These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy.Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g.,  Intraburst neural signatures were first described for the neurons of the pyloric central pattern generator (CPG) of the lobster stomatogastric nervous system . This pattern corresponds to the spontaneous activity of the neuron. Note that in this situation, the neuron keeps silent with probability 1 − ep.If no fingerprint is recognized in a time step, the neuron emits a predefined serial binary pattern with probability After emission, neurons have a refractory period of 10 time units during which neither emission nor recognition are made.Neurons communicate by exchange of serial binary patterns. The information processing in each individual unit is oriented to determine the sequence of bits to be emitted in the following time steps in the output channels. The response pattern is calculated as a function of the detection of specific incoming stimuli. Each individual neuron has the ability to recognize a predefined set of serial binary patterns. These patterns constitute the set of neural fingerprints recognized by the neuron. In response to a recognition, the cell emits a specific output pattern in a probabilistic way. For that, neurons use rp and ep are subcellular parameters that control, accordingly, the permeability of the neuron to external stimuli and its level of spontaneous activity.Probabilities Many biological neural networks present structural characteristics that coincide with a small-world topology , p = 0.1, p = 0.25, and p = 1 (random networks). Hereafter, we accordingly identify these topologies with the labels “Regular,” “SW(10),” “SW(25),” and “Random.”In this work, we build small-world networks of 2500 neurons where each unit is connected to eight other neurons. To build these networks, we start with a regular two-dimensional (50 × 50) grid with periodic boundary conditions where each unit is connected to its eight nearest neighbors. Then, each connection with a neighbor is broken with probability ep regardless the network topology  is introduced as external input in a randomly chosen neuron. In our first analysis, this neural fingerprint is the only pattern that neurons are able to recognized.ep and rp. In the case of external stimuli of 5 bits, when rp is lower than 0.30 or ep is too high , the greater the number of neurons that follow the incoming stimulus. For the same network topology, the activity evoked by this stimulus can even be increased more than 100% by simply modifying the value of rp. For instance, in the regular network case, the mean number of neurons following the stimulus in simulations with rp = 0.5 and rp = 0.8 is around 275 (≈ 11% of the neurons in the network) and 532 (≈ 21% of the neurons), respectively. As we have already shown above, unlike the spontaneous intrinsic activity, the activity evoked by stimuli also depends on the network topology. The higher the number of random connections, the larger the detected fingerprint emission level —i.e., network randomness has a similar effect to increasing the permeability of individual neurons to external stimuli  and/or they are injected in a greater number of neurons (from 2 to 30), results are equivalent to the ones discussed here, but taking into account the following. On one hand, increasing stimulus lengths helps the spontaneous intrinsic activity to win the competition because longer stimuli require more time steps to be detected and, therefore, the probability to emit the spontaneous binary pattern increases. And, on the other hand, a greater amount of stimulated neurons helps the detection process since the level of activity related to the external stimulus grows with the number of stimulation sources.In the previous section, we have studied the ability of the neural network to sustain external stimuli while they are active. However, an interesting feature of the network is its memory ability, i.e., the ability to sustain a detected pattern in the collective fingerprint-based dynamics beyond the time period of stimulation. In order to study this property, we have carried out simulations where the external stimulus is introduced in the network just for a while. In these simulations, we study how the stimulus induced activity survives in the network after the end of the stimulation. Taking into account the results described in Section 3.2, we focus on networks able to detect incoming stimuli. Here, we analyze the same simulations as in the previous section, but when at time step 15,000 the stimulation ends—i.e., the network initially evolves freely, then, at time step 5000, the stimulus  is introduced into a randomly chosen neuron during 10,000 steps, and, finally, no stimulation is present again . In long-term memory networks, the stimulation produces persistent changes in the network collective dynamics. This leads the network to a new stable state where the reverberation of the patterns associated with the external stimulus is not sustained by stimulation, but it is a network effect. In this way, external stimuli can win the competition and prevail over the spontaneous intrinsic activity .The level of activity analysis indicates that the network may sustain external stimuli transiently—i.e., the stimulus induced activity reverberates just for a while and then the corresponding binary pattern disappears from the network (short-term memory or working memory)—or persistently—i.e., after the stimulation, the activity evoked by the stimulus coexists with the spontaneous intrinsic activity in a permanent way (long-term memory). Top and bottom panels of Figure ep and rp and the network topology. To illustrate the relationship between these factors, a phase diagram locating the different behaviors in the space of intraunit parameters for networks with different topologies is shown in Figure rp for each value of ep that determines if the network behaves as a transient (below the threshold) or a persistent memory (above the threshold). Independently of the network topology, as the level of spontaneous intrinsic activity grows, a higher permeability to external stimuli is required to become a long-term memory. Regarding the network topology, random connections contribute to long-term memory mechanisms, unlike regular connections that help short-term memory mechanisms. In this manner, for example, when ep = 0.05, random enough (p > 0.80) small-world networks of 2500 units never behave as a short-term memory.The factors that determine if the neural network behaves as a short-term or a long-term memory are again related to the competition among the spontaneous intrinsic and the stimulus induced activity. As we have previously discussed in Section 3.2, the mode of competition varies as a function of the relation among the intraunit parameters The network can display two different modes of operation according to the evolution of the total level of activity in the network, i.e., the overall number of active neurons whatever the binary pattern they are emitting. In Section 3.1, we show that the spontaneous intrinsic activity in autonomous networks reaches a steady level. Similarly, the arrival of a stimulus makes the level of activity related to this external input also reaches a steady level. In these cases, a transition to a lower level of spontaneous activity is produced due to the spreading of the stimulus (see Section 3.2 for details). This lower level is also kept constant during the stimulation period. Nevertheless, the total level of activity in the network can be kept nearly constant over time, but also it can be increased due to stimulation  and in parallel (Section 3.5.2). The arrival of multiple stimuli simultaneously or close in time induces the generation of different coexisting patterns of activity within the network. In this situation, a competition between the spatio-temporal patterns encoding the different stimuli arises. In simulations with in-series stimulation, we study the competition between reverberating patterns encoding a previously received stimulus and patterns supported by an active stimulus. This allows us to assess the ability of the network to retain information regarding recent past stimuli  when a new stimulus arrives. Meanwhile, in-parallel stimulation allows studying the ability of the network to sustain several simultaneous stimuli.Short-term memory networks with a low level of activity. These networks generate propagating well-defined spatio-temporal patterns to encode incoming stimuli , stimuli induced activity (Section 3.2), type of memory (Section 3.3), and total activity in the network (Section 3.4). This allows us to compare networks with different topologies under the same conditions. We discuss here two particular cases of equivalent networks:In the following sections, we discuss equivalent networks of 2500 neurons with the intraunit parameters shown in Table In simulations where the stimuli are in series introduced in the network, we alternate stimulation episodes in which two different external stimuli are injected in the same group of neurons. In the simulations presented here, initially, the network evolves freely. At time step 5000, the stimulation begins. In each stimulation episode, a single stimulus arrives to the network during 10,000 time units. Then, there is an inter-stimulation interval of 5000 time steps where no stimulus is introduced. This sequence is repeated eight times alternating two different stimuli (A and B), which are the two binary patterns recognize by the neurons in the network. Finally, at time step 120,000 no stimulus is introduced any longer. Results are independent of the stimulation patterns and of the location of the neurons that receive the external stimuli.Initially, without any stimulus, the activity of the network evolves to a stationary state where only spontaneous intrinsic activity is present in the network regardless its topology.The total activity in the network can be kept constant or be increased due to stimulation depending on the intraunit parameters and the network topology.Stimulation evokes collective dynamics in which the spontaneous intrinsic activity competes with the activity induced by external stimuli. Due to this competition, the level of spontaneous intrinsic activity drops.When the stimulation is over, reverberating patterns encoding external stimuli can survive within the network transiently or persistently.Equivalent results to the ones described in the previous sections are also produced when the network receives two stimuli