Digitonthophagus gazella. Although the community showed an initial dominance by the invader that decreased over time, the theory of regime shifts reveals the possibility of an ecological transition driven by climate factors (summarized here in a climatic index that combines minimum temperature and relative humidity). Mid and low values of the driver led to the existence of two alternative stable states for the community structure , whereas large values of the driver led to the single dominance by tunnelers. We also quantified the stability of these states against climatic changes (resilience), which provides insight on the conditions under which the success of an invasion and/or the recovery of the previous status quo for the ecosystem are expected. Our approach can help understand the role of climatic changes in community responses, and improve our capacity to deal with regime shifts caused by the introduction of exotic species in new ecosystems.Although biological invasions play an important role in ecosystem change worldwide, little is known about how invasions are influenced by local abiotic stressors. Broadly, abiotic stressors can cause large-scale community changes in an ecosystem that influence its resilience. The possibility for these stressors to increase as global changes intensify highlights the pressing need to understand and characterize the effects that abiotic drivers may have on the dynamics and composition of a community. Here, we analyzed 26 years of weekly abundance data using the theory of regime shifts to understand how the structure of a resident community of dung beetles  responds to climatic changes in the presence of the invasive tunneler  There is evidence that such ecological regime shifts can be driven by climatic factors like increased warming, heat waves, and droughts6. In addition, climate can interact with co-occurring anthropogenic disturbances such as invasive species9, increasing the per-capita effect of the invader on native species in some situations10, which may directly or indirectly shift the ecosystem to a new state11. The understanding of how invasive species affect changes of dominance triggered by climate factors is, however, still lacking. In this context, investigating a particular example can reveal important information on the climatic resilience of an ecosystem to the modifications brought by an invasive species. To address this knowledge gap, we used the theory of regime shifts to explore the climatic response of a dung beetle community in Brazil after the invasion of a competitor1. Specifically, our goal was to understand the response of the dung beetle community to climate in the presence of the strongly interacting invader, including the characterization of community structure, the possibility for alternative stable states, and their resilience.Ecologists have long had an interest in how and why abiotic or biotic drivers can lead to changes in the resilience of a specific ecosystem state, and under what circumstances such drivers will result in an ecosystem potentially moving into a different stable state12, empirical evidence of ecological transitions has continued to lag, leading to a debate on their prevalence and importance14. The current ‘gold standard’ to provide empirical evidence for regime shifts comes from a combination of empirical observation and experimentation or modeling15. The increasing availability of long-term datasets has provided another compelling avenue of investigation, especially in regards to how ecosystems respond to combinations of documented changes in system drivers16, although a challenge in the identification of an ecosystem’s stable states and shifts between them is still the absence of field studies, since laboratory observations are not always verified in non-controlled situations17.Although the theory behind resilience and regime shifts has matured considerably in recent yearsDigitonthophagus gazella . D. gazella was  intentionally introduced in Brazil aiming to reduce the negative effects of cow pads within grazed lands and control dung-breeding horn flies19. Dung beetles provide substantial ecosystem services within open-grass dominated ecosystems21. For example, their decomposition activity leads to a reduction of natural greenhouse gas emissions  by up to 40%23; moreover the burial of manure deposited in grazing systems is followed by nutrient cycling, increasing grass growth and fertilization24.Here, we analyzed a previously published 26-year dataset on a focal native dung-beetle community after the introduction of the non-native 25, and therefore shifts in this functional diversity are of clear practical importance in Brazil and elsewhere. Thus, here we used functional diversity to represent the state of our focal dung beetle community. The community was composed of two functional groups: dwellers, which do not move the dung from its original place but consume the food directly in the dung piles; and tunnelers, which excavate tunnels and store the food underground27. The presence of D. gazella had a clear effect on functional redundancy within our focal system, as it replaced nearly all native species of its same functional group (tunnelers) and suppressed the abundance of the other . During the same time frame, local climatic conditions shifted toward drier and warmer conditions in our focal region, likely in accordance with the El Niño Southern Oscillation1. Each dung beetle group in this community exhibits a certain response to these climatic factors (see below), and therefore it is expected that the diversity of the dung beetle community also shift in accordance with prevailing climatic conditions. Mesquita Filho et al.1 indeed found that several climatic factors drive each group’s changes over time. Here, using the tools and concepts from the theory of regime shifts, we characterized the qualitative and quantitative response of the community to climatic changes by defining a single variable representing community structure (system state), and a single index representing climate. Although intermediate values of the climatic index should facilitate the survival of both groups, it is unclear whether this will translate into a single state in which one group dominates over the other, or the system can alternate dominance by either group. It is also unclear whether there is a climate range that favors overwhelming dominance by one group, and what that range is. To fill this gap, we explored whether the focal system transitioned between states, and measured the resilience of the associated states. Our framework contributes to understanding the environmental conditions that, in the presence of an invasive species, may trigger long-term changes in the community. In addition, our methodology may provide important information to support management plans, thus increasing the capacity to deal with these regime shifts and reduce the likelihood of disruptive events30.The functional composition of a dung beetle community is key to the provisioning of these servicesUrochloa decumbens). The native vegetation was removed, pasture areas were implemented, and livestock was introduced in the 1970s, maintaining this configuration during the following 50 years. The climate of this area is categorized as equatorial savanna, with dry periods concentrated mostly during the winter, from April to August. During our sampling period , no vermifuges and insecticides that could affect negatively the community of dung beetles associated with cow pads were used1.Our focal ecosystem is in Selvíria, state of Mato Grosso do Sul, Brazil  and ten native tunnelers . These species were chosen for our study because, as the invasive tunneler D. gazella , they all co-occur in pasture and exploit the same resource (cow pad)32. The initial establishment of D. gazella caused the loss of most of the native tunnelers from the community, with the invader becoming the overwhelming representative of the functional group, and an initial decrease of abundance for dwellers. Differently from native tunnelers, however, dwellers were able to recover their number a few years after invasion . Thus, we considered only samples with a total number of beetles  higher than the value of the median of all data, a conservative threshold that retains observations that allow for as much representation of the community as possible. As will become evident in the We focused first on the weekly abundance data, which we needed to process in order to avoid spurious results in our analyses stemming from the measurement protocol. Specifically, we filtered out seasonal low values associated with sampling in the coldest periods, when few beetles are captured because the reduced activity in all functional groups restricts their spatio-temporal distribution1, we categorized all sampled species into either dwellers or tunnelers. D. gazella is a tunneler and, as explained above, the native tunneler species experienced massive declines in abundance after its establishment, leaving D. gazella as almost the single representative in the tunneler functional group during the period of observation1. Thus, given the sharp contrast in community composition, we also separated the data into before and after invasion using to that end the 200th week, when D. gazella was first observed at the study site .Following Mesquita -Filho et al.i, 44. Here, we chose K did not alter our results qualitatively. In addition, the original data showed random mismatches in the phenology of each group, which gave the wrong impression of extreme short-term shifts in functional group dominance within the community. To avoid such artifacts, we used nonparametric local regression (LOESS)45 to smooth the dynamics of each group46. For this smoothing, we employed the loess function in the R software 3.6.147 with a smooth parameter equal to 0.25, but other moderate values  did not alter our conclusions. Finally, we extracted back from the smoothed curve the number of beetles within each functional group to calculate the fraction D. gazella observed per week (after invasion), using their corresponding smoothed curves. Including also native tunnelers after invasion did not alter our conclusions.To describe community functional composition (i.e. system state) through time, we derived a normalized functional group ratio. First, because the abundance of each functional group spanned up to four orders of magnitude, we performed a logarithmic transformation of the number of captured insects from each group T corresponds to the normalized temperature, RH. Based on the information above regarding beetle response to climate, the merged climate factor c was defined as the relationship:D. gazella) lead to higher values for c. On the other hand, lower temperatures and/or more humid conditions (expected to favor dwellers) imply lower values for c. Intermediate values of c can represent either moderate or extreme values for bothT and RH.We devised a single climatic driver variable that merges the weekly measurement of temperature and relative humidity over the years, abiotic factors key to the survival and reproduction of both groups (see above). We first converted minimum temperatures and relative humidity to normalized climate variables using a min-max normalization :density, R software 3.1.647) for a given range of climatic driver c values. We grouped the c of size 0.4, to ensure a significant amount of weekly samples that allowed for the reconstruction of these probability distributions , shows by design well-defined minima for the most frequently observed values of 51. Nonetheless, because the position of extrema is