution to handle this error is to observe the gradient of the UWB location and IMU location and determine the emergence of barriers and correct the error by adjusting the trust weight of the UWB and IMU results.By integrating the UWB and IMU with the proposed hardware and data fusion solutions, the UWB-IMU sensing module takes the advantages of the high-accuracy UWB localization and IMU localization signal stability, robust NLoS localization, and capability for motion tracking. Through the design and implementation in this investigation, the lessons learned are summarized as follows:Since indoor localization has attracted widespread interest in recent years, more accurate wearable modules and the corresponding algorithms are focal research topics. The solutions for the above technical issues may finally result in higher accuracy.This investigation proposed cost-effective hardware and data fusion solutions for the integration of UWB and IMU in a wearable module for human indoor localization and motion tracking. The Mahony filter and quaternions were employed for attitude estimation using the accelerometer and gyroscope, and the accelerations in the navigation coordinates were obtained to calculate the variations of location. UWB and IMU locations were integrated with a data fusion model based on a Kalman filter to obtain the final results. With these techniques, the designed module achieved the indoor localization and motion tracking goals. The UWB-IMU integrated solution takes the advantage of both the high-accuracy UWB localization and the IMU localization signal stability, robust NLoS localization, and motion tracking capability. Since the module is compact in size and is battery powered, it is a competitive alternative for many wearable indoor localization applications, such as elderly care, sport motion analysis, rehabilitation medicine, and robot navigation. (1)A smart algorithm to automatically identify the NLoS blocking errors and adjust the weight of trust between the UWB results and IMU results to improve the localization accuracy. (2)The determination of the key turning corners in the motion where the IMU results may introduce errors and use the UWB location to correct the accumulated error of IMU localization results.(3)Comprehensive evaluation of the designed IMU-UWB system by comparing the estimation with more reliable references using methods such as the Cramer–Rao low bound (CRLB). (4)Evaluation of the influence caused by indoor environment including human body and furniture, such as the error caused by the random walking pedestrians and their walking speed.It was found that the UWB error caused by NLoS blocking by human body parts between the UWB anchor and sensor node had a critical influence on the measurement, and the accumulated error of the IMU measurement was another critical error. The future work will focus on seeking solutions for minimizing the errors, including: In addition, to extend the UWB-IMU measurement from indoor localization to building-scale localization and navigation for product and human tracking is a promising field. Undoubtedly, there has been a great need for indoor localization for human tracking and motion analysis. A smart wearable sensor device that is more accurate, reliable, and easy-to-use for indoor localization is of interest to many novel IoT applications in the future rich-sensing smart world.


Noise is an environmental stressor and can cause or exacerbate mental disorders, and affect the individual performance in certain conditions. This study aimed to evaluate the combined effects of noise and smoking on the cognitive performance of the workers in the automotive industry.This research is a descriptive-analytical study with a cross-sectional design conducted on 300 workers randomly assigned into two groups of noise-exposed and nonexposed. They were examined using computerized tests, including the Tower of London test (TOL), Continuous Performance test (CPT), and Stroop test. The sound pressure levels were measured based on an 8-hour equal-loudness contour in each group according to ISO 9612 standard, using the Testo CEL-815 sound level meter.The study of combined effects of noise and smoking on 12 CPT indicators using the 2-way Analysis of Variance (ANOVA) indicate that noise and smoking factors had a significant impact on the mean number of errors and correct responses in the third 50-stimuli stage, the mean number of errors and correct responses in the second 50-stimuli stage with P<0.001, P<0.001, P=0.012 and P<0.001 for smoking respectively, but only noise affected the other 7 indicators (P<0.001).Smoking and noise have negative impacts on concentration, attention, and cognitive processing speed, which can lead to an individual’s mistakes and delayed decision making at the workplace. Brain function, cognitive processing speed, and individual performance are significantly lower in smokers compared to non-smokers.Noise and smoking have significant effects on cognitive performance indicators.High noise exposure is associated with the risk of an increased number of errors in responding to the test stimuli.Addiction to cigarettes and other nicotine-containing products harms cognitive performance, including executive functions.Excessive noise in industrial environments can cause hearing impairment, speech problems, sleep disorders, noise annoyance, and decreased