in series . As we demonstrate in Section 3.2, the presence of random connections increases the level of activity induced by an external stimulus and the corresponding propagation velocity. When two stimuli are in series introduced into a random enough long-term memory network  network), this results in a relatively fast (as compared with networks with more regular topologies) winner-take-all competition where the activity supported by the active stimulus in each stimulation episode prevails. Thus, in networks with prevailing random connections, the last received stimulus always wins the competition and, therefore, the preferred pattern reverberating in the network starts following this stimulus . Therefore, these networks have the ability to behave as a long-term memory simultaneously encoding more than one stimulus.However, in the more regular networks, the mode of competition among external stimuli is not always a winner-take-all regime. As illustrated in the stimulation episodes pointed out by the arrows in panels A, B of Figure The activity movies illustrating the evolving network dynamics give additional insight about the results derived from the level of activity analysis. When a new stimulation episode starts, the new stimulus induces the coexistence of different spatio-temporal patterns with different spatial organization. Figure In the simulations where multiple external stimuli arrive in parallel to the network, we consider a single stimulation episode. During this time period, all the external stimuli considered in the experiment are simultaneously introduced in different groups of neurons. The set of neural fingerprints recognized by the neurons varies in the different experiments, but always consists of the serial binary patterns used as external stimuli during the corresponding stimulation period. Results are independent of the stimulation patterns and of the location of the neurons that receive the external stimuli.When stimuli are applied, they start to spread through the network and, again, new collective dynamics emerge in the network Figures , 11. If Finally, results observed in short-term memory networks when the stimulation is over are equivalent to those obtained during in-series stimulation. In long-term memory networks, a winner-take-all competition usually occurs and the reverberating patterns follow one of the external stimuli Figure . NeverthThe idea that neural systems can encode information in the precise timing of spikes has attracted increasing attention over the last years. The presence of precise firing patterns in different neurons has been reported in several vertebrate and invertebrate neural circuits . However, changes in the collective dynamics are also observed as a function of the network topology. The presence of random connections has a similar effect to increase the permeability to external stimuli. These connections facilitate the detection of specific external stimulus in the sense that they increase the stimulus propagation velocity and the level of activity related to stimuli. Networks with a more regular topology (connections with close neighbors) usually benefit the competition between external stimuli. This implies that these networks are able to encode a larger number of stimuli simultaneously.This work presents a simulation study showing that a simple neural network model composed of neurons that are able to emit and recognize neural fingerprints can detect specific patterns in its input signals and encode these stimuli in its collective dynamics. The processing and encoding ability of the network is related to the competition established between the spontaneous intrinsic activity and/or the activity evoked by the arrival of external stimuli. This competition may change from winner-take-all to winnerless regimes. When the level of spontaneous intrinsic activity is too high, this wins the competition and external stimuli do not propagate through the network, which avoids the generation of spatio-temporal patterns. This provides a “reset” mechanism to the network. Only by increasing the level of spontaneous intrinsic activity, the network stops processing and the previously detected stimuli almost instantaneously disappear from the network. The coexistence of stimuli in the network is related to the different clusterization and coherence of the transient spatio-temporal activity generated in response to external input. When a winner-take-all competition occurs between the spatio-temporal patterns of activity evoked by different stimuli, one stimulus prevails over the others and only its corresponding patterns travel through the network . On the other hand, when a winnerless competition emerges in the network, the total encoding capacity of the network is alternately distributed among different stimuli. The main factors governing the mode of competition are the level of spontaneous activity and the permeability to external stimuli. Both are driven by intraunit parameters (When the stimulation is over, the same competition regimes emerge between the coexisting spatio-temporal patterns following external stimuli. The only difference is that, now, these patterns are not supported by stimulation and, therefore, when a stimulus looses the competition with other stimuli or with the spontaneous intrinsic activity, it completely disappears from the network. In this way, the patterns of activity induced by external stimuli can almost instantaneously disappear from the network when the stimulation is over. This can be a desirable behavior for some systems. However, the more interesting situations are those where the network displays short-term or long-term memory abilities. In short-term memory networks the stimulus induced activity transiently reverberate within the network. Conversely, in long-term memory networks, the stimuli lead the network to a new stable state where the reverberating patterns related to stimuli becomes a network effect. Then, the stimulus persistently survives in the network until a new stimulus arrives. A short-term memory can become a long-term memory only by increasing the neurons' permeability to external stimuli, and vice versa. In the same way, increasing the number of connections to close neighbors benefits short-term memory mechanisms; while increasing the presence of random connections potentiates the long-term memory mechanisms.The results reported in this paper indicate that information processing based on the identification of specific neural fingerprints can be a plausible and flexible strategy for neural systems. Different complex dynamic regimes are observed in a simple network of neurons that communicate by exchange of neural fingerprints. Intraunit parameters affecting the individual information processing and the network topology can tune the self-organizing properties of the network. This indicates the great adaptability of the network to different modes of operation and, although not addressed in this paper, the large flexibility to implement both synaptic and subcellular plasticity Davis, . These rThe 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.


Secure data transfer against the malicious attacks is an important issue in an infrastructure-less independent network called mobile ad-hoc network (MANET). Trust assurance between MANET nodes is the key parameter in the high-security provision under dynamic topology variations and open wireless constraints. But, the malicious behavior of nodes reduces the trust level of the nodes that leads to an insecure data delivery. The increase in malicious attacks causes the excessive energy consumption that leads to a reduction of network lifetime. The lack of positional information update of the nodes in ad-hoc on-demand vector (AODV) protocol during the connection establishment offers less trust level between the nodes. Hence, the trust rate computation using energy and mobility models and its update are the essential tasks for secure data delivery. This paper proposes a trust-aware ad-hoc routing (T2AR) protocol to improve the trust level between the nodes in MANET. The proposed method modifies the traditional AODV routing protocol with the constraints of trust rate, energy, mobility based malicious behavior prediction. The packet sequence ID matching from the log reports of neighbor nodes determine the trust rate that avoids the malicious report generation. Besides, the direct and indirect trust observation schemes utilization increases the trust level. Besides, the received signal strength indicator utilization determines the trusted node is within the communication range or not. The comparative analysis between the proposed T2AR with the existing methods such as TRUNCMAN, RBT, GR, FBR and DICOTIDS regarding the average end-to-end delay, throughput, false positives, packet delivery ratio shows the effectiveness of T2AR in the secure MANET environment design. Mobile ad hoc network (MANET), a dynamic configured and infrastructure less architecture that contains operating nodes responsible for data transfer. The absence of centralized controller to determine the routing path initiates the cooperation mechanism among the nodes for data packets transfer. Generally, the nodes in the MANET are responsible for packet transfer and their join or leave the network without any constraints leads to an unpredictable MANET structure. Moreover, the absence of centralized administration in MANET requires nodes cooperation with the assumption of genuine and trustworthiness. Practically, these assumptions are violated due to the user misbehavior and the denial of service (DoS) attacks generation. Besides, the arrival of malicious attacks degrade the network lifetime and disrupt the data delivery that leads to an evolution of security provision schemes in the research studies. The lack of prior information about the other nodes in cooperation mechanism leads to a sharing of resources to both trusted and non-trusted nodes. Hence, there is a need of formalization of trustworthiness to assure the resource sharing between the trusted nodes only. The dynamic and unpredictable nature of MANET causes the vulnerability of several attacks that lead to less security. Trust is an important aspect of MANET to create the secure MANET environment. The evolution of trust management protocols to provide/enhance the trust level creates a new era in the research studies.The security enhancement employs the trust management protocols which stimulate the cooperation behavior in two phases namely, prevention and detection based