invariant under the transformation, using probability distributions instead would not alter our conclusions.Further, we transformed the kernel density function:c values allowed us to identify stable community configurations associated with a specific climate. The comparison of the potentials obtained for different c ranges enabled the description of how the community changed in response to climatic variation. The location of the minima revealed which states were stable for a given value of the climatic driver; the presence of two minima, then, flagged the existence of bistability .Representing the potential obtained from all the 53. Thus, in addition to number and location of wells, measuring their associated area allowed us to further characterize the resilience of the community. To this end, we first set a visualization window common to all potentials. Specifically, we plotted the potentials within a range for the vertical variable  given by These minima are materialized as wells in the potential’s landscape, which provides an easy way to understand the concept of stability: the dynamics of the system for the given value of the driver will eventually “fall” into a well (either a state dominated by dwellers or a state dominated by tunnelers), with the shape of the well (e.g. its depth) determining how difficult it is for the system to “escape” that state. Therefore, the area inside a well provides quantification of the tendency of a system to stay in that specific state, i.e. the resilience of the associated ecological state or how strong a perturbation has to be to move the system from such an ecological state to anotherc , allowed us to study how their observed behavior over time materializes in a driver-state relationship  defining the possible shifts in dominance (i.e. regime shifts) that the community may undergo as climate changes12. The non-monotonic temporal behavior of the components of the order parameter (i.e. dwellers and tunneler availability) and the components of the control parameter (i.e. temperature and relative humidity) makes it difficult to predict the shape of the phase diagram, and therefore whether we can expect alternative stable states in the focal example. For such cases, the dominance of the dung beetle community could (1) shift in a linear fashion toward the functional group favored by climatic conditions; (2) shift between functional groups in non-linear threshold response to climatic conditions without hysteresis; or (3) shift between functional groups in non-linear threshold response to climatic conditions with hysteresis –and thus showing bistability . Other possibilities, e.g. a non-linear shift between functional groups where one group is favored at intermediate climatic conditions12 are discarded as the invader is better suited for warmer and drier conditions. To evaluate which of these possibilities occurred, we represented c, as well as the location of the minima shown by the potentials above. In addition to the emerging shape of this relationship, this plot can reveal the presence of alternative stable states if two or more different points occur for the same value of the control parameter, c.Measuring a state variable, mentclass2pt{minimWe calculated the median of all weekly sample sizes  favored the invasive species (a tunneler), allowing it to effectively exclude most native tunnelers and dominate the community. Intermediate climatic ranges led to the bistability of alternative states where either dwellers or tunnelers dominated. This reveals the existence of an abrupt transition (or shift in dominance) triggered by the combined effect of temperature and relative humidity.Identifying and characterizing ecological transitions, and the possibility of alternative stable states associated with the transition, is an elusive but essential task. Given the accelerated rate of climatic change, understanding how potential climatic drivers affect a focal system, and whether they trigger a significant change in its observed state (i.e. a transition or shift) or lead to bistability, is key to understanding system dynamics and informing management decisions. Identifying the control and order parameters of the transition provides invaluable information to this end, but requires a large amount of data. Here, the use of a 26-year data collection in combination with the theory of regime shifts has unveiled a climate-driven dominance transition and alternative stable states in a beetle community in the presence of the invasive c, control parameter). These two measures provided a clear picture of the qualitative changes in beetle functional composition after invasion under different climate conditions. After invasion, the dominance of tunnelers or dwellers strongly depended on c.We identified an ecologically relevant parameter able to represent the state of the community  favoring the invading D. gazella. This result is highlighted by the decrease of the fraction of dwellers, D. gazella individual growth rates, the invader managed to thrive in such conditions, which may be the reason why it established as a non-native species even in arid ecosystems within Texas and Australia55. Although temperature variation has been flagged in Texas and Australia as an influential factor in favoring the establishment of D. gazella57, it has remained unclear until now how temperature and relative humidity influenced long-term shifts in native dung beetle community structure in these locations.Our results illustrate how the establishment of the invader, involving survival and reproduction, required for the organism to overcome a “survival barrier”c, favored instead the dweller functional group. Our data, however, are not conclusive enough to identify the origin of these climatic changes, although it is possible that they are associated with El-Niño Southern Oscillation, typical in this region1.The subsequent decreasing trend for the composite driver, D. gazella, i.e. bistability, with the invader representing the vast majority of tunnelers. The presence of alternative stable states in this dung beetle community leads to uncertainty about its expected structure for a given range of environmental conditions. For values of the composite driver Under climatic conditions with intermediate temperatures and relative humidity, our results show the possibility for either dominance by dwellers or the tunneler 58, which contributes to niche separation and reduces competition across groups. Climatic changes that are non-extreme, however, could tilt dominance towards one versus the other group, at least when those changes occur while the community is still in the transient years after invasion .A plausible explanation for bistability in this climate range is that intermediate temperatures and relatively humid conditions favor both groups, creating circumstances in which either could competitively dominate. Coexistence is reinforced by the fact that the native beetles can explore outside grasslands and utilize dung from animals other than cattle, whereas the invader is specialized to open areas and use of livestock dungD. gazella allows it to thrive. In other words, such conditions enable the growth of one group and hinder the growth of the other, preventing the existence of a second well in the potential. The calculation of potentials thus offers more focused information than observing only how annual abundance  lead to the overwhelming single dominance by tunnelers because, on one hand, dung piles are hotter and drier, which is detrimental for dwellers as they consume food at the surface of the dung pile; on the other hand, the plasticity in climate tolerance of the tunneler c values). The threshold is, nonetheless, necessary because weeks with low activity/observations may lead to the mischaracterization of the community based on only a few individuals, consequences exacerbated by the fact that we focus on the structure of the community .The existence of alternative stable states was unaltered by either relaxing or tightening our (conservative) choices for smoothing intensity or the threshold above which we considered weekly data as representative. The latter is especially important, since reduced beetle activity (and therefore fewer observations) occurred mostly in colder weeks; our results show that the reported alternative stable states do not result from removing those weeks  is more limited55. The phase diagram, however, shows that low values of the composite climatic driver c were not enough for dwellers to recuperate dominance over tunnelers when these climate conditions prevailed. Thus, the resurgence of the native dweller beetle community in the years after invasion is not necessarily a sign that the community can be fully restored after the removal of D. gazella, as its success depends on climatic conditions.The invasive D. gazella and the native tunnelers59, all part of the same taxonomic group. Moreover, the presence of bistability and associated hysteresis further reinforces the possible irreversibility of the transition to single-species dominance by D. gazella that occurs for higher c. The discontinuity and bistability of the order parameter in the phase diagram, resembling the brown curve (i.e. hypothesized curve 3) in Fig. c differently from the way it will respond to changes that decrease the climatic driver. This path dependence may eventually hinder an eventual single dominance by dwellers .The invasion resulted in the restructuring of the dung beetle community, with a stronger impact of the invader on tunnelers than on dwellers as it reduced the diversity of resident tunnelers and almost drove them to extinction (see above). This increased impact on beetles with similar feeding behavior may be explained by the the functional closeness of c that would increase this probability, are represented by the area of the associated wells and how it depends on c. Our results suggest that, in the presence of the invader, the dweller-dominated state loses stability as the driver c increases, in favor of the tunneler-dominated state. This interpretation of the area of the wells in turn enables a quantitative understanding of the resilience of a particular desirable (e.g. dweller-dominated) state and, therefore, of the community structure. According to this, the resilience of the dweller-dominated state decreased as the composite driver, c, increased whereas that of the invader-dominated state increased. In contrast, before invasion the stability of the dweller-dominated state remained barely affected by the climatic driver, i.e. the community structure remained resilient against environmental change. Bistability can be expected for any range of the driver for which the areas of both dweller-dominated and tunneler-dominated wells are different from zero, which for our system occurs for intermediate values of c after invasion.The probability for the dweller-dominated state to withstand a given climate fluctuation, and/or the ranges of the composite driver c and before invasion. Thus, although the bistability we observed is robust from a biological and a technical  viewpoint, more data would have allowed us to include a wider range for c in our analysis and thus investigate whether lower c lead to the recovery of single dominance by dwellers even in the presence of the invader.Characterizing ecological transitions is very data-intensive. Despite compiling more than 1300 weekly observations spanning 26 years, we needed to use a high smoothing factor to eliminate low-sampling noise that occurred especially for extreme values of the climatic driver c did not benefit resident tunnelers  as to shift