efficiency of the workers. This study aimed to evaluate the combined effects of noise and smoking on the cognitive performance and psychological flexibility of workers in the automotive industry in Iran. The workers were assessed by proper tools to examine their mental performance and responses to low- or high-frequency noises. We found that long-time exposure to noise significantly affects the individual’s performance and psychomotor speed, which resulted in impaired concentration, poor working performance, and increased mistakes at work. This study revealed the relationship between smoking and brain functions in terms of response type and decision-making.1.Noise is one of the most important sources of stress in life. Loud noise may not only cause physical problems such as hearing loss and increased vulnerability, but also lowers cognitive performance . Noise iBecause noise is an environmental stressor and combined with other stressors can cause or exacerbate mental disorders and consequently affects individual’s performance in certain circumstances , the WorSound has different effects on cognitive performances. Research evidence suggests that long-term exposure to noise can affect cognitive performance in central processing and language comprehension . BesidesAvailable studies on noise effects suggest that noise can cause occupational problems and increase the number of mistakes during working, depending on the type of sound and job. Also, meaningful and relevant information that attracts one’s attention is more likely to decrease efficiency . CognitiSome studies on the effects of noise with different bandwidths on cognitive performance have shown that cognitive impairment is more common in people when they are exposed to the high-intensity than to the moderate-intensity sounds . Consequ2.This research is a descriptive-analytical study with a cross-sectional design. 300 workers of the automotive industry in Tehran, Iran were randomly selected and assigned into two groups of noise-exposed  and nonexposed . The demographic information of each group was recorded and checked if they were cigarette smokers. They were considered smokers if they had smoked 5 or more cigarettes a day for 14 years . The souWe used the Testo CEL 815 sound level meter with a precision of 0.5 dB and ability to measure the sound intensity in the A network calibrated by Testo IEC 942/90 Class2 calibrator with an intensity of 114 dB at the frequency of 1 kHz. The subjects who had willingness to continue participation in the study were subjected to psychological and intellectual tests. Their sustained attention was measured using CPT, while for selective attention and cognitive flexibility assessment, the Stroop test was used. Also, their problem-solving ability was assessed using the TOL test. One of the inclusion criteria for entering this study was lacking underlying diseases  or metabolic and psychiatric disorders. The subjects were allowed to enter the study based on their medical records and demographic information.2.1.CPT is one of the simplest psychological tests . In all 2.2.The Stroop test evaluates the selective attention and cognitive flexibility in various clinical groups . The tes2.3.The TOL test evaluates at least two executive functionings of planning and problem-solving. It is used to assess executive functions and cognitive performance of various people, including the patients with glioma, schizophrenia, and so on . During 2.4.For analyzing the collected data, descriptive statistics  were used as well as the Kolmogorov-Smirnov test whose results showed the normality of the quantitative data distribution (P>0.05). tow-way Analysis of Variance (ANOVA) examined the combined effects of noise and smoking. The data were analyzed in SPSS V. 22 by considering the significance level of P<0.05.3.The tow-way ANOVA was used to determine the effect of noise and smoking on the cognitive performance variables. The results showed that the combined effect of noise and smoking on the cognitive performance variables was not significant (P>0.345) . RegardiThe study of the combined effects of noise and smoking on 12 CPT variables using the 2-way, ANOVA indicated that both sound and smoking factors had significant effects on the number of errors and correct responses (P<0.001).4.The results of this study showed the combined effects of noise and smoking on the cognitive performance of workers in the automotive industry. The results of this study not only confirmed the impact of noise on cognitive performance but also emphasized the effect of noise on responsiveness and individual performance speed, which was considerably reduced in exposure to high levels of noise. This finding is in line with the results of the previous studies . The envRegarding the effects of smoking on the cognitive performance of the workers in the automotive industry, the results of our study demonstrated the relationship between smoking and cognitive performance. Although brain function and mental performance in the work environment are affected by environmental factors such as light, sound, and temperature, some studies also investigated the effect of using nicotine-containing products such as cigarettes on the mental and brain functions in different age groups and genders and reported various results . SmokingThe concentration which can affect an individual’s reaction