approaches. Prevention-based approaches require the centralized control that is not suitable for distributed environment. The evolution of detection approaches based on trust management protocols do not consider the malicious behavior observed from direct and indirect schemes. The trust updating and reputation protocols implementation are the active research area to suppress the effect of malicious nodes. The major factors limit the operation of nodes are power, computing ability, and battery and such affected nodes refer selfish nodes. Hence, the resource preservation is a pre-requisite to handle the selfish node operation. The mobility and relocation of selfish nodes in MANET have the great impact on malicious node avoidance. The intruder acquires the information regarding the dynamic changes due to relocation process in the routing path to provide the effective data delivery. Hence, the trust management requires an immediate attention in diverse and crucial MANET applications. But, the introduction of malicious behavior during the communication disrupt the performance of protocol performance. The provision of false information by the nodes and the opinion-based trust estimation disrupt the data delivery adversely in traditional direct and indirect observation schemes.To overcome these problems, an energy model-based trust evaluation scheme is presented in this paper. The neighbor log collection and route maintenance based on log reports constitute the energy models with stable connections. The traditional ad-hoc on demand vector (AODV) routing protocol performs the sequential update for neighbor log collection. But, the connection establishment during the movement of nodes is not an effective due to the lack of positional update. Hence, an extension trust-aware ad-hoc routing protocol (T2AR) is proposed in this paper. The novelty lies in T2AR is that the use of direct and indirect trust observation schemes on neighbor log results and trust assurance via sequence ID matching.Neighbor sensing and route maintenance are based on the log collection from the nodes that provide the necessary trust rate value. Besides, the trust value is periodically updated through the locational information to enhance the security level of nodes present in MANET.The utilization of direct/indirect observation schemes followed by the sequence ID matching in proposed work increase the trust level.The use of received signal strength indicator (RSSI) in distance estimation effectively predicts whether the trusted node within the communication range or not.The log collection-based trust computation, observation schemes and the RSSI estimations in the proposed work increases the packet delivery ratio (PDR), throughput with less delay and false positives compared to the traditional trust-based protocols.The technical contributions of this paper are listed as follows:The rest of the paper is systematized as follows: “This section discusses the influence of trust management protocols on secure data delivery in MANET. The lack of centralized control unit in MANET initiates the cooperation mechanism (nodes rely on the other nodes) to forward the packets. The difficulties in cooperation mechanism made the trust-management as the complex task. Cho et al.  highlighThe rapid increase in wireless application size maximized the attack densities in MANET. Hence, the suitable plan was required to analyze the sophisticated attack behavior such as insidious and random attacks. The optimized link state routing (OLSR) protocol was suitable in large density MANET with a number of mobile devices interconnection. Tan et al.  proposedShabut et al.  added thAyday and Fekri  developeDue to the less utilization of third party information in the decentralized control architecture of MANET, the information about the trust is obtained from the peers. Banerjee et al.  proposedThis section explains about the proposed trust management scheme implementation in detail. The proposed method is mainly used to improve the trust of the nodes in MANET. The following sections describe the processes involved in trust management. The overall flow diagram of the proposed approach is shown in Fig. Then, the node with maximum trust value is chosen for packet transfer. With these estimations, the discovered route is reliable, secure and should possess high trust value. Moreover, the distance estimation prior to trust value computation using RSSI assures the selected trust node is within the communication range. The mobile nodes in the MANET have the ability to move in any direction and act as both routers and hosts. Since there is no particular infrastructure, data can be sent from any node to another. The node which sends the packet or data is called as the source node and the node which receives it is called as the destination node. If the trust value of the nodes is found to be high, then transmission of data between them is reliable. In our approach, the trust value is calculated by combining direct and indirect observations. A source node is selected and the neighbor node is extracted by using the RSSI based distance estimation. Then, the trust value of the node is updated based on energy model calculation, packet sequence ID matching rate and mobility estimation. A node having maximum trust value is selected as an intermediate node for packet transfer to the destination node. The proposed scheme is evaluated in terms of PDR, throughput, average delay, the number of false positives. Table Neighborhood estimation is an initial step to compute the trust value of the node. The RSSI-based distance estimation identifies the nodes nearer to the source node. The neighborhood nodes are calculated using the following algorithm:The trust value of the particular node is based on the energy, mobility and trust rate. Hence, the proposed work contains three phases namely trust rate computation, energy model, and mobility model.The nodes and the graph are provided as the input values to the neighbor log collection algorithm. Initially, the neighbor node list is constructed for the input node. Then, the information is collected from the log reports of all the nodes participated in the secure data transfer. From the log reports, the packet sequence ID of the particular node is extracted. The distance is estimated by using RSSI method  calculated is the ratio between the number of successful packet transmission and overall packet transmission [sum of a number of successful packet transmission (NPs) and a number of failed packet transmission (NPf)].Packet success rate (RSR) is the ratio of a number of successful packet transmission and overall reply packet transmission [sum of a number of successful reply packets (NRPs) and a number of failed reply packets (NRPf)].Reply success rate (R) is the ratio of successful request transmission to the overall request transmission [sum of a number of successful request (NRs) and a number of failed packet transmission (NRf)]. The network lifetime improvement depends on the amount of energy preserved and the mobility of the nodes that leads to the construction of energy and mobility models.Request success rate  will be updated by using following equationAfter selection of trusted node for packet transmission, selected node requires updating their energy level for further packet transmission. Overall energy  and and4) anFrom the computed trust value, the node with maximum trust value is chosen for data or packet transfer. Once the destination is reached, route discovery has to be done. Otherwise, again the neighbor estimation has to be done. This method examines the route between the source and the neighbor nodes in the network. If the distance between the source and neighbor node lies within the coverage area, then it is added to the NN list and collects the log reports for the computed neighbor node. The packet sequence ID from the log reports is extracted and compared with other nodes. If both the IDs are matched, then the trust rate is computed by using the request, reply, packet delivery rate and the computed trust rate is given as the input parameter to the trust update process.There may be malicious or misbehaving nodes in the MANET and so, trust between nodes is significant for packet transfer. The presence of malicious nodes causes the packet dropping and incorrect packet forwarding to the nodes in general. In this paper, the effect of malicious nodes under packet dropping is considered. The trust assurance via direct and indirect observational schemes is efficiently reduces the packet drops in proposed work.α > 0, β > 0 and the random variable 0 < θ < 1 follows the beta function as follows:In this scheme, an observer node can directly estimate the trust value by using the Bayesian framework with the assumption of an observer node overhear the forwarded packets and compared with the original packets to find the malicious behavior. The distribution function with shape parameters Bs,i is defined by the expectation function or punishment factor as follows:The degree of belief functions between the nodes The more weight on punishment factor specifies the huge misbehavior is observed which leads to less trust value. By using this type of factor measurement, the isolation of malicious node from the normal node is achieved. The punishment factor-based deduction refers the trust rate value asThe observation scheme based on malicious behavior identification leads to less trust in traditional protocols. But, the trust-aware routing protocols implementation in this paper computes the trust rate via sequence ID matching assures the secure data transfer between the nodes.The Dempster’s Shafter theory (DST) based indirect observation computes the belief functions for three sets as follows:The belief function for the above sets if the node A observed node B as trusted node is as follows:The belief function for the above sets if the node A observed node B as untrusted node is as follows:TRi) is computed from the neighbor log collection process initially. Then, for each node, the trust value is computed by using the energy model , Trust rate (TRi) and mobility model (Mi) by using the Eq.  , reputation-based trust-aware routing protocols (RBT), trust-based GR, FBR and DICOTIDS. The proposed approach achieved high throughput and PDR and optimal end-to-end delay and less false positives. Our future work will include the security enhancement using location key management protocol. Enhancing the security by using location and key based security scheme is considered as the future work.