the community structure. Also, bistability was not observed before invasion despite the apparent lack of trend shown by the driver, c. This suggests that the invasion changed how the community responded to climatic changes: before invasion, the dominance of dwellers would be robust against changes in temperature and relative humidity within ranges for which the community post-invasion showed bistability. The observations before invasion (200 weekly observations), although a rare feat in invasion studies, were still sparse in the context of regime shift analysis and therefore our conclusions regarding the period pre-invasion are to be taken with caution.A longer time period of data collection would also have allowed us to be more certain regarding the behavior of the system before invasion. Our data indicate that, before the invasion, the dweller functional group dominated under all climatic conditions experienced in this time frame. The potentials show that changes in the value of the composite driver 1 as correlating with changes in the number of dwellers and tunnelers. The robustness of our post-invasion results further justifies our choice of driver. However, other factors may also correlate with changes in the number of beetles that we could not include here due to lack of information, such as natural predators targeting the invader after it started dominating the community. Moreover, although not the scope of this study, the co-occurrence of climatic changes and the invasion prevented us from discerning the effects of these two factors separately.Another aspect that calls for caution is the potential existence of other factors that may participate as a driver of the transition. We focused on the combination of temperature and relative humidity because both are important factors influencing beetle survival, and were identified in60, conditions that our study indicates should favor the dominance of D. gazella.Our results constitute evidence for the effects of climatic change on the establishment of new exotic terrestrial arthropods. Our framework provides information about the climatic conditions that will favor dominance by either dung beetle functional group, and hence the conditions are more optimal for each group. Recent evidence shows that the area of study , is becoming warmer and drier61. This information, together with the identification of the order and control parameters associated with the regime shift, can help managers and stakeholders devise strategies designed not to stop the (climate-driven) transition, but rather to ameliorate its potentially disruptive consequences. For example, ecosystem engineering may help eliminate bistability totally or partially48, thus reducing uncertainty and increasing the effectiveness of management for invasive species impacts. Thus, our methodology provides relevant information for regional land management, increasing the capacity of local human communities to anticipate regime shifts and reduce the likelihood of associated disruptive consequences30.Although we applied our methodology to a specific case study, it should be similarly useful in other systems involving biological invasions and climatic change. Given the increased concern on the effects of climate change on ecosystems and the widespread introduction of non-native species, our study provides a framework to assess how these dual forces of global change can interact to cause regime shifts. Climate change may either prevent or favor recently introduced species, the latter disrupting the structure of local communities and leading to augmentative shifts in the stability of ecological systems, which in turn modifies relationships of dominance and may cause loss of biodiversity. In such cases, like in our study, the biological invasion is regulated by climate factors, and therefore it is not possible to propose a management plan to reverse the associated shifts. However, by identifying the key elements involved in these shifts, our framework helps pinpoint and prioritize vulnerable species or groups in the focal systemSupplementary Information.


The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging. With the development of drone technology over recent years, unmanned aerial vehicles (UAVs) have been widely used in civil fields, such as power-line inspection, target tracking, agricultural plant protection, and geographic mapping ,2. HowevRaster maps are more advantageous in representing the real environment, and enabling precise positioning and navigation; research into the problem of merging together multirobot raster maps has attracted much attention ,6,7,8. TX and Y axis directions, and a rotational angle In a two-dimensional raster map, there are three degrees of freedom representing translation in the problems ,20 becauproblems . We alsoproblems  on genetproblems , we applThe crossover and variation operators are improved compared with traditional genetic algorithms. A directed crossover operator and a multidimensional adaptive perturbation mutation operator are designed. To keep the population evolving excellently, a directional cross operator is employed. Individual variations in the population take different forms of variation depending on the fitness via the multidimensional adaptive variation operator.The raster maps are filtered on the basis of raster thresholds, and a mathematical model to optimize the merging maps is constructed. The DCPGA algorithm is applied to solve optimization problems in the process of merging maps, and achieves the real-time map merging of two UAVs with LiDAR. A distributed mapping–centralized merging method is used to improve the system’s efficiency, and a judgment function is introduced to avoid overlay between the two maps.In this case, the DCPGA algorithm still receives an erroneous optimal solution. A judgment function was added in this paper. We propose an improved genetic algorithm based on a directional cross and multidimensional perturbation mutation (DCPGA). The contributions of this paper are summarized as follows:As shown in R is a two-dimensional rotational matrix related to rotational angle t is a two-dimensional column vector, as shown in Equation R=coT is obtained, a global map As shown in Obstacle point cloud maps were assumed to be R, and translational transformation is t, which is shown in M¯B[xOn the basis of transformed matrix x is a continuous variable with domain D-dimensional solution space. Function f if a, called a generation. At each generation a, the DCPGA performs selection, crossover, and mutation to update the current population of solutions Genetic algorithm (GA) ,25 is anX and Y axis directions, respectively. The position of the chromosome is, therefore, coded as In a two-dimensional raster map, there are three degrees of freedom representing the displacement and deflection angles in the n. The probability of it being retained is calculated according to Equation p(xi)This method of selection ensures that all individuals have a certain chance of being retained. If the fitness of an individual is greater, it has a greater chance of being retained.In a series of genetic operators, the crossover operator generates better individuals through gene pairing and crossover. The main operation of the crossover operator is to achieve genetic optimization. Before crossover operation, we sorted the chromosomes as Equation :(7)x¯a,jj-th dimension chromosome generated by the crossover, a-th generation, In Equation , x¯a,j rGenetic optimization algorithms tend to fall into the local optimum and cause the convergence result to deviate greatly from the optimal solution. In order to improve population diversity, and reduce the degree of population aggregation, mutational operations are introduced. When the algorithm has a precocious convergence phenomenon, the optimal position of the group is a locally optimal solution. The individuals tend to move closer to the optimal position of the group, at which time the individuals gather near the local optimal solution and lose the ability to find a better solution existing in other positions. If the optimal position of the group can be perturbed out of the local optimal solution, the algorithm would achieve further convergence. Therefore, globally optimal individuals and the method of multidimensional random perturbation of the optimal position of the group were mutated to jump out of the local optimal solution in this paper. The variation method is as follows.D of the population selected for random perturbation and the j-th dimension random perturbation were given as shown in Equation xa+1N and at the t iteration, the coordinate position vector of each individual is expressed as The mutation probability of the heuristic optimization algorithm should be adjusted with reference to the population aggregation degree. If the population size is fined as :(10)σ=1σEquation .(11)XavIn the previous experiments, the population standard deviation was concentrated in the interval b is the offset of the variation probability and it is a constant. C is the maximal number of algorithmic iterations. b are parameters to be set and we experimented several times to find the suitable values of Therefore, the variation probability calculation formula is established on the basis of population standard deviation and iteration times as in .(12)p=ωc of overlapping areas as follows:The map-merging function is solved on the basis of the assumption that there are overlapping areas between local maps. There is no overlap of local maps at all due to the distributed mapping–centralized merging method when the working area is large. In this case, the DCPGA algorithm still receives an erroneous optimal solution. Therefore, a judgment function is added to determine whether the maps were merged successfully. The judgment function is given by combining the raster similarity and confidence In , comcom , that waThe establishment results of the submaps and the fusion maps are shown in We experimented with a single UAV 10 times and recorded the time of each experiment. For comparison, the experiments of mapping with two UAVS were carried 10 times. The results are shown in This paper focused on the problem of raster map merging when multiple UAVs are used for collaborative exploration. The theory of image matching was adopted in this paper. A mathematical model was constructed to transform the raster map-merging problem into a multidimensional combinatorial optimization problem. A DCPGA was proposed based on the random distribution of the optimization objective function. Directional crossover kept the algorithm searching in the direction of the optimal solution. Multidimensional perturbation variants could overcome the prematureness problem of genetic algorithms, and reduce the possibility of genetic algorithms falling into local optimum solutions. Experimental results from algorithmic simulations showed that the DCPGA had a fast convergence rate and strong global convergence capability. In the experiments of merging maps, the raster map similarity was used as a metric, and the Euclidean distance transform and the improved algorithm performed a global search for the best overlap between each local map. The experimental results demonstrated the feasibility and effectiveness of the method in complex environments.The proposed method was only applied to offline map merging during exploration experiments with two UAVs. Research will next be conducted to achieve the online real-time map merging of two drones, and the collaborative mapping of more drones. The DCGPA will also be improved to improve the efficiency and accuracy of mapping.