time was one of the factors examined in this study in smokers and non-smokers. Smoking interfered with concentration through physiological responses to the nicotine substance in the body and significantly increased the reaction time and the correct response time in Stroop and CPT-tests. In responding to the congruent stimuli in the Stroop test, smokers showed weaker concentration compared with their responses to the incongruent stimuli, implying that concentration affects the time of response to the two different stimuli patterns.Brain function and mental performance are affected by psychomotor speed and mental flexibility following damage or injury in the cerebral cortex capillaries; therefore, cigarette smoking can cause some changes in the vascular mechanism for blood flow and some physiological changes in the organs like brain . Thus, tAccording to the study of  on the eAltogether, cigarette smoking has a direct impact on workers’ cognitive functioning and reduce their mental and cognitive performance. Smokers also suffered more from concentration deficits and poor working performance compared with non-smokers. According to the results of the Stroop test and CPT-test, smokers had low-speed cognitive processing in decision making and weakness in making timely decisions.The diagnostic skill model is one of the main reasons why concentration is more affected than attention . AccordiThe rate of human errors in high-risk and managerial jobs, where there is a need for proper concentration, has a relationship with exposure to noise such that with higher noise frequency, the human error rate increases . This fiBased on the transactional model of stress, mental assessment of threatening events, or the ability to cope with stressors are related to the mental stress experienced by individuals . AlthougExperiencing stressful events that do not threaten their lives can also bring up positive emotions on them  and increase the workers’ self-confidence at the workplace. In addition, studies have emphasized that the Stroop test and other cognitive/psychological tests are beneficial for the analysis of mental performance and responses to intervening stimuli that require attention (e.g. low- or high-frequency noises), and making it possible to examine the impacts on cognitive performance. The results of this study were partly consistent with the central nervous system arousal theory which is related to the stimulation of the central nervous system and the adaptation of human responses to stimuli. In our study, the samples’ physiological system could adapt to the noise in the long term and could even work better with the noise, but its consequences would reduce their accuracy and concentration in performing the assigned tasks .In general, the reaction time in mental activities is influenced by noise. Various studies reported the effect of exposure to high- and low-frequency noises on cognitive performance. These reports indicate the influence of exposure to chronic noise on the mental and psychological conditions, including performance and processing speed. Several studies that have examined the relationship of human cognitive performance with other physical factors have also reported similar results . Sound pIn this study, the TOL test indicated a significant relationship between individual performance and strategic approaches to problem-solving and planning and noise exposure. Variables such as time delay and the number of errors showed a significant and robust relationship with noise exposure such that those exposed to chronic noise had more delays and errors in responding to this test. Mental and cognitive performances of the workers employed in industries are directly and significantly related to chronic exposure to noise. Controlling noise in industries and using personal protective equipment can remarkably and significantly reduce human errors and, on the other hand, increase efficiency and effectiveness to achieve organizational goals .One of the limitations of this study was the lack of individual matching, which can allow a more accurate evaluation of the study variables and eliminate the effect of confounders. Another limitation was the theoretical aspect of the issue. In this regard, the test stimuli were presented to the subjects on a computer monitor, which may not adequately examine the cognitive factors.Noise exposure and smoking could affect the workers’ cognitive performance, thereby reducing their attention and concentration and increasing the risk of errors at work. Hence, taking into account the effect of noise and smoking on workers’ health, developing strategies for controlling smoking, and reducing noise pollution should be considered among the essential preventive measures.All of the experiments were conducted according to the guidelines of the Ethics Committee of Iran University of Medical Sciences .