To maintain a competitive position, companies are increasingly required to integrate their proactive environmental strategies into their business strategies. The shift from reactive and compliance-based to proactive and strategic environmental management has driven companies to consider the strategic factors while identifying the areas in which they should focus their green initiatives. In previous studies little attention was given to providing the managers with a basis from which they could strategically prioritise these green initiatives across their companies’ supply chains. Considering this lacuna in the literature, we present a decision-making method for prioritising green supply chain initiatives aligned with the preferred green strategies alternatives for the manufacturing companies. To develop this method, the study considered a position between determinism and the voluntarism orientation of environmental management involving both external pressures and internal competitive drivers and key resources as decision factors. This decision-making method was developed using the analytic network process (ANP) technique. The elements of the decision model were derived from the literature. The causal relationships among the multiple decision variables were validated based on the results of structural equation modelling (SEM) using a dataset collected from a survey of the ISO 14001-certified manufacturers in Malaysia. A portion of the relative weights required for computation in ANP was also calculated using the SEM results. A case study is presented to demonstrate the applicability of the method. In today’s competitive market, the companies are increasingly being encouraged to incorporate environmental strategies into their business strategies. The increased competition and globalisation have caused organisations to shift from local optimisation at the firm level towards the entire supply chain . Hence tEnvironmentally conscious thinking in supply chain management offers a long list of green initiatives in various operational areas across the value chain from the raw material supply to product usage and even further to post-use processes including reverse logistics, product recovery and recycling , 2, 4, 5The pressures from external stakeholders, such as regulators, customers, suppliers, competitors, community groups and the media, force organisations to implement several determinant and regulatory-driven green practices. However, there are still numerous voluntary environmental activities that can be initiated by companies depending upon their competitive position and their internal resources . This prTo implement strategic planning concerning green supply chain initiatives, business managers are required to simultaneously consider external pressures, internal competitive drivers and firm’s key resources.However, there has been little discussion in the literature that shows how external pressures and internal resources interactively can affect managers’ decisions to prioritise green strategies and initiatives . Most ofConsidering this lacuna in the literature, the present study aims to develop a decision-making method for prioritising green strategies and GSCIs while considering both external and internal strategic factors. This decision method will help business managers answer the following questions while they are developing their strategic environmental plan:Meeting the demands of external stakeholdersAchieving the firm’s desired competitive advantagesOptimising the exploitation of the firm’s key resourcesWhat will be the priorities of green strategies if the company aims to satisfy the following objectives simultaneously?What will be the priorities of GSCIs if the company aims to adopt the prioritised green strategies?To develop a more reliable method, the present study is devoted to establishing the link between empirical studies and the approach of quantitative decision-making modelling that has only been carried out in a small number of previous studies . For thiTo demonstrate the applicability of the proposed method, we tested the method using the case study of a manufacturer from the electronics industry in Malaysia.Recently, researchers and practitioners have shown an increased interest in the topic of green supply chain drivers, pressures and practices. There have been several empirical studies in the literature analysing the determinant factors and their impacts on the development of green initiatives in supply chains –21.Most previous studies assumed that there is a direct relationship between external drivers and green supply chain initiatives. Most researchers referred to external drivers as institutional pressures imposed by regulatory bodies , 22–25, From the literature review, it can be seen that the external drivers have received more attention from researchers compared to the internal drivers. Examining the impact of firms’ key resources as an internal driver or enabler to drive the green initiatives is placed in second rank. Although there are several arguments proving that the implementation of green initiatives generates competitive value for companies –34, few Additionally, several attempts have been made to evaluate environmentally related practices by applying the ANP/AHP techniques. So far, however, these studies paid more attention to the operational level of decision making than the strategic level.Previous studies suggested various independent decision factors that impact the priority vector of green supply chain initiatives. These include internal factors, such as “time period” , 35, “prIn reviewing the internal decision factors, it can be seen that the internal strategic factors associated with competitive advantage have been partially addressed in a few studies. For example, Sarkis , and BuyA firm’s key resources constitute another internal strategic factor that has been rarely addressed in the literature. In its general form, the influence of a firm’s key resources on the prioritisation decision for green supply chain initiatives has been presented as the readiness of a firm’s operational process to contribute to green programmes .With regards to external strategic factors, a few studies include institutional pressures, such as regulatory pressures , customeWhile the role of a firm’s environmental strategic approach to drive the implementation of green supply chain initiatives is emphasized in the literature , 41, mosOnly a few studies paid attention to the role of green strategies in prioritising green supply chain initiatives. In this respect, the study conducted by Chen et al.  made an Because organisations have to adopt a proactive approach to gain competitive advantages, the model suggested in this paper will take a forward approach. In this approach, the company first decides which green strategy should receive the most focus while considering all the external and internal factors. Then, in the second stage the priorities of GSCIs to achieve these prioritised strategic goals will be determined. These priorities will be made based on two factors, the importance of the green initiatives to the fulfilment of the requirements of the green strategy and the current performance of the firm in each green initiative.The first step in making the strategic prioritisation of GSCIs is to determine the green strategies preferences of the company. To do so, we applied two well-known theories, namely, the natural resource-based view (NRBV) –45 and iAccording to institutional theory, external pressures, by forcing companies to implement regulatory-driven practices, reduce the variation in firms’ environmental strategies. Meanwhile, the natural resource-based view (NRBV) suggests that the firm’s key resources providing differentiation can increase the variation of strategies .Institutional theory emphasises the role of external pressures imposed on the organisation to develop green initiatives. The main sources of external pressures are regulatory bodies, customers, competitors, and society  , 46. TheAccording to the NRBV framework, there are three main green strategies, namely, pollution prevention, product stewardship, and clean technology, which can bring to companies the competitive advantages of cost reduction, reputation and legitimacy, and future positioning, respectively. The strategic factors of competitive advantages expected by companies can motivate them to incorporate several voluntary green initiatives in their environmental improvement programs. These voluntary green practices can provide companies the expected competitive advantages.Each of the above-mentioned green strategies dictates a series of green initiatives in various operations of the supply chain based on the following definitions:Pollution prevention refers to reducing waste and emissions from the company’s current operations through the incremental improvement of the company’s existing products and processes.Product Stewardship relates to reducing the environmental impact of a company’s existing products and processes at every stage of a product’s life cycle .Clean technology extends beyond a company’s existing products and business models. Companies adopting this approach use innovative technologies and make a disruptive change in their product and process design so they can gain benefits from future market opportunities.By referring to the definition of these green strategies, adoption of these strategies can be realised through the various green initiatives across the supply chain classified in the following categories , 48:Product design for the environment involves initiatives relating to the design of products for reuse, recycling, or recovery, the design of products for reducing emissions and other environmental design objectives.Greening upstream relates to using environmentally friendly materials, collaboration with suppliers in environmental objectives, and other upstream activities.Green production includes activities such as the optimisation of manufacturing processes to reduce waste, the consumption of materials and energy and the recycling of materials internally in the company.Greening downstream addresses environmentally friendly transportation, green packaging, and cooperation with customers in environmental objectives.Greening post-use refers to initiatives treating used products such as recovery activities for used or defective products/components or recycling from end-of-life products.According to NRBV, key resources such as continuous improvement, stakeholder integration, and disruptive change enable companies to implement these green strategies. The strategic factors of a firm’s key resources can help managers make more realistic decisions on launching green initiatives with achievable performance targets.As a consequence of applying these two theories, we introduced five clusters in our decision-making model. There are three clusters for the strategic factors that influence the prioritisation of green strategies, namely, institutional pressures, firm’s desired competitive advantages, and firm’s key resources. The other two clusters are green strategies and green supply chain initiatives.To develop a decision-making method for prioritising green strategies and initiatives, this study went through six steps within three main stages in the research process see .The study begins with a review of the literature on green strategies and GSCIs and the factors that influence the decisions of firms’ managers in the prioritisation of green strategies and initiatives. The outcome of this step is a conceptual model of the causal relationship among the determinant factors, green strategies, green initiatives and performance.To validate the developed conceptual model, we conducted a questionnaire survey among the ISO 14001-certified manufacturers in Malaysia. The manufacturing sector