In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases. The large amount of online data allows scholars to perform tremendous research on health conditions, specifically oral health. Regardless of the high-performing dental consultation tools available in current healthcare, computer-based technology has shown the ability to complete some tasks in less time and cost less than when using similar healthcare tools to perform the same type of work. Machine learning has displayed a wide variety of advantages in oral healthcare, such as predicting dental caries in the population. Compared to the standard dental caries prediction previously proposed, this work emphasizes the importance of using multiple data sources, referred to as multi-modality, to extract more features and obtain accurate performances. The proposed prediction model constructed using multi-modal data demonstrated promising performances with an accuracy of 90%, F1-score of 89%, a recall of 90%, and a precision of 89%. Dental caries is one of the most highly chronic oral disease affecting populations of various ages worldwide ,2 and waNowadays, dental caries has become a problem for oral health and healthcare in general. Despite the action taken by current healthcare, the speculated reasons for the high recurrence of caries are suspected to be poor oral health behaviors and failure to attend appointments due to financial situation . HoweverThe main contribution of this work is to propose a novel approach for dental caries prediction using a multi-modal deep neural network with two pathways and having the merit of learning patterns from heterogeneous features of the different data sources and making accurate predictions. Considering the ability of multi-modal to provide more information using different data sources, we utilized the Hybrid model  and Artificial Neural Networks (ANN)) to learn from the gathered data to predict dental caries precisely and accurately within a given population.The remainder of the proposed work is organized as follows: Artificial Intelligence (AI) has been used in every work domain for the past few years. They are extensively being used in the medical healthcare domain to better people’s lives. Given the fast development witnessed in the AI field, together with a large amount of healthcare data available, researchers are opting more for computational prevention approaches to develop new tools that accurately and efficiently diagnose patients . DietaryHealthcare has exploited the advantage of automatically extracting essential features from the image dataset offered by the Deep Learning (DL) models . CNN wast + 1 and a RF used to predict the affective state). Venugopalan, J. et al. .The MMDCP model was applied to the multi-modal data to predict the presence or absence of dental caries in patients. The proposed model fuses the features from both modalities  and aggregates the collected information to increase the prediction accuracy. Among the different DL model available, this study combines two efficient methods suitable for the types of data used: ANN and DenseNet201. The neural network structures of the MMDCP model separately extracted features from the two modalities and performed an intermediate-feature-level fusion. The ANN model extract features from the numerical dataset, while the DenseNet201 is applied to extract features from the imaging dataset. The fusion of the intermediate features was performed by concatenating the output vectors of the two DL models, and these generated output vectors are then passed to the classification layer to predict the state of patient oral health .The standard ANN  algorithm was previously experimented on for the numerical dataset during our previously published work on dental caries . As descOn the second branch of the proposed model, to train on images data, we made good use of the pre-trained DenseNet algorithm , with ththl:Considering the preprocessed dental caries image input of the DenseNet to be Batch Normalization is obtained as shown below:K, another important feature of DenseNet helps in generalizing the thl layer as follows [The growth rate  follows :(8)K[l]=To tackle the dental caries prediction using multi-modal data, intermediate features generated by ANN and DenseNet201 algorithms are merged. The vectors generated by the two networks, Equations (2) and (3), are concatenated together to produce the output vector as shown in Equation (9) which in turn is the input of the classifier to predict the presence or absence of dental caries.With the complexity of the proposed model and the enormous dataset used, an optimal experimental environment was required for the success of our work. This study conducts the experiments on a computer with the following settings  and a test set (20% of the data set). Then the training set was further divided into training (80% of the training samples) and validation set (20% of the training samples). Given the nature of the task at hand, evaluation metrics  for classification problems were used to evaluate the performance of the proposed model, therefore observed to avoid the overfitting. The following TP (True Positive): positive observation predicted as positive;TN (True Negative): negative observation that was correctly predicted as being negative;FP : negative observation wrongly predicted as positive;FN : positive observation wrongly predicted as negative;TPR (True Positive Rate): is the percentage of class labeled as “0” (absence of caries) points incorrectly classified by the model;FNR : is the percentage of class labeled “1” (presence of caries) points correctly classified by the model.Where:During our experiments, we used metadata gathered from the numerical and dental image datasets to predict the presence or absence of dental caries. Different features from the multi-modal dental datasets were extracted to train the hybrid model (constructed of ANN and DenseNet201) to predict dental caries. Our datasets were both divided into three sets: the training set of each modality is used on their respective network, allowing the hybrid model to identify a pattern within the data samples; the validation set is used to validate the model performance during training and fine-tuning the model hyperparameters; and, finally, the test set: used assess the performance of the trained hybrid model. The commonly used metrics of evaluation  for classification problems were selected to evaluate the proposed model. To analyze and evaluate the model’s capability in distinguishing the different classes , we proceeded by computing the AUC-ROC of the model. The higher the AUC-ROC value, the better the model performance when distinguishing between the label “0” and label “1” classes. The proposed model yielded an AUC-ROC of 0.90. Finally, to summarize the performance of the proposed model by comparing the frequency of the predicted classes to the expected classes, we performed a confusion matrix of the proposed model, as shown in the In this study, we proposed a novel MMDCP model used to predict dental caries among young people living in Korea; one of the datasets was provided by the Korean Center for Disease Control and Prevention under the Ministry of Health and Welfare. The critical difference between this work’s novel approach and prior works is that it has leveraged the usage of multi-modal data for the training on a hybrid model . Using the classification metrics of evaluation to assess the model performance, the results obtained in this study show that the proposed multi-modal prediction model allows to accurately predict the presence or absence of dental caries in patients. This work displays the importance of multi-modality in developing prediction models on healthcare datasets. Consequently, future works should consider using several modalities to improve the training technique’s performance.From the thorough analysis performed to assess the novel model performance in significantly predicting dental caries, the multi-modal dental caries prediction model proposed in this work yielded more robust prediction results than some previously published work on dental caries prediction using a single modality dataset. As such, the authors of  presenteRegardless of the impressive results generated by the prior published works on dental caries prediction, the novel approach present in this study displays significant results showing the importance of multi-modality and hybrid networks for prediction operation.To the best of our knowledge, this study conducted the first study using a multi-modal dataset  for dental caries prediction using the DL technique, and we firmly believe that using multi-modality in oral healthcare can be a big step toward the development of more efficient prediction models in dental caries prediction.In this study, we introduced a novel method to predict dental caries using a multi-modal dataset  that combines numerical and image data. In order to perform a binary classification and predict the presence or absence of dental caries, the model was fed the extracted features from the two modalities. Numerous experiments were performed on the datasets, and the results revealed that the proposed model was very effective. When compared to numerous earlier single modality models for the prediction of dental caries, the model presented in this work displayed better accuracy, precision, recall, f1-score, and AUC-ROC. As a result, this highlights the high likelihood of using multi-modality in dental caries prediction.During the experimentation stage, there were some limitations that had to be overcome, such as the lack of a large collection of X-ray dental images, by using dental images and data augmentation as described in the data preparation section. For our upcoming work, the labeling of X-ray images dataset is currently being performed.The suggested novel approach performed admirably when evaluated using the test data and could thus be applied to the development of a diagnosis tool that can aid dentists in accurately and meaningfully recommending treatments to patients who have a past, present, or future risk of developing dental caries. In order to improve oral healthcare and lessen the burden on the population, we are currently working on the implementation of a decision support system for dental caries prediction.