Deficits in stress-response systems are a characteristic of schizophrenia and psychosis spectrum illnesses, and recent evidence suggests that this impairment may be evident in those at clinical high-risk (CHR) for the development of a psychotic disorder. However, there is limited research specifically investigating biological and subjective stress reactivity in CHR individuals. In the present study, 38 CHR individuals and group of 38 control individuals participated in the Trier Social Stress Test (TSST), an experimentally induced psychosocial stressor. Changes in salivary cortisol and alpha amylase, as well as self-reported units of distress (SUDS), were evaluated. Interestingly, the TSST did not induce a change in cortisol levels in either group, though the CHR group did show higher overall cortisol levels throughout the TSST (pre-anticipation period through recovery period). However, indicative of an effective task manipulation, the TSST did illicit an increase in alpha amylase in both groups. CHR participants exhibited higher levels of subjective stress prior to the stressor compared to the control group and CHR SUDs did not significantly increase in response to the stressor. In contrast, the control group showed an increase in SUDS in response to the stressor. Notably, SUDS for the control group post task mirrored the levels CHR youth endorsed prior to the stressor. Taken together, these findings suggest that there may be a functional relationship between persistently elevated cortisol and chronic high levels of subjective distress in CHR individuals. Psychotic disorders are extremely debilitating, chronic, and costly illnesses , 2; therThe HPA axis is a dynamic pluripotent stress response system which contributes to the important circadian regulation of the body through a prominent daily rhythm of basal cortisol secretion . Thus, tFew studies have examined both hormonal and psychological responses to psychosocial stress in CHR individuals and results have been inconsistent. In a study examining psychological stress induced by the Montreal Imaging Stress Test (MIST), significantly greater salivary cortisol response to the stressor was found in the CHR group compared to the healthy controls . AlternaGiven that stress reactivity depends on the interplay of multiple neurophysiological systems , 22, theThe present study is the first to examine group differences in salivary cortisol (HPA-axis), salivary alpha amylase (SNS), and subjective response to an acute lab stressor experimental paradigm in CHR adolescents and a control group of adolescents. Based on evidence indicating a blunted hormonal stress response in patients with schizophrenia , it was Participants were recruited at the Adolescent Development and Preventative Treatment (ADAPT) research program and a total of 76 (38 CHR and 38 control) adolescents participated in the study. Participants were recruited by Craigslist, e-mail postings, newspaper ads, bus ads, and community professional referrals. Exclusion criteria included age <12 or >21 years of age, history of head injury, the presence of a neurological disorder, and lifetime substance dependence. The presence of an Axis I psychotic disorder was an exclusion criterion for CHR participants; however, other disorders were not exclusion criteria for CHR participants as comorbid Axis I disorders are typical of CHR individuals . Rates oThe Structured Interview for Psychosis-Risk Syndromes [SIPS ] was admA modified version of the Trier Social Stress Test [TSST ] was useThe TSST is a robust procedure that induces a moderate psychosocial stressor and evaluates its effects on physiological and psychological response . ModificHPA-axis reactivity was assessed by examining salivary cortisol levels at 6 points during the TSST. Timepoints allowed for the baseline period, acute reaction to the stressor, and recovery period of the HPA-axis to be captured. SNS reactivity was assessed by examining alpha amylase at three timepoints: the start of the anticipation period, at the end of the of the stressor, and 10 min into the recovery period; see not stressed at all, 100 = extremely stressed). Participants were asked to orally report their levels of stress at three time points: the start of the anticipation period, right before the start of the TSST, and at the end of the TSST; see The psychosocial stress level was measured by the Subjective Units of Distress Scale (SUDS) ; a self-Based on prior studies, saliva collection utilized a passive-drool method , 40. Part-tests and chi-square tests were employed to examine differences between groups in respective continuous and categorical demographic variables such as age, sex, and parental education, along with total positive symptoms. To test for changes in cortisol levels between groups over time, a 2-factor mixed design ANCOVA (time × group) controlling for age was conducted and 2-factor mixed design ANOVAs (time × group) were conducted to examine changes in alpha amylase and SUDS by group over time. Consistent with previous work  was dropped from the analysis to exclude potential confounds , which is consistent with previous studies  = 1.14, p = 0.260], in years of education , or sex  between the CHR and control groups. As expected, the CHR group showed significantly more positive SIPS symptoms  when compared with healthy controls. See As noted, a total of 76 (38 CHR and 38 control) adolescents participated in the study. There were no significant differences in age . These results suggest that the TSST stressor did not induce a significant change in cortisol in either group. However, the analysis revealed an overall main effect of group where cortisol levels during the TSST were significantly higher in the CHR participants compared to the control group , see In the examination of cortisol levels across the TSST, the group by time interaction and main effect of time