of Malaysia accounted for 24.9% of the gross domestic product (GDP) in 2012, and is considered to be the second largest contributor in Malaysia’s GDP . ThroughSince the food industry does not cover all operational areas of green supply chain initiatives, this industry is excluded from the scope of study. For example, the initiative of eco-product design and the initiative of greening post-use in the food industry would be limited to packaging and is not applicable to food products.ISO 14001 certified companies were selected as they are more likely to incorporate the green supply chain initiatives into their business. This is supported by previous studies –54.The unit of analysis in this study is the individual company. The sampling frame was obtained from SIRIM and the Federation of Malaysian Manufacturers (FMM) directory  of MalayThe dataset is comprised of 139 completed questionnaires out of 430 questionnaires that we distributed to each firm’s Environmental Management Representative (ERM). The ERM is a key informant in EMS ISO 14001-certified companies who has knowledge about green issues . The givthe results and contributions will be solely used for academic research purposes and research article publications and no attempt will be made at identifying the individuals and/or the organizations they represent in any publications”. We asked the respondents to fill up the questionnaire if they agree to these terms and conditions. Before analysing data, all identifiable information was removed from the data set to be insured that the respondent and its organization’s identification is kept confidential.We used a web-based survey solution, SurveyMonkey , to distThe collected data have been analysed by applying partial least square-based structural equation modelling (PLS-SEM)  using thStructural equation modelling (SEM) is a second-generation statistical method that has several advantages in comparison with traditional methods such as multiple regression analysis. One of the most important feature of SEM is its ability to simultaneously test the relationships between the multiple variables as a structural model and the relationship between a latent variable and its indicators as a measurement model . Recent There are two types of SEM, namely, covariance-based SEM (CB-SEM), and partial least square-based SEM (PLS_SEM). CB-SEM is applied in confirmatory research to test the established theories, while PLS-SEM is used in exploratory research to develop theories .There are several reasons why researchers in the strategic management discipline use PLS-SEM. The main reasons are the ability to predict and explain the variance of key target constructs, handle the small sample size, manage the non-normal data and analyse the formatively measured constructs . We haveWith regards to the factors and their relationship in the validated model, a decision-making tool was designed by using the ANP technique .ANP is a more general form of the analytic hierarchy process (AHP) technique that was developed to prioritise the alternatives in a decision problem by formulating the problem as a hierarchic structures consisting of a goal, criteria, and alternatives . As oppoANP was selected, firstly, because it is the most commonly applied method for evaluation GSCM decision alternatives. Secondly, with regards to the nature of the decision model developed in this research, which underlines the satisfaction of the requirements imposed by both external and internal drivers, simultaneously, adopting a win-win approach in the decision making procedure is critical. As Seuring  noted, wTo obtain the relative intensities of the clusters and elements in the model, we applied two approaches. First, we made twelve sets of pairwise comparisons for determining the relative importance of the cluster’s institutional pressures, key resources, and competitive advantages and their elements in respect to the goal. Based on the AHP approach , we usedSecond, we prepared four matrices of relative importance by calculating the results of the total effect of a given variable on a target variable in the structural model. The total effect has been derived from a PLS path model by estimating the direct, indirect and total relationship in the structural model.For performing computations and obtaining the priorities in the ANP model, we used the “Super decision” software. We used the direct mode in the “Super decision” software to input the weights that were calculated based on the total effects derived from PLS path model. After entering the raw data in the “Super decision” software, the computations were performed, forming the unweighted supermatrix, weighted supermatrix, and limit matrix. The limit matrix provides the relative priorities for each alternative considered within the decision framework. These priorities show the importance of every green strategy in satisfying the internal and external factors and the importance of every GSCI in achieving the prioritised green strategies. To ensure the model stability, we executed sensitivity analysis, which shows how the optimal solution responds to changes in input parameters.To adjust the priorities according to the current performance of the company, it was necessary to calculate the company’s performance in every area of green supply chain operation and conduct the importance-performance analysis. This technique can improve the method of prioritisation .To conduct an importance-performance analysis, we developed a performance measurement system including the measurement indicators and their weights associated with the GSCIs. The indicators are the formative measures that we have already collected data for and analysed in the PLS-SEM. The weights for these formative measures are also derived from the results of measurement model in the PLS-SEM. To calculating the performance, we asked each company to assign a score between 0  to 100 (excellent) to each indicator. The final priorities of GSCIs were made based on importance-performance matrix analysis (IPMA).As the last phase of the research, the usability of the developed prioritisation method was evaluated using the case study. To evaluate the method’s usability, a 2-hour feedback session was conducted by involving the environmental management representative (EMR) in the process of method’s implementation and evaluation.For conducting a systematic evaluation, an interview protocol was also developed to consider the user’s feedback through semi-structured interviews. This interview protocol included questions on usability dimensions, namely, learnability, efficiency, and helpfulness, which were selected from the dimensions suggested by the literature for evaluation of the software usability –70. ThesAt the beginning of the meeting, the purpose of the method, the concept behind the prioritisation method, the procedure for prioritising green strategies and green supply chain initiatives, and the main functions of the method were briefly explained for the contributors in the evaluation process. The EMR was then asked to provide the input regarding the current performance score of the green supply chain initiatives in his/her company. After inputting the performance scores, the EMR was asked to make pairwise comparisons. By taking these inputs, the final report showing the priority vector of green supply chain initiatives was made using the Super Decisions  softwareAccording to the findings from the literature review (presented in section 2), we have introduced five clusters in our decision model, namely, institutional pressures, competitive advantages, key resources, environmental strategies, and GSCIs. Each of these clusters has its own elements, as shown in To test the conceptual model we applied the partial l square-based SEM (PLS-SEM) technique. To ensure the validity and reliability of the model, we evaluated the measurement model for both reflective and formative measures.To evaluate the reflective measurement model, we analysed composite reliability (CR), convergent validity, and discriminate validity following the methodology documented by Hair et al. . Table 3Discriminate validity refers to the distinction of a construct from the other constructs in the measurement model. To test the discriminate validity, we used Fronell-Larcker criterion approach. According to this approach, the square root of the AVE for each construct must be larger than its correlation with other constructs . This heTo evaluate the formative measurement model, we first assessed the collinearity issues by examining the variance inflation factors (VIF), which are recommended to be less than the value of 5 . Except Common method variance (CMV) has been also examined by using Harmen’s single factor test in the SPSS software and the results showed that the measures are not affected by CMB.To investigate how each decision element in a cluster influences its related elements in the other clusters, we evaluated the structural model to investigate the proposed relationship in our conceptual model. To obtain the relative intensities of the first three clusters and their elements with respect to the objective of the strategic improvement of green supply chain management, we suggest using the firm’s environmental management representative’s judgment for making pairwise comparison between the clusters and elements. The reason behind this is that the environmental management representative is a key informant in Malaysian ISO 14001-certified companies who has the appropriate knowledge about green issues . We haveCompany X is a subsidiary of a Japanese manufacturer that produces electronic components in Malaysia. The company is a large-size company with more than 15 years experiences in the electronics manufacturing industry. The company works with several suppliers from Asia, and its products are sold in Southeast Asia and the rest of the world.Similar to AHP, pairwise comparison is made based on a scale from 1 to 9 in which 1 indicates the equal importance of two elements regarding their contribution to the objective and 9 indicates the absolute importance of one element over the other.The responses to pairwise comparisons are presented in The relative weights of the clusters with respect to the goal in company X are 0.121957, 0.558425, and 0.319618 for institutional pressures, competitive advantages, and key resources, respectively.To obtain the relative intensities of the elements in the cluster green strategies and GSCIs, we calculated the total effect of each independent element on its associated dependent variables. For this purpose, we ran the PLS algorithm for the modified causal relationship model including only significant relationships. The results of the total effect are shown in Then, by normalising the values of the elements in one cluster with respect to its control variable in another cluster, we obtained the relative priority vector. For example, the results of our survey show that competitor pressures has a total effect of 0.2532, 0.1451, and 0.2143 on the adoption of pollution prevention, product stewardship, and clean technology strategy, respectively. By normalising these total effects, we can obtain the relative importance of each strategy in satisfying the requirements of competitive pressures by 0.41332, 0.236859, and 0.34982, respectively.After obtaining all the relative intensities of the clusters and elements in the ANP model, the importance of green strategies and GSCIs was computed by forming the supermatrix. We used the Super Decisions  softwareTables The unweighted supermatrix is an arrangement of all priority vectors, representing the impact of a given set of elements in a cluster on another element in the network. The priorities vector for the elements in each cluster can be seen from the unweighted supermatrix.The weighted supermatrix was formed