Light detection and ranging (LIDAR) is an active remote sensing system. Lidar echo signal is non-linear and non-stationary, which is often accompanied by various noises. In order to filter out the noise and extract valid signal information, a suitable method should be chosen for noise reduction. Some denoising methods are commonly used, such as the wavelet transform (WT), the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and their improved algorithms. In this paper, a new denoising method named the WT-VMD joint algorithm based on the sparrow search algorithm (SSA), for lidar signal is selected by comparative experiment analysis. It is shown that this method is the most suitable one with the maximum signal-to-noise ratio (SNR), the minimum root-mean-square error (RMSE), and a relatively small indicator of smoothness when it is used in three kinds  of simulate lidar signals. The SNR is increased by 138.5%, 77.8% and 42.8% and the RMSE is decreased by 81.8%, 72.0% and 68.8% when being used to the three kinds of cumulative signal without pollution. Then, the SNR is increased by 83.3%, 60.4% and 24.0% and the RMSE is decreased by 70.8%, 66.0% and 50.5% when being used to the three kinds of cumulative signal with aerosol and clouds. The WT-VMD joint algorithm based on SSA is used in the denoising process for the actual lidar signal, showing extraordinary denoising effect and will improve the inversion accuracy of the lidar signal. As a combination of traditional radar technology and modern laser technology, lidar has the advantages of high spatial and temporal resolution, wide detection range, and so on . It is aThe noise in the lidar signal has the characteristics of continuous amplitude and random phase in time and can be regarded as a typical nonlinear and non-stationary signal. For this kind of signal processing, the current common denoising methods include wavelet transform (WT) , empiricWith the deepening of denoising research, for the purpose of improving the performance of the single denoising algorithm, some scholars try to combine a variety of algorithms and have achieved specific research results ,12. Hu eThe rest of this paper is organized as follows. The related methods, including WT, EMD, VMD, WT-EMD, and the proposed denoising method WT-VMD, are introduced in The wavelet transform is based on Fourier transform and has good characteristics in the time-frequency domain. This method describes the signal as a superposition of a series of wavelet basis functions at different scales. The relationship between the frequency components and the wavelet basis functions can be characterized by the approximate coefficients and detail coefficients obtained after wavelet decomposition. The noise is contained in high-frequency components corresponding to the detail coefficients. Wavelet threshold denoising is the removal of signal noise by zeroing or shrinking the detail coefficients using a threshold function while retaining some of the approximate coefficients. The WT method requires the selection of a wavelet basis function that is similar to the signal to be decomposed. Appropriate decomposition level and threshold function can avoid incomplete separation of the signal or filtering out valuable information. It depends on the signal characteristics to determine whether the hard or soft threshold function is used. The hard thresholding function can preserve local features such as signal edges, but it is easy to cause distortion and discontinuity in the signal; the soft threshold function is relatively smooth but may suffer from loss of high-frequency information and blurred edges . The selres).Empirical mode decomposition is an adaptive time-frequency processing method, which decomposes a set of nonstationary nonlinear signals into a series of intrinsic mode functions (IMFs) arranged from high to low frequency and a residual component (Since the noise is primarily contained in the low-order modes (high-frequency components) of the IMFs, part of the high-frequency components can be directly discarded to achieve the purpose of denoising.C. In general, C belongs to  range.given by (5)kth=aIn contrast to the WT method, the EMD method does not require a predetermined basis function and the appropriate mode function can be chosen to decompose the signal according to its own characteristics. Although the EMD method does not require the selection of parameters such as decomposition levels and threshold functions, modal confounding, endpoint effects, and difficulties in determining stopping conditions may occur . Other dVariational mode decomposition is also an adaptive signal processing method, which continuously updates each mode function andwidth .(6)xt=∑Contrary to EMD, the VMD method realizes the decomposition of the signal from low frequency to high frequency. The noise is hidden in the high-order BLIMFs. Therefore, the VMD method selects the low-order modal function to reconstruct directly to remove the noise.Compared with the EMD method, it has a fast convergence speed, strong robustness and can effectively avoid problems such as modal aliasing and end effects. However, VMD is not suitable for all non-stationary signals. Some non-stationary signals need to be preprocessed before using VMD.K and the secondary penalty factor K and K and When VMD is used for signal decomposition, the number of decomposition layers  results ,25. UsuaHowever, in practice, manual search is not feasible. Consider using swarm intelligence optimization algorithms to apply to VMD parameter selection problems. Sparrow search algorithm (SSA) is an effective optimization technique, which simulates the foraging and anti-predation behaviors of sparrows . Its parThe fitness function in SSA needs to be determined according to the function characteristics of the parameters to be optimized. According to the VMD algorithm of the parameters to be optimized in this paper, the energy entropy is selected as the fitness function. Energy entropy reflects the distribution of signal energy in the time domain, and its size is inversely proportional to the proportion of the signal in the total energy. In the process of parameter combination optimization, it is necessary to calculate the energy entropy of each fitness function in SSA. The formula is as follows:The local minimum energy entropy is used as the The single denoising algorithm has its own merits and demerits. Both EMD and VMD achieve efficient denoising by directly removing high-frequency modes, but there is also a small amount of signal information mixed in the high-frequency components. WT processes the noise components by threshold so as to protect useful signal peaks and bursts well, but it also leads to incomplete noise removal. Therefore, this paper attempts to combine WT and EMD/VMD algorithms to enhance the denoising ability and obtain more efficient results.WT-EMD: first, the raw noisy signal is denoised by WT, then the denoised signal is decomposed using EMD. Finally, the signal is reconstructed by superposition of the relevant components.WT-VMD: after the overall denoising process with WT, the denoised signal is further decomposed by the VMD method and then the relevant BLIMFs are selected for reconstruction to complete the secondary denoising.The echo signal received by lidar in the actual work can be simulated by simulating the number of ideal lidar echo photoelectrons and adding Poisson noise ,28. Due The wavelet basis functions of WT include haar, sym, coifle, dB, etc. In this paper, the sym wavelet is chosen as the wavelet basis function. Sym wavelet is a typical discrete wavelet basis with good orthogonality and symmetry, which is suitable for the processing of lidar echo signals. The vanishing moment sym3–sym8 and decomposition level 3–6 are chosen, respectively, to denoise the simulation echo signal. It is experimentally determined that the denoised signal has the best SNR and the lowest RMSE when the wavelet basis function is sym7 and the decomposition level is 4. Therefore, this paper uses the sym7–4 scheme for wavelet denoising in the subsequent experiments. In order to obtain better overall continuity of the signal, a soft threshold function is chosen for denoising in this paper.The selection of EMD relevant components and VMD decomposition levels can be determined adaptively by using the methods mentioned in r) [SNR means the lower noise ratio. The smaller RMSE means closer to the raw clean signal. The smaller r means the smoother curve.The noise of lidar signal is filtered by different single algorithms and combined algorithms, respectively, and the denoising effects are evaluated by three indicators: root mean square error (RMSE), signal-to-noise ratio (SNR), and smoothness (r) :(10)SNR=A clear sky aerosol model is used to simulate lidar signals without pollution, and three sets of different cumulative pulse numbers (50\100\1000) represent different original SNR conditions. Denoising experiments are carried out using the five different denoising algorithms mentioned above, and the results are shown in The original clean signal without air pollution is a smooth curve. Due to the large amount of noise in the remote distance, the simulation noisy signal fluctuates greatly with the bad smoothness of the curve. The signal noise content of 50 accumulated pulses is relatively high with the original input SNR of 10.6707dB. As the number of cumulative pulses increases, the input SNR of 1000 cumulative pulse signals can reach 23.6621 dB, and the smoothness decreases significantly. In the denoising experiments, despite all algorithms filtering out noise to a certain extent, there is variability in the results of different denoising methods under different input signal-to-noise ratio conditions. The effect of WT denoising method is not sufficiently thorough, there still remain large signal fluctuations in the remote distance after denoising. However, when the number of cumulative pulses increases, the output SNR of WT increases greatly. Under the condition of high noise, decomposing the signal with EMD and retaining the low-frequency components allow for maximum noise filtering, resulting in better denoising. Retaining only the low-frequency components ensures the smoothness of the signal so that the EMD maintains a good advantage