were not significant , levels in both groups increased in response to the TSST as indicated in the main effect of time , see F = 3.06; p = 0.086]. Exploratory post-hoc tests indicate that lower CHR's alpha amylase levels after the stressor (p = 0.052) appear to be driving the trend result. This pattern is supported by follow-up AUCi analysis provided in the Interestingly, while there was not a significant group by time interaction for alpha amylase , see post-hoc analyses revealed an interesting pattern where CHR individuals started and remained at an elevated level compared to controls before and after the anticipation period prior to the stressor . However, by the time the stressor was over, the SUDS level of control group increased to a level that was comparable to the CHR level (p = 0.174). Results suggest that while the CHR group started the task with higher SUDS, the control participants experienced a larger increase in SUDS in response to the stressor compared to the CHR group.In the examination of group difference across the TSST in subjective stress experience, results revealed a significant group by time interaction [The current study expands the small literature base examining biological and subjective stress reactivity to an acute laboratory stressor in CHR and control youth, and is the first study to include alpha amylase as a measure of SNS in this population. It was predicted that the CHR group would exhibit blunted cortisol levels in response to the stressor relative to the control group; however, group differences in cortisol reactivity to the TSST could not be evaluated as a significant change in cortisol after the TSST was not observed in either group. The results suggest that while the adapted version of the TSST used did induce a stress response as evident by the increase in alpha amylase levels (SNS response) and the SUDS, an HPA-axis response was not activated. Lack of HPA-axis activation has been observed in a previous psychosis risk study that used a different, but similar social stressor , as wellSalivary alpha amylase was included in the protocol as a second biological marker of stress reactivity (SNS). The SNS speed of onset is more rapid than the HPA response (presently measured by cortisol) and it was predicted that the CHR group will have higher alpha amylase response compared to the control group. Interestingly, an increase in alpha amylase levels was observed in both groups in response to the TSST , which indicates that a physiological stress reaction was induced by the stressor. Given the lack of HPA-axis reactivity observed, the inclusion of salivary alpha amylase provides an important window into biological reactivity that would have been missed had it not been examined. The results suggest that the fast acting SNS response is a useful measure within this population. This is promising as studies examining other markers of the autonomic nervous system and SNS reactivity have reported mixed results among patients with schizophrenia and CHR individuals , 47. IntContrary to the hypothesis that the CHR group would report elevated SUDS in response to the stressor compared to the control group, a pattern emerged showing that the CHR group experienced higher subjective stress at the first two timepoints prior to the stressor without a notable increase in response to the TSST. The control group did show the expected pattern of increase in response to the stressor. Notably, the CHR group's SUDS at baseline appeared similar to the control group's levels post stressor. This pattern provides a unique perspective of subjective stress experience over time and suggest that timing is important. One interpretation of these results is that the CHR in this sample came into the study feeling more stressed than the control group and the CHR subjective stress levels did not have the capacity to increase at the rate of the control group. Higher SUDS prior to the stressor in the current study is consistent with previous findings indicating that CHR individuals report elevated levels of perceived chronic stress , 29, 43.Given the combination of the lack of SUDS reactivity and the trend result suggesting a weaker alpha amylase response in the CHR group, another interpretation to consider is that the CHR group may not have experienced the experimental stressor to be as stressful as the control group. Deficits in Theory of Mind (ToM), the ability to infer and understand others' mental states, have been observed in CHR groups . Thus, iTaken together, the results of the study replicate previous observations of higher overall cortisol levels in CHR individuals and introduces the consideration of the impact of persistently high subjective stress in the dysregulation of acute stress reactivity in psychosis risk. It is possible that chronically high levels of subjective stress could lead to a decreased sensitivity and subjective reactivity to stressors and cause dysregulation to biological stress systems, such as the alteration of the negative feedback of the HPA-axis believed to occur in patients with schizophrenia . InteresThe findings from the present study should be interpreted in the context of its limitations. First, while the overall sample size was larger than previous studies that examine stress reactivity among CHR individuals , 20, 43,The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by University of Colorado Institutional Review Board. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.Material preparation and data collection were conducted by EC and VM. Data analysis was performed by EC with feedback and guidance on analysis plan and interpretation by VM and RS. VM and EC provided funding for the project. All authors contributed to the manuscript, study conception, and design.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The harm caused by defective vaccines to human health and social stability is immeasurable. Aiming at the government’s supervision of the vaccine market, an evolutionary game model is constructed to analyze the quality of supervision and the key factors in the dynamic interaction between government departments and vaccine manufacturers under different supervision modes in the vaccine manufacturing process. The results show that: (1) Severe punishment by government regulatory authorities, and increased costs of rectification after investigation and handling of involuntary behaviors of vaccine enterprises can effectively prevent involuntary behaviors of vaccine enterprises. (2) In the early stage of the game, the success rate of the government’s efficient supervision will make the vaccine enterprises continuously self-disciplined; when the vaccine market is relatively stable, the government’s supervision departments tend to be more conducive to passive supervision. (3) The success rate of government regulatory departments and the probability of a third-party reporting to play a great role in promoting the self-discipline of enterprises. (4) The power of government and regulation are conducive to promoting the active supervision of the government regulatory authorities but corruption of government and awareness of people are different. Once the phenomenon of vaccine enterprises’ non-discipline increases, the government regulation must change from passive regulation to active regulation. Therefore, the government should implement different measures according to the characteristics of each period in the manufacturing process to effectively prevent problematic vaccines. The conclusions and policy recommendations are significant for addressing the issue of insufficient self-discipline of vaccine manufacturers. For more than two thousand years, human beings have been searching for ways to fight all kinds of infectious diseases. Vaccine immunization is an economical and effective way to curb infectious diseases, and many diseases have been effectively controlled by vaccine immunization . HoweverIn recent years, many experts and scholars have studied vaccine regulatory events, regulatory approaches, and regulatory models. Vaccine safety research is an important component of public health program ,9,10,11.At present, scholars’ research on government regulation and vaccine quality and safety supervision is still insufficient. Firstly, with the development of society and the continuous awakening and improvement of public safety awareness, it is imperative for third-party regulatory agencies to participate in vaccine quality and safety supervision. The studies on third-party regulatory agencies participating in vaccine quality and safety have not received enough attention so far. Secondly, the government’s supervision of vaccine manufacturers pays more attention to the difference between active supervision and passive supervision instead of that between supervision and non-supervision before. Thirdly, in the existing game models, the government supervision department is regarded as a rational economic man who pursues the maximization of his own interests. This assumption is not consistent with the actual situation. In reality, the government supervision department aims at minimizing social losses. Therefore, under the assumption of bounded rationality of government supervisory authorities and vaccine manufacturers, this paper will analyze the equilibrium results by applying evolutionary game theory to vaccine quality and safety supervision. It is supposed to make the enterprise as self-disciplined as possible through the joint efforts of the government and the third party, and provides the basis for the government to formulate and improve various prevention policies and measures.There is a famous “broken window effect” in criminal psychology. If someone breaks the window glass of a building and the window is not repaired in a timely manner, others may be encouraged by some demonstration to break more windows ,24,25,26Under the mode of government supervision, there are many factors affecting the behavior of drug (food) enterprises, and many scholars have given different opinions. These research results focus on the impact of external and internal factors on their decision-making. This paper searches the fields of “drug regulation” and “food regulation” from Google academic and China HowNet, and the relevant summary is shown in According to the research results in recent years, it can be seen that the cost of government supervision, social benefits, benefits of drug (food) enterprises, penalties for non-cooperation, probability of public or third-party supervision are the core factors that affect the behavioral decisions of drug (food) enterprises. Therefore, based on the existing research costs, this paper took these factors as the main parameters to construct the evolutionary game model for behavioral decisions of vaccine manufacturers under different government regulatory modes.