by normalising the values in every column of the unweighted supermatrix, so that the sum of the column was equal to the value of 1. To normalise the values, the relative score of each element was multiplied by the relative importance of its cluster.The optimum solution was obtained using the limiting priorities. The limiting priorities were computed by multiplying the weighted supermatrix by itself n times until the columns stabilised. The final ranking shows the influence of each of the GSCIs on the objective of the strategic improvement of green supply chain management. According to the results, the ranking of the green strategy is: 1-pollution prevention, 2-product stewardship, and 3-clean technology. For the implementation of this ranking of green strategies, the importance of each green initiative is: 1-greening upstream, 2-green production, 3-product design for the environment, 4-greening downstream, and 5-greening post-use.To ensure the stability of our final outcome, we conducted a sensitivity analysis. The results show that changes in the input parameters by the value of 70% do not affect the overall rank of GSCIs, confirming that the solution is robust. For example, the results of sensitivity analysis for 4 elements are illustrated in Figs i.To complete our analysis and finalise the priorities, we calculated the performance of each GSCI in company X. For this purpose, we asked the company to make a score between 0 , and 100 (fully implemented) for each indicator associated with the green initiatives. Then, we calculated the performance of the GSCIs by applying the Eq  with its value of importance and calculated the contribution of the green initiatives in achieving the prioritised green strategies. Each of green initiatives prioritised in this step has several sub-practices See . The priThe final list of prioritised green strategies, GSCIs, and the various practices in each initiative can help company X initiate its strategic plan for its environmental improvement program. For example because the two highest ranks are assigned to the area of green production and greening upstream, improving these areas can be a good start for improving the firm’s green supplier management. Given the high priority of greening upstream, the company is recommended to pay close attention to expanding the extent of environmental actions to its supplier. Communicating a shared vision among the firm’s suppliers toward the pollution prevention strategy as the first-ranked green strategy is suggested as the starting point. The prioritised list of practices in the area of greening upstream also can be considered a guideline for developing the detailed environmental assessment program for suppliers.To evaluate the usability of the proposed method for the strategic prioritisation of green supply chain initiatives, the EMR in the case study were asked to comment on the entire method and then to give her feedback overall and for each dimension of usability, namely, learnability, helpfulness and efficiency. Her comments and feedback are transcribed and analysed here under.From the evaluator’ overall opinion about the entire system, it can be seen that she found the method helpful. In addition, she believed that the proposed method is comprehensive, systematic and structured, and involves comprehensive strategic factors and green supply chain initiatives. Regarding the applicability of the outputs, she believed that the outputs are applicable and are consistent with her expectations.“The decision factors considered in this software are very comprehensive” “This method systematically makes the priorities, now we make decisions based on one-to-one factor, but this software compares between several strategic factors and gives us the priorities.”“The results go along with what I understand the way the company gives priorities too. So, the understanding I have now about what the company directs me to do, is supported, it goes along the same line as the report, it doesn’t go against. If it is just produces different report I would be shocked, but from my experience it seems consistent. I mean from my understanding about the companies’ road versus the output of the method, the results are consistent with my expectations.”Regarding the method’s learnability, the expert user concurred that the method is easy to learn and understandable.“Regarding the learnability there is no problem, actually it is not very complicated”From the summary of the feedback obtained, it is found that the evaluator believes that the proposed method helps her to do her job more effectively. She found the method useful for gap analysis and prioritising their environmental improvement opportunities. She also commented that using this method strengthens the decision making process and increases her confidence level while making decisions. She also believed that the data-driven nature of this method provides her with the ability to make solid justification while suggesting environmental improvement programmes to her top managers.“This method is useful for us if we want to see where we are now, what we want is also there, that is good. From this method we can identify our weaknesses, from the prioritised list of initiatives we know what we should do, so we can improve ourselves in that area. It helps us to come up with some ideas for improvement of our environmental performance.”“It is just a check and balance to see that you are right on track, you may know that your main things are logically this, this, …, but this method actually proves, it can prove it using numerical data and calculation, so it helps to further strengthen your decisions.”“When we use this method if somebody asks why these priorities, we know that these priorities are made based on comprehensive strategic factors and we can justify that these priorities are based on this, this, …. Now our justification for giving the priority is not very systematic. But, with this method, because the solution is based on statistics, we have quantitative evidence and it is much easier to explain to people rather than qualitative justification. Qualitative justification always leads to arguments. Justification is very important for us, because top management ask for justification because the implementation of these initiatives is costly.”The evaluator also gave good commendations concerning the system efficiency. She believed that the system is efficient in terms of the output she received as compared with the time she spent to implement the method and the volume of input she provided to get the results.“It is quiet efficient in a couple of hours we can get a comprehensive report.”“I do find that it is very useful because you can do a lot of things with this, it doesn’t demand a lot of inputs and it produces a lot of data for you and you can get a lot of useful information from it.”By linking the findings from the empirical study of Malaysian industries to the formal assessments of green strategies and GSCIs offered in an ANP decision framework, we developed a practical tool to help company managers develop a strategic goal-oriented plan for their environmentally related programs.First, this tool determines the priorities of green strategies by identifying managers’ preferences with respect to satisfying external pressures and the desired competitive advantages while considering the firm’s capability to implement these strategies. Second, the GSCIs will be prioritised according to their relative importance to the adoption of the prioritised strategies. Third, regarding the firm’s current performance in each green initiative, the final priorities for improving these performances will be calculated.There are several opportunities to improve this work as stated below:The list of decision factors offered in this study is derived from the literature review. This list can be extended by conducting qualitative research among practitioners who are experts in this research area.The environmental management representatives (EMR) who were requested to complete the questionnaire in this research were introduced in the literature as a key informant person in the area of green supply chain management. It is assumed that EMRs have insights into the company’s strategies and policies. However, it is suggested that ANP questionnaire and the part of the survey questionnaire that relates to the firm’s strategy are completed by a involving the firm’s top manager. This might improve the accuracy of the answers. Due to the limitation of the sample size for every specific industry, we used the commutative data collected from multiple industries in our empirical study. Conducting an empirical study for every particular industry can provide an opportunity to developing a more accurate and reliable tool for every specific industry.The indicators for evaluating the performance of GSCIs have been proposed based on the various studies on the green supply chain. These indicators can be customised for each particular industry by studying the environmental reports of the leading companies in each industry.With respect to the ANP technique and performance measurement system, the nature of the present study leaves room for the application of fuzzy methods in the form of linguistic variables for pairwise comparison and performance metric evaluation.S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.S3 Appendix(DOCX)Click here for additional data file.S4 Appendix(DOCX)Click here for additional data file.S5 Appendix(DOCX)Click here for additional data file.S6 Appendix(DOCX)Click here for additional data file.S7 Appendix(DOCX)Click here for additional data file.S8 Appendix(DOCX)Click here for additional data file.


Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information. Stroke, also known as cerebrovascular insult (CVI), cerebrovascular accident (CVA), or brain attack, is when poor blood flow to the brain results in cell death. Between 1990 and 2010, the number of strokes which occurred each year decreased by approximately 10% in the developed world and increased by 10% in the developing world .Considering the large population of stroke patients in China and the limited rehabilitation resources , it is an inevitable trend for stroke patients to do rehabilitation training in home settings. Many previous research results have proven that, in comparison with inpatient care, home-based rehabilitation shows no difference in the effect on any of the outcomes ,5. Moreoetc. T was a K-sparse vector θ only has K non-zero elements.Assume the signal In this paper, for ease of hardware implementation, the sparse Gaussian random matrix was adopted, where the value of every element was previously embedded into the microcontroller unit in the form of a lookup table.Based on the framework proposed by Candes and Donoho , the priet al. [In the past decades, many researchers have focused on this problem and proposed a series of recovery algorithms, such as matching pursuit (MP) , orthogoet al. ,36 propoet al. [Extreme learning machine (ELM) was firstly proposed by Huang et al. , who appN samples Assume there are The training process of an SLFN is simply the equivalent to finding a least-squares solution H+ is the pseudo inverse of H.According to the least-squares regression theory, the best solution of the linear system is:et al. have proved that given any small positive value Compared with traditional backpropagation neural networks, ELM does not have to iteratively adjust the connecting weights and bias, as it maps the training process to a problem of solving a group of linear equations. Besides, in reference , Huang eIn summary, the whole procedure of establishing an ELM model includes three steps, which are listed in All the following experiments were approved by the Ethics Committee of Jiaxing 2nd Hospital, . Twenty-three stroke patients and four physicians were selected to participate in these experiments. According to the Brunnstrom stage classification criteria, the 23 patients’ Brunnstrom stages ranged from II to V. However, considering that patients in stage I cannot move their upper limbs without extra assistance, patients in stage I were not selected for our experiments. Similarly, due to the fact that in China, the majority part of stage VI patients leave the hospital due to the high medical costs and limited rehabilitation resources, we used four physicians instead of stage VI patients to participate our experiments. The accelerometer sensors were placed on the geometric center of the arms, as shown in Generally, in motion capturing and monitoring applications, reconstruction performance is often evaluated by comparing the reconstructed signals with raw ones through the mean square error (MSE). However, in this paper, the signal reconstruction was not the final goal, so the reconstructed accelerometer signals were further processed to establish an automatic Brunnstrom stage classification model. Due to the infidelity of the MSE for structured signals, it is difficult to analyze how the predictive accuracy of the quantitative assessment model is affected by the reconstruction errors measured by MSE. Hence, it is necessary to compare the predictive accuracy of Brunnstrom stage classification models, which are built based on the reconstructed and raw accelerometer signals, respectively. Consequently, to achieve our goal, all of the experiments were implemented in two actions: (1) Bobath handshake; (2) shoulder touch.