in pollution-free atmospheres with different SNRs. The denoising capability of VMD is relatively stable and does not change significantly with different input SNRs, while the VMD-processed signal deviates more from raw signal at the near-end points. The WT-EMD joint algorithm improves the denoising effect of WT, but it does not perform as well as the EMD algorithm at high noise levels. In all three groups of experiments, WT-VMD has the best denoising effect which can reduce the smoothness of the denoised curve to below 1. In the case of 50 cumulative pulse signals, the signal denoised by WT-VMD can improve the output SNR to near 2.5 times the original SNR. From The ultimate goal of lidar signal denoising is to retrieve the real distribution of the atmosphere better, so it is necessary to further verify whether the denoised signal can improve the accuracy of subsequent inversion. The Fernald method ,31 is usTo demonstrate the capability of these methods can be applied to wide conditions, this paper also simulates signals with realistic aerosol (0–2.5 km) and cloud distributions (9.5–11.5 km). The lidar echo signal with aerosol layer and clouds is not a simple smooth curve. These denoising methods should be tested that if they can retain the information of aerosol and cloud while denoising. Under this simulated condition, three groups of denoising experiments with different cumulative pulse numbers are also carried out. The results show in WT can retain more signal characteristics, especially good expression of the aerosol layer structure. However, also because of this, WT retains more noise resulting in a poor denoising effect under the condition of low SNR. EMD method pursues high smoothness of curve, which will weaken signal properties such as aerosol layer leading to large deviation from the original signal. It is apparent that this situation is more likely to happen when the signal has a low content of noise. For example, the aerosol layer of the signal after EMD denoising has a deviation in the experiment of 1000 cumulative pulse signals. Signals processed by VMD also retain part of the fluctuation with larger smoothness, but better than that after WT denoising. In the experiment of 50 cumulative pulse signal denoising, the VMD method still has the problem of endpoint deviation. Similar to the experimental results of denoising in the case of no pollution, when the cumulative pulse signal is 50 or 100 times, the results of WT-EMD are slightly better than that of the single WT method. When the cumulative pulse number reaches 1000 times, the RMSE of the WT-EMD combined method increase. Because the WT method performs well under high SNR and can filter out a large amount of noise while EMD is prone to modal aliasing under high SNR conditions. Therefore, the combined algorithm (using EMD secondary denoising again on the basis of WT) may perform worse. The WT-VMD method is still the best in the denoising experiment for the lidar noise signal with aerosol and clouds. Compared with the original signal, the results not only ensure good smoothness but also show the aerosol and cloud structure well. In addition, the WT-VMD joint algorithm improves the endpoint problem of a single method VMD. It is noted that we can see that the SNR is increased by 83.3%, 60.4%, and 24.0%, and the RMSE is decreased by 70.8%, 66.0%, and 50.5% when the proposed method is used for the three kinds of cumulative signal with aerosol and clouds from The results of extinction coefficient before and after signal denoising under cloud and aerosol conditions are also compared in It is necessary to verify the performance of the WT-VMD method by experimenting on the actual lidar echo signal, which is complicated by various interferences. As the altitude increases, the power of the lidar signal attenuates with the square of the distance. In general, the range-square corrected signal is obtained to eliminate the distance’s influence on the echo signal by multiplying the square of the distance by the echo power. The WT-VMD method is used to filter the signals’ noise when selecting the rang-square corrected signals detected by lidar at two different times in Hefei. As shown in To intuitively illustrate the influence of noise on the result of signal inversion, selecting the lidar signal data, which is averaged using 100 cumulative pulses for each profile within 10 s and its sampling rate is 40 MHz, measured in Heifei from 18:00 to 23:00 on 30 December 2020. The range-squared corrected signals were inverted by the Fernald method after the WT-VMD denoising. Extinction coefficient results of lidar signal inversion before and after denoising are shown in In daily lidar signal processing, time averaging is often used as an effective denoising method. The longer the average time, the higher the SNR. In order to compare the difference between WT-VMD method and time averaging method, the one-hour lidar signal measured in Heifei from 15:00 to 16:00 on 8 September 2021 is selected as experimental data.The one-hour lidar signal is cumulative averaged and compared with the 5 min signal denoised by WT-VMD. The Median filtering is a signal processing method based on sorting theory, which can suppress impulse noise well. The main principle is to replace a point in the number sequence with the median value of other neighboring data points, so that the value of the point is closer to the real value. This method can eliminate isolated data points and protect the edge of signal from being blurred. It is also commonly used in practical signal processing. A lidar signal profile measured in Hefei at 15:10 on 8 September 2021 is selected as experimental data. When the number of cumulative pulses is large, that is, the SNR of the original signal is high, all kinds of denoising methods can effectively remove most of the noise information. However, when the cumulative pulse decreases, that is, the SNR decreases, and the difference of noise reduction ability among different methods increases.From the perspective of denoising evaluation indicators, the related methods of VMD are better than the related methods of EMD in terms of SNR, RMSE or smoothness. Among them, the proposed WT-VMD method has absolute advantages, and it has the best noise reduction performance under different sizes of original SNR, and its advantages are more obvious when the noise is more.The effect of WT is the worst. The overall wavelet threshold denoising can only weaken the protrusion of the curve, but there is still a lot of noise information left, and the denoising is not complete. Additionally, with the increase in noise, the shortage of the WT method in filtering noise is becoming more and more prominent. Although the smoothness of the curve obtained by the EMD method is small, the endpoint effect may occur when the SNR is low, that is, the signal endpoint offset is large, causing signal distortion, and modal aliasing is easy to occur when the SNR is high.As to the joint denoising, the filtering ability of WT-EMD and WT-VMD has been significantly improved. However, not all joint denoising methods are significantly better than the single denoising method. Since there are still useful signals hidden in high-frequency components after EMD/VMD decomposition, WT processing for low-frequency components with less noise information cannot achieve the purpose of secondary effective denoising. On the contrary, if WT is used to filter most of the noise, then EMD/VMD decomposition. At this time, the noise content in the high-frequency component is higher. After discarding the high-frequency component, the SNR can be improved more.In order to find the most suitable denoising method for lidar signals, this study employs the joint algorithms WT-EMD and WT-VMD based on the single denoising algorithm WT, EMD, and VMD. The comparative experiment of denoising is carried out by using a simulation lidar signal. The simulated signal with noise is divided into two types, one is under the condition of no pollution, and the other is with aerosol and cloud distribution. Three kinds of experiments were conducted with 50, 100, and 1000 cumulative pulses, respectively, corresponding to different input SNR. Three indicators, SNR, RMSE, and smoothness, are used to evaluate the noise reduction effect of the algorithm.The experimental results show the difference in denoising ability under different denoising methods. WT method is more suitable for the condition of high SNR and can restore the signal structure well, but the signal after denoising still has a lot of fluctuations in the far distance and the smoothness of the curve is poor. EMD method performs well in pollution-free weather, but it will lose the details of atmospheric distribution for aerosol and clouds. VMD method has stable denoising capability under different noise levels. Its overall effect is better than WT, but there may exist an endpoint problem. Joint algorithms are usually superior to single algorithms. WT denoising can filter out most of the noise while retaining the signal characteristics, and then EMD/VMD can further remove the remaining noise in detail. However, mode aliasing may occur in EMD at high SNR, so WT-EMD sometimes leads to signal distortion. The result indicates that the WT-VMD method has the best noise reduction effect. Regardless of the noise level, the signal processed by it has the minimum RMSE, the maximum SNR, and a relatively small indicator of smoothness. Then, the denoised signal by WT-VMD based on SSA can display the information of the original aerosol and cloud well and reduce the relative error of the extinction coefficient obviously.Applying the proposed method to the actual lidar data can effectively reduce the signal’s noise with the increasing detection range and enhance the accuracy of subsequent aerosol extinction coefficient inversion. Compared with the common averaging method and the median filtering method, the atmospheric inversion results obtained by WT-VMD denoising are also better. The experimental results demonstrated that the WT-VMD method based on SSA could be well used in practical lidar signal processing. In the next step, the algorithm will be further optimized to improve the operation speed, so as to play a greater role in the actual lidar atmospheric detection.