(1)This paper analyzes the competition and cooperation relationship and game strategy of the behavior decision-making of vaccine enterprises under the supervision of the government, and considers the third-party supervision as an environmental factor. Combined with the analysis of variables, it discusses the conditions that affect the evolution of the interaction between vaccine manufacturers and the government towards the direction of cooperation;(2)Based on the premise of bounded rationality, the decision-making process of vaccine manufacturing enterprises is regarded as a dynamic process of gradual learning. The evolutionary game model of government and vaccine manufacturing enterprises is constructed. The key factors affecting the game strategy of both sides are found by solving the model;(3)By analyzing the equilibrium point and stability of the evolutionary game between the government and vaccine manufacturers, the choice of the stable strategy of the two is studied. Then, through the changes of government punishment, third-party reports, and other parameters, using MATLAB simulation analysis, the evolution trend of the game of the behavior decision-making of vaccine enterprises is investigated under different government supervision modes, and reasonable countermeasures and suggestions are put forwarded to provide decision-making basis for government departments.The main problems to be solved in this paper are as follows:Hypothesis 1: Under different regulatory models, the game process between vaccine manufacturers and the government is also dynamic, and the strategies of both sides of the game can be adjusted at any time before reaching the evolutionary game equilibrium.Hypothesis 2: Bribery and rent-seeking behaviors between government departments and vaccine manufacturers do not exist.Hypothesis 3: In order to avoid the risk of penalties and ensure the maximization of benefits, the vaccine manufacturer will adopt the self-disciplined behavior to produce qualified vaccine with a certain probability .In this paper, the profit and loss variables between government regulatory authorities and vaccine manufacturers are set as follows:The government regulatory authorities need to invest certain human, material, and financial resources in the active supervision of vaccine production enterprises. This investment is affected by government power and regulations, and the resulting cost of active supervision is At the same time, active supervision will reduce non-self-disciplined behaviors of vaccine enterprises, which can be restrained from the source and effectively decrease social losses. Passive supervision is a type of behavior regulation after the vaccine manufacturers choose a non-self-discipline strategy, which usually causess some losses to the society. In the case of active supervision by government supervision departments, the success rate of their supervision is expressed by Assume that The game payoff matrix of the two players is given in According to Malthusian equation, the expected benefits of vaccine manufacturers for self-discipline strategy The replication dynamic equation selected by vaccine manufacturers is as follows.Similarly, the Therefore, the replication dynamic equation of active supervision chosen by government regulatory departments is as follows.Equations (4) and (8) constitute a two-dimensional dynamic system (I).Proposition 1. For the two-dimensional dynamic system (I), there must be Proof. For the above two dimensional dynamical system, when According to Friedman (1998) , the equThe Jacobian matrix of the system is:(1)(2)If both of the following conditions are satisfied, the equilibrium point of the replicated dynamic equation is the evolutionary stability strategy (ESS).From the above calculated data, it can be seen that Proposition 2. Assume that (1) When (2) If (3) If It is shown that the local stability can be determined by the determinant and trace values of the Jacobian matrix of the two-dimensional dynamical system (I).Depending on the above analysis, the two-dimensional dynamic system (I) in the case (2) and (3) in proposition 2 has corresponding evolutionary stability strategy. In particular, when Under the above assumptions, the evolutionary game model has four saddle points and a central point, which are , , , , and According to the solution of the model, the characteristic roots corresponding to the point f Taylor , they arProposition 3. When the profit-loss ratio of the non-self-disciplined behavior strategy of the vaccine manufacturer and the probability of success of the two different regulatory approaches meet the conditions Proof. The two-dimensional dynamical system (I) of the duplicate dynamic equation is as follows. It can be known that, when The optimal decision of the two sides is obtained through the establishment of the game model between the government regulatory department and the vaccine manufacturer. In view of the fact that although the government continuously strengthens the supervision of vaccine manufacturers, the non-self-discipline of vaccines occurs from time to time. It is of no practical significance to discuss the high incidence period of non-self-discipline and the period of enterprise self-discipline. In this context, based on the above analysis and