(1)Sit down on a chair, cross the hand and keep the thumb of the hemiplegic side on top.(2)Straighten the upper extremities, lift above the head and hold for 3 s.(3)Move the hands back to the initial position.The purpose of this experiment is to validate the reconstruction performance of the BSBL algorithm during the Bobath handshake exercise. The whole process of the Bobath handshake exercise can be divided into the following three steps:An additional movie file shows the Bobath handshake exercise in more detail see .(1)Sit down on a chair, and naturally droop the upper limb of the hemiplegic side.(2)Raise the upper limb of the hemiplegic side to the horizontal position.(3)Move horizontally to the healthy side shoulder and hold for 5 s.(4)Move back to the initial droop position and take a short break.Our previous studies have found that the accelerometer signals of the shoulder touch exercise can be applied to automatically classify the Brunnstrom stage for stroke patients. To investigate whether the reconstruction errors affect the predictive accuracy of the quantitative assessment model, the shoulder touch exercise was completed by every participant in order to collect the compressed accelerometer signals. The whole process of the shoulder touch exercise can be divided into the following four steps:An additional movie file shows the shoulder touch exercise in more detail see .All patients were required to finish these exercises without extra assistance to reflect their true movement function. Before the data collection, they were asked to practice several times with the guidance of physicians so that they were familiar with the whole process. During the experimental processes, they were requested to repeat the shoulder touching exercise eight times.The data sampling and management were implemented by using the Remote Rehabilitation Training and Assessment Software (RRTAS), which was developed by our group. The run environment of RRTAS is Windows 32 bit platform and .Net Framework 3.5 or above. An additional document file introduces RRTAS in more detail see .The raw accelerometer signal of the Bobath handshake exercise is shown in As mentioned above, by adjusting the dimension of sensing matrix Here, we first set the CR to 0.7222 to validate the reconstruction performance of the BSBL algorithm. This means that the raw accelerometer signal was compressed to 1500 sampling data. The corresponding compressed signal is shown in From In this section, we will discuss the effects of block size and compression ratio on the quality of signals reconstructed from raw accelerometer signals. Here, we chose the signal to noise ratio (SNR) as the evaluation index to analyze the reconstruction error. The definition of SNR is shown in Equation (10):The effects of block size on SNR are shown in Although the reconstruction performance is affected by the block size in the block partition, this does not limit its application to the wearable sensor network. M and N should satisfy the following inequality:K is the sparsity of the raw signal. Now we validate the theoretical CR of the accelerometer signals. K ≈ 600) have large amplitudes, while the majority of coefficients have small amplitudes. Hence, the theoretical maximum CR should be:According to compressed sensing theory, the relationship between This result is essentially the same as In the previous experiment, we showed that the accelerometer signals of rehabilitation exercises can be compressed and precisely reconstructed. Another question is whether the reconstructed signals affect the predictive accuracy of the quantitative assessment model. To answer this, we carried out the following experiment: as mentioned above, there are many assessment scales in clinical settings to evaluate the movement function of stroke patients, such as the Fugl-Meyer assessment scale, Functional ability scale, Brunnstrom stage classification, Action Research Arm Test, and so on. Compared with other assessment scales, the Brunnstrom stage classification tool only has six levels, hence it has the advantage of being easy to use and time saving. In our previous work , we haveDue to space limitations, here we present only the reconstructed and raw axis Y1 accelerometer signals ranges from Brunnstrom stage II to VI in To investigate the effects of reconstruction error on the predictive accuracy of the Brunnstrom stage classification model, we established the quantitative assessment model with the reconstructed and raw accelerometer signals, respectively. To ensure the consistency of our comparison, the feature extraction and ELM parameters were all set to be the same. More specifically, 12 features including root mean square (RMS), amplitude (AMP) from three axes of two accelerometer sensors were extracted and set as the input of ELM model, the output of the ELM model was the Brunnstrom stage level, and the number of hidden neurons was set to 30. The training set contains 152 samples and the testing set contains 38 samples.The results are shown in To reduce the amount of data in a wearable sensor network during the sampling and transmission processes, this paper proposes a novel wearable sensor network based on compressed sensing technology, and applied it to the monitoring and quantitative assessment of stroke patients’ upper limb motor function. The experimental results showed that the proposed method can not only compress and precisely reconstruct the raw accelerometer signals, but can also apply the reconstructed signals to establish the Brunnstrom stage automatic classification model. It also indicated that the proposed system can provide a theoretical basis for individualized and remote rehabilitation. However, considering the fact that the clinical experiment only recruited 23 stroke patients, which is too small a number to verify the statistical reliability and validity of the proposed quantitative assessment model, in the future, we will gather more clinical data and improve the generalization performance of the model. Additionally, in this paper, the compressed signals need to be reconstructed on the computer side before the analysis process. In the future, we will try to investigate whether the analysis process can be directly implemented in the compressed domain. If yes, this could reduce the off-line computation burden and make the “on-node” analysis more possible.


Foot pathologies can negatively influence foot function, consequently impairing gait during daily activity, and severely impacting an individual’s quality of life. These pathologies are often painful and correspond with high or abnormal plantar pressure, which can result in asymmetry in the pressure distribution between the two feet. There is currently no general consensus on the presence of asymmetry in able-bodied gait, and plantar pressure analysis during gait is in dire need of a standardized method to quantify asymmetry. This paper investigates the use of plantar pressure asymmetry for pathological gait diagnosis. The results of this study involving plantar pressure analysis in fifty one participants  support the presence of plantar pressure asymmetry in normal gait. A higher level of asymmetry was detected at the majority of the regions in the feet of the pathological population, including statistically significant differences in the plantar pressure asymmetry in two regions of the foot, metatarsophalangeal joint 3 (MPJ3) and the lateral heel. Quantification of plantar pressure asymmetry may prove to be useful for the identification and diagnosis of various foot pathologies. Gait is one of the most frequently used forms of human movement during daily activity. As an inherently complex task, human gait requires the coordination of both neural and musculoskeletal systems to provide balance and stabilization of the body during movement. Gait is represented by out of phase leg movement, in which each leg successively shifts from one phase of the gait cycle to the next. Analysis of gait parameters plays an important role in the evaluation and characterization of able-bodied and pathological gait. The analysis of foot function in particular is essential, as the feet are the main point of support during gait, and are constantly adapting to various environments and regular exposure to large forces. The gait cycle is fundamentally divided into stance and swing phases. The stance phase accounts for 60% of the total gait cycle, during which the foot is in contact with the ground and bears the full weight of the body. The swing phase comprises the remaining 40% of the gait cycle and begins at the toe off of the foot. During this phase the foot is off the ground and swinging forward to begin the next stance, while the body weight is transferred to the other foot  1,2].,2.1,2].Changes to normal foot function can result in impaired gait. One of the most common and debilitating clinical conditions that can impact foot function during the gait cycle is the pain pertaining to the pathological foot. This pain is often associated with higher than normal plantar pressure, which can incite various injuries during daily activity. The alteration of normal foot function can instigate a chain of unfavorable outcomes which range from inflicting further high pressure onto a new location of the foot, to placing the individuals at a greater risk of imbalance and injury when walking . These oThere have been substantial advancements in plantar pressure measurement over the recent decades. The development of measurement technology has been heavily influenced by an interest in understanding the pressure generated by the feet during human locomotion, both qualitatively and quantitatively ,7. InforPlantar pressure sensing technology has, without doubt, become an indispensable tool for gait analysis in both clinical and research settings. The sensing technology, which is capable of measuring static and dynamic pressure, has played a crucial role in understanding the effects of the pressure on the foot. A variety of pressure sensing systems are currently available in the market, with research continually working towards improving and developing new systems to meet the growing demands of a large user base. There are slight variations in the nature of the pressure sensors used in commercially available systems, and all have advantages and disadvantages in their application. These sensors do, however, share a commonality in that they produce electrical signals that are proportional to the vertical forces acting on the individual sensors when the plantar surface of the foot makes contact with a supporting surface ,14. The Platform systems are comprised of a large number of sensors arranged in a matrix formation, and generally offer a high spatial resolution for the measurement of plantar pressure . These sIn-shoe pressure measurement systems include sensing elements in the insoles ,15 and hWhile measurements of plantar pressure are relatively easy to obtain, quantifying and interpreting the results has proven to be a difficult task, which has consequently limited the analysis and diagnosis of plantar pressure abnormalities . Anotheret al. [Lower limb symmetry has often been used as a reference to determine able-bodied gait . Variouset al.  proposedet al. .Opinions are divided on what constitutes a diagnosis of pathology due to asymmetrical limb function, and there is no general consensus on the presence, or degree, of lower limb asymmetry in healthy populations . It is e2 and contain a total of 960 sensing elements, which can be trimmed to fit into the participants’ shoes. Sensors were not used for more than five sessions of data collection. The F-scan software accompanying the pressure measurement system was used to calibrate the sensors according to each participant’s body weight prior to data collection, and record approximately six to seven steps (stances) per foot at 100 Hz for each participant.Fifty one participants  took part in the data collection. 