Neurodegenerative diseases such as Parkinson’s disease affect motor symptoms with abnormally increased or reduced movements. Symptoms such as tremor and hand movement disorders can be subtle and vary daily such that the actual condition of the disease may not fully present in clinical sessions. Health examination and monitoring, if available in the living space, can capture comprehensive and quantitative information about a patient’s motor symptoms, allowing physicians to make a precise diagnosis and devise a more personalized treatment. WiFi-based sensing is a potential solution for passively detecting human motion in a contactless way that collects no personally identifiable information. This study proposed an approach for human micromotion detection using the WiFi channel state information, which can be realized in a regular-sized room for home health monitoring and examination. Three types of motion were tested to evaluate the proposed method in quantifying micromotion using single and multiple WiFi links. The results show that micromotion could be captured at all distributed locations in the experimental environment (4.2 m × 7.9 m). Our computer algorithm computed the frequency and duration of simulated hand tremors with an average accuracy of 90.9% (single WiFi link)—95.7% (multiple WiFi links). In the last two decades, wireless technology has achieved significant success in data communication. Currently, WiFi connections exist in every houses due to their low cost and easy-to-set-up features, which also make wireless sensing using commodity WiFi devices a popular research field. In the past few years, human motion detection technologies using WiFi have been developed for indoor applications, such as localization/tracking , walkingNeurodegenerative diseases such as Parkinson’s disease (PD) are a progressive disease where patients’ motor symptoms develop slowly. As the global population ages, the prevalence of diseases is rapidly increasing. Unfortunately, there is no cure for the disease, and patients can only relieve symptoms and get back into the rhythm of life by taking drugs. Precise medication is critical to disease management but requires a comprehensive evaluation of the patient’s condition. However, assessing the motor functions of patients relies on the physician’s experience and subjective judgment; still, the disease’s actual condition may not be fully present during clinical examination due to the motor fluctuation or symptom variation in a day. Therefore, health monitoring is needed to detect motor symptoms and provide quantitative data.At present, research on quantifying motor symptoms of PD mainly uses wearable devices and computer-vision-based methods. Sensing motion via wearable devices ,7,8 has The main contribution of this paper is the realization and evaluation of the WiFi-based detection of micromotion related to PD symptoms. Abnormal gait, bradykinesia, and hand-resting tremor are typical symptoms of PD . As studMicromotion detection for health monitoring is a popular research topic. Existing technologies are developed based on wearable- and smart-device-based computer vision algorithms and wireless signal-based methods.® Balance Board [Wearable devices such as accelerometers ,19, gyroce Board , iPhone ce Board , and smace Board  because ce Board , particuWith the advancement of image processing techniques, quantifying motor symptoms based on computer vision has become increasingly popular ,25,26. MNon-contact wireless sensor networks include ultrasound , infrareThe CSI tool for 802.11n NIC Intel 5300 was released in 2010 . From thEstablishing a relationship between movements and CSI is the key to motion quantification. In the early development of CSI-related research, pattern-based sensing methods  were mosThis paper proposes a WiFi-based method that utilizes the CSI and Fresnel zone theory to quantify the most typical hand motor symptoms in PD, including resting tremor and finger tapping, in Parkinson’s disease. The algorithm processes raw CSI data and eliminates environmental interference to the signal, allowing the subtle motion to be detected throughout the experimental environment. We also designed experiments in a room with WiFi signal coverage to demonstrate the symmetric properties of single and multiple Fresnel zones, which were used to derive the contour of the detection accuracy. Finally, both settings of single and multiple WiFi links were evaluated to further our understanding of the WiFi CSI sensing ability. The strategy for using multiple WiFi links in a larger room was also discussed, moving the technology closer to a realistic application.In this study, the physical layer CSI was utilized as a primary indicator for human motion. When a WiFi signal propagates along multiple paths in an environment, a moving target would affect a fraction of propagation paths and be depicted by CSI variations. We considered symptoms related to hand movement as target motion and hypothesized that such movement would cause temporal variations in CSI, which can be collected from commodity WiFi devices.i-th subcarrier and i-th subcarrier.To describe the basic model of CSI , the wirThe proposed method detects the variations in CSI amplitude, phase, and phase difference to quantify the target motion. The method was evaluated by four scenarios for the basic signal selection, directional effect of motion, and sensing accuracy.We set up WiFi devices to collect CSI data when target motion was performed. The experimental settings and WiFi device configurations are explained in P of amplitude, phase, or phase difference, we performed Z-score normalization to obtain P, respectively.Firstly, given an input raw CSI sequence Equation , where PA can be obtained (Equation (We then used a third-order Butterworth filter  to remov of 6 Hz  were useEquation ).(4)G  to find E represents energy of the input data series . A sample result of energy calculation is illustrated in Thirdly, the short-time energy threshold segmentation  was usedj is the index number of the input and output data sequences (Q), and the convolution coefficients, m.Finally, a third-order Savitzky–Golay filter  with a wAn input CSI sequence contained 20-s data in 180 subcarriers with a sampling rate of 1000 Hz  and the minimum peak prominence as 0.3 max(Q). These empirically determined parameters helped to improve the peak finding accuracy by 3–5% compared to the cases that only used the minimum separation. With all criteria, although there were still true/fake peaks identified incorrectly, the error rate of motion quantification was within 7% compared to the reference data  and evaluated the sensing performance  was simultaneously used to capture reference data at a sampling rate of 200 Hz. The sensor was attached to the free end of the steel ruler to detect the vibration. For resting tremor, the person who performed the task wore the sensor on the wrist. The sensor was not used in the finger-tapping test as the person already followed the metronome sound at 1 Hz. The reference data served as the basis of comparison for evaluating the accuracy of our WiFi-based sensing in terms of the frequency and duration of motion. A sample reference data collected from a resting tremor test is shown in q, and that quantified using the sensor measurements, g. Therefore, the error percentage could be calculated by:N is the number of repetitions for a test item. The accuracy would be The absolute error was defined as the discrepancy between the frequency/duration of motion quantified using the CSI data, All basic signals, including amplitude, phase, and phase difference, clearly captured the effects of steel ruler vibration, finger tapping, and resting tremor . These rThe amplitude is a directly extracted measure of CSI, which is the most used basic signal. The CSI amplitude represents the decay of the signal strength caused by the multipath effect. The CSI phase also records changes in the wireless signal to reflect activities that occur in the environment. However, issues such as the carrier frequency offset and sampling carrier frequency offset occurred because of force majeure . The CSIUsing the variation in the CSI amplitude, the directional effect of motion was investigated by comparing the sensing performance in the five experimental settings shown in Among the 34 experimental points in the test environment , the aveA filled contour of the sensing accuracy for the frequency of motion is shown in a) yielded at the 34 experimental points and their distance to the origin in the Y direction (a decreases as The effect of distance between the target motion and the origin on the sensing accuracy was much more clearly observed in the Y direction than in the X direction. Equation ) using tFor the duration of motion, the highest sensing accuracy was 99.0% (Point 28), and the lowest was 92.7% (Point 14), with an average of 95.2% and standard deviation of 1.75%. In this setup, a Tx and three Rxs at the four corners of the room formed three WiFi links. Compared to the single WiFi link, the average sensing accuracy for the frequency of motion increased from 90.2% to 91.4% (standard deviation: 0.93%). The highest accuracy was 93.2% at Point 6, and the lowest was 89.3% at Point 4. For the duration of motion, the highest sensing accuracy was 97.0% at Point 