the results, the influence of parameter changes on the strategic choice of both sides is discussed through analyzing the severity of undisciplined punishment, government active regulation, and government passive regulation. By setting relevant parameters, MATLAB R2017b software was used for the simulation to more clearly reflect the influence of parameter changes on the evolution direction of both sides. The initial value setting is shown in Matlab simulation was conducted based on the above parameter assumptions, and the results are shown in According to the definition of profit–loss ratio Based on the simulation shown in Depending on the simulation in Based on the simulation in Based on the simulation in According to the above analysis, the main conclusions can be drawn: (1) Severe punishment by government regulatory authorities, and increased costs of rectification after investigation and handling of involuntary behaviors of vaccine enterprises can effectively prevent involuntary behaviors of vaccine enterprises. (2) In the early stage of the game, the success rate of the government’s efficient supervision will make the vaccine enterprises continuously self-disciplined; when the vaccine market is relatively stable, the government’s supervision departments tend to be more conducive to passive supervision. (3) The success rate of government regulatory departments and the probability of third-party reporting play a great role in promoting the self-discipline of enterprises. Once the phenomenon of vaccine enterprises’ non-discipline increases, the government regulation must change from passive regulation to active regulation. (4) The power of government and regulation are conducive to promoting the active supervision of the government regulatory authorities, but corruption of government and awareness of people are different.Built on the above conclusions, the following policy suggestions are proposed for the government to guide and supervise the vaccine manufacturers to take self-discipline actions.Make full use of the publicity advantages of new media platforms, strengthen the publicity and education of vaccine safety knowledge, and improve public safety awareness. Specifically, continuously strengthen the team construction of the vaccine regulators and use new media software through various channels to improve the public’s ability to identify fake and substandard vaccines. Strengthen public awareness and safeguard their legitimate rights and interests of consumers to report complaints of their own, so as to establish the corresponding reporting incentives and actively guide the public to participate in supervision. Encourage and support the vaccine production line staff in their disclosure regarding vaccine quality and safety problems in a timely manner.Heavy penalties for non-self-disciplined vaccine production enterprises should be enforced according to specific situations. For a large-scale vaccine production enterprise, two hundred million RMB is not significant, while it is too much to bear for a small-scale vaccine production enterprise. Therefore, one of the principles that must be adhered to is to hit the “pain point” of the non-self-discipline vaccine manufacturers. Severe punishment will be meted out to the vaccine manufacturers that refuse to change.Government supervision includes active supervision and passive supervision. Active supervision requires the government to continuously increase capital investment in vaccine safety supervision, and urges food and drug safety supervision departments in various regions to conduct regular and irregular inspections on local vaccine manufacturers with high frequency and intensity. Passive supervision is mainly third-party supervision agencies and public supervision. The involvement of third-party regulatory agencies is a change from the traditional government regulatory model. With the introduction of a third-party regulatory agency, vaccine regulation is linked with “Internet plus”. Through the use of various information technologies, the vaccine safety supervision information platform can monitor vaccine manufacturers in real time, increase the depth and breadth of supervision, and realize the traceability of vaccines in various circulation links, which can effectively improve the supervision efficiency. Public supervision is an important part of vaccine safety supervision. When the government is in a state of passive supervision for various reasons, public supervision can effectively improve the success rate of the government’s passive supervision. Although the cost of government’s passive supervision is higher than that of active supervision, timely loss control is another way of self-protection. Public supervision is an effective supplement to government supervision. Multiple forms of supervision coexist to achieve multiple co-governances.In this paper, we investigate the evolutionary game of behavior decision-making of vaccine enterprises under the supervision of the government, and analyze the interaction of behavior between the players. However, we have focused on the situation where there are only two strategies for the players. In future research, we will consider the evolutionary game involving multi players and multi strategies, and the impact of government policies and strategies on the behavior decision-making of heterogeneous vaccine enterprises.