2 area; average of four sensors) within each region during the middle four stances taken by the participants. Peak plantar pressure for the whole foot was also extracted per stance. To accommodate for variations in the shoes among participants, extracted pressure values from each of the 10 regions were normalized to the peak pressure of the whole foot per stance. Normalized data from the four stances were averaged for the left and right feet individually for the 10 regions. These averages were used for the quantification of asymmetry.A customized mask comprised of 10 regions of interest was fitted to each pedobarographic image using the F-scan software. The Symmetry Index (SI)  and SymmSymmetry Index: Symmetry Angle: 1 and Xright are the pressure parameters for the right foot, and X2 and Xleft are the pressure parameters for the left foot. Perfect symmetry between the feet is signified by a value of 0% for both the SI and SA, while perfect asymmetry is indicated by a value of 100% only for the SA. In this paper, group results have not taken into consideration which foot contains the highest asymmetry, as this result is subjective. The focus has instead been placed on identifying the degree of asymmetry per pressure location, therefore only absolute values will be reported.However, if U test. Data are presented as medians unless otherwise stated. Results from the test were considered to be statistically significant at p < 0.05.All statistical analyses were performed using SPSS Statistics version 20 . Prior to analysis, all data were assessed for normality. As data were not normally distributed, the control and pathological groups were assessed using the nonparametric Mann-Whitney t-tests to determine significant differences between the left and right feet. Variables with a p < 0.05 were considered to display significant asymmetry, and were reanalyzed using the Mann-Whitney U test described above to ascertain whether group asymmetry results were influenced by large individual participant asymmetry values that were not significant [As symmetry calculations often use mean values from left and right limbs, there is the possibility that a large amount of intra-limb variability could result in large, but not significant, differences in the mean values, consequently leading to misleadingly large asymmetry values . For a snificant .The reliability of the plantar pressure measurement was assessed on five healthy individuals from the control group in two stages: (1) within-session; and (2) between-session. The first stage compared data from two consecutive walking trials from the same session, collected and extracted using the methods described above. The walking trial was repeated one week later for between-session comparison using the same F-scan sensors. The intraclass correlation coefficient  was calculated for all anatomical regions of interest using measures of absolute agreement. In line with the suggestions of Portney and Watkins , ICC valThe ICC for within-session walking trials demonstrated excellent reliability, with all regions of interest obtaining ICC values greater than 0.86. Between-session results also showed good reliability, with all regions, apart from the lesser toes and midfoot, achieving ICC values greater than 0.80 . These rThe outputs from plantar pressure measurement systems can provide important visual feedback during the diagnostic process to assist in identifying the problem areas of the foot. These outputs, which can be examined in both 2-dimensional (2D) and 3-dimensional (3D) form, give an insight into the progressive changes in pressure during gait. These changes in pressure can be distinguished by a color legend ranging from dark blue (low pressure) to red (high pressure) .The pressure changes caused by the rollover of the foot during the stance phase of able-bodied gait  typicallMetatarsalgia, which is pain at the metatarsals, can cause significant changes in pressure around the affected area. In the example presented in In Asymmetry levels between left and right feet were determined from the normalized peak plantar pressure parameters at 10 anatomical regions of interest for the control and pathological populations. Asymmetry values from the SI and SA were found to be perfectly correlated based on the Spearman’s rank-order correlation analysis. The degree of plantar pressure asymmetry in the control group showed a median range between ~10%–18% for the SI, and ~3%–6% for the SA across the regions of interest. This was slightly higher for the pathological group, with detected levels of asymmetry ranging between ~14%–22% for the SI and ~4%–7% for the SA .U test was run to determine if there were differences in asymmetry levels between the control and pathological groups at each of the anatomical regions of interest. p = 0.01) and at the lateral heel .A Mann-Whitney U test  was at the medial heel.To take into account intra-limb variability, asymmetry levels between the control and pathological groups at each of the anatomical regions of interest were reanalyzed using the Mann-Whitney U test  to only t-tests typically also had a larger calculated asymmetry percentage. As with the group results, the levels of asymmetry between the feet were also found to be statistically significantly higher for the pathological group in comparison to the control group at MPJ3  and at the lateral heel . Additionally, MPJ5 also showed a statistically significant increase in the asymmetry level for the pathological group versus the control group .The median asymmetry for both pathological  and control  groups is higher when accounting for intra-limb variability, as the variables identified as significant by the individual paired-samples This study aimed to determine a normal range of plantar pressure asymmetry, and to investigate the effect of foot pathologies on deviations from this normal asymmetry range during gait. The results of this study support the notion of asymmetrical plantar pressure during able-bodied gait. This is consistent with previously published research. The findings of this study also showed significant increases in the level of plantar pressure asymmetry at the third MPJ and the lateral heel in a pathological population.Plantar pressure analysis predominately relies on the detected level of pressure to make a judgment in regards to the presence of a foot pathology. This has eased the diagnostic process involved during foot assessment, and significantly improved the treatment capabilities for the pathological foot, particularly in cases of pressure redistribution to ease foot pain and reduce the risk of plantar injury ,33,34,35Foot function can be severely affected by injuries to the forefoot, particularly at the metatarsal region. The site of injury is often reflected not only in the plantar pressure distribution, but also in the measures of asymmetry between the feet . The preHowever, asymmetry in the lower limb is not only associated with the manifestation of a pathology, but is also found to be present in able-bodied gait in this study, with a normal range of asymmetry determined to be between approximately 10%–18% based on the SI, and 3%–6% based on the SA across the regions of interest examined on the plantar surface of the foot. The assumption of symmetry in able-bodied gait can be problematic in gait analysis, especially when lower limb asymmetry is considered to be related to gait pathology . This haet al. [et al. [This study quantifies measures of asymmetry based on the SI proposed by Robinson et al.  and the  [et al. . As prev [et al. ,51,52. M [et al. . The pot [et al. . It has  [et al. ,31. IntrAlthough the findings show significant regional plantar pressure asymmetry, several limitations are present in this study. The pathological population comprised of individuals with varying foot problems, which include hallux valgus, metatarsalgia, and subtalar joint and heel pain. Different patterns of asymmetry are likely to be observed in different foot pathologies during gait. While these findings are not conclusive for all pathologies, there does appear to be a shared commonality in the changes to plantar pressure asymmetry at the third MPJ and also at the lateral heel. Although it should be noted that plantar pressure measurements could potentially be influenced by body mass index (BMI) and also the nature of the shoes worn during gait, both of which were not assessed in this study.The difference in sample size between the control and pathological population in this study is potentially another limiting factor. The small number of participants in the pathological group did not allow for a more in-depth analysis into the effects of particular pathologies on plantar pressure asymmetry. Further research needs to be performed with a larger sample size to allow for comparisons among subjects with specific types of foot pathologies. Ideally, a large-scale analysis of plantar pressure symmetry should also be carried out in a healthy control population. This is particularly important in the development of a standardized definition of symmetry in the feet, which would provide researchers and clinicians with a predetermined normal range of symmetry, outside of which would indicate abnormality in the plantar pressure distribution.This study supports the presence of plantar pressure asymmetry in able-bodied gait. Significant increases in the plantar pressure asymmetry of the pathological population have also been demonstrated. Assessments of plantar pressure asymmetry could potentially assist and improve the diagnostic process to determine the presence of a foot pathology. Furthermore, these assessments may serve to be a useful tool in early detection of plantar injury, and can also aid in the identification of effective treatment options, allowing for more informed decisions to take place in regards to appropriate methods of treatment. Future research would benefit from investigating pressure asymmetries in specific pathologies, including the impact of limb dominance on plantar pressure, and the long-term changes that take place in the presence of a foot pathology during gait.