11, the lowest was 93.5% at Point 13, and the average was 95.7%, with a standard deviation of 0.86%. The filled accuracy contours are shown in For all three types of micromotion considered in this study, the proposed method accurately detected their frequency and duration of motion. However, the comparison with the reference indicated some limitations and errors.Theoretically, the magnitude of the vibration of the steel ruler decreases gradually from the initial 10 cm deflection at the free end. However, the variation in the CSI amplitude in response to the vibration of the steel ruler did not represent the decay trend. By analyzing the waveform changes in CSI, we detected the frequency and duration of the vibration accurately, demonstrating the ability to quantify the motion on a centimeter scale. However, it is challenging to determine the amplitude of the vibration. Wireless signals are subject to complex multipath effects during their propagation. Even with the same type of movement and amplitude, the signals cannot always reach the Rx with the same intensity.In the finger-tapping tests, the number of taps was accurately counted when the direction of motion was perpendicular to the LoS. However, when the direction of motion was parallel to the LoS, we could not obtain the number of taps, but could roughly distinguish between the active and non-active states. This is because, according to the Fresnel zone theory, only movements along the direction normal to the Fresnel zone affect the reflection path, whereas those in the tangential direction would have no influence . InteresErrors were also caused by packet loss, the slow response of the wireless signal, and the multipath effect. Packet loss commonly occurs during the transmission of wireless signals, and is unavoidable. When packet loss occurred in our tests, the Rx still recorded CSI at the specified rate  and only logged the same value as the previous data point, leading to a horizontal line shown in the signal plot . In the A slow response indicates that the wireless signal does not immediately respond to the impact of the motion at its start and end. As shown in The input CSI sequence was controlled in the same length (20 s) for all scenarios used to evaluate the proposed approach. Since the input CSI sequence length was short and fixed, the data processing algorithm read it all at once to segment the target motion signal without concerning the computation performance. However, in a real situation, target motion can occur at any point in the received CSI data streams, which requires a method to identify data frames in the CSI stream that contain motion signals. Future works should develop a motion recognition method to identify target motion occurring at a random time point and with an arbitrary time length. The block-processing method would be more feasible and efficient for such an implementation than the sample-processing method. For real-time applications, the delay due to block processing should be considered.Overall, the accuracy of estimating the motion duration was much higher than that of determining the motion frequency, and the standard deviation of the former was lower than that of the latter. This can be explained by the error sources. As shown in The frequency spectrum of the CSI data could contain useful information to help improve the sensing performance. A fast Fourier transform of the resting tremor motion g indicatA past study realized a system that can detect subtle motion, i.e., human respiration, in a range of distances . InsteadThis study showed that different types of subtle hand motion, i.e., finger tapping and resting tremor, could be accurately quantified, and their features of motion could be captured in the CSI signal variation. After the data processing steps, the known range of the target motion frequency could be used to calculate the minimum separation, which was combined with the empirically determined minimum peak height and prominence to accurately quantify the motion. The literature may have provided sufficient information to make this scenario applicable to on-field detection for motor evaluation. Features of different types of tremors  have been studied . FeatureThe sensing accuracy decreased linearly when the motion moved away from the LoS in the Y-direction. In the X-direction, the accuracy was much less sensitive to the change in the distance to the LoS. Wireless signals fade during propagation owing to environmental effects, phase interference, and phase time variability between signals. When the resting tremor occurred farther away from the LoS, the received data were more disturbed by the signal reflection. If environmental noise gradually dominates the signal presentation, the effect of tremor motion becomes trivial, increasing the quantification error.When three Rxs were paired with a Tx and arranged at the four corners of the room, three Fresnel zones were formed. All experimental points were covered by at least one Fresnel zone with good signal reception; thus, the average sensing accuracy was higher than when using a single Rx. However, single-Rx detection reached a higher maximum accuracy than multi-Rx detection. This can be attributed to the application of PCA in the data processing. For multi-Rx detection, the data of all 180 subcarriers from three Rxs were processed together through PCA, resulting in an averaged performance of the final signal. In the future, a subcarrier selection process should be added before PCA to eliminate unnecessary data interfering with the performance of PCA. Furthermore, the errors found at the six validation points were all within 2.5%, confirming the symmetric property of the sensing accuracy contour.This study investigated two common Tx-Rx arrangements, and the sensing performance could be understood from the accuracy contours . Based oSignal disturbances are commonly found in healthcare environments. A comprehensive evaluation is needed in the future to move the proposed approach closer to a real-life application. A preliminary study was conducted to investigate the impact of static and dynamic objects on the sensing performance. We placed an office partition wall constructed from an aluminum frame and wood as a static barrier to block the LoS. Resting tremor tests were performed with and without the barrier. To simulate dynamic disturbance, a person was asked to walk back and forth near (approximately the same distance as the target motion from the LoS but on the opposite side of LoS) and far (3 m) away from the LoS. Another person performed resting tremor tests with and without dynamic disturbance. The results showed that the sensing accuracy remained the same for the duration of motion but reduced from 91.7% to 72.3% for the frequency of motion under the presence of the static barrier. When the dynamic disturbance was introduced, human walking near the LoS dominated the signal response, making the target motion not quantifiable; walking on the far side of the LoS had a significantly reduced effect, so the sensing accuracy was around the same for the duration of motion and reduced by 3.1% for the estimation of motion frequency compared to the case without disturbance. In summary, the biggest challenge to overcome is when a large motion occurs nearby the LoS to shield the target motion, which is currently an open issue. For health monitoring applications, an alternative solution might be to first recognize target motion from the CSI data and discard the unrecognizable data segments. With an accurate recognition algorithm, the monitoring system should still be able to collect a significant amount of reliable data for quantification. As an assessment tool, clear instructions can be given to set up the environment to properly avoid disturbance.This paper demonstrates a WiFi-based approach that enables the contactless detection of micromotion related to hand movement disorders of PD. This approach accurately quantifies the frequency and duration of the hand resting tremor and finger tapping, which are critical indicators of the disease. Moreover, we confirmed that the micromotion could be detected in a wide space by showing experimentally verified contours of sensing accuracy for the arrangements of single and multiple links.The proposed approach can be implemented as a clinical assessment tool to detect the patient’s movements simultaneously when the doctor conducts a regular motor evaluation. The doctor can review and compare the quantified movements with their evaluation. The proposed approach could also be further developed for telemedicine applications to monitor a patient’s movements in daily life and track drug efficacy by providing quantitative data on symptoms. Other than clinical applications, gesture recognition could be benefited by analyzing the subtle features in the quantified hand and finger movements; after some parameter tuning, other types of subtle motion, such as eating or breathing, could be detected. WiFi-based motion detection is a device-free and passive sensing method that allows for capturing long-term data under the patient’s natural condition to provide doctors with a comprehensive insight into the disease status, leading to a more timely and personalized treatment that will improve the quality of life and slow the disease progression in a patient.