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This article investigates a Denial-of-Service (DoS) attack problem for nonlinear unknown discrete-time multiagent systems (MASs) to implement bipartite consensus tracking tasks with fixed and switching topologies. Firstly, an equivalent linearization data model of each agent is constructed using a pseudo partial derivative approach, where only one parameter needs to be estimated using input/output data of the controlled MASs. Meanwhile, the DoS attack behavior is described by a Bernoulli distribution process, and both cooperative and competitive relationships among agents are investigated. Moreover, an increment prediction compensator is designed to reduce the effect of DoS attacks. A data-based adaptive predictive bipartite consensus control algorithm is formulated. The corresponding theoretical analysis indicates that tracking errors of MASs with fixed and switching topologies converge to a small range around zero. Finally, several simulations and hardware tests further verify the proposed scheme's effectiveness. [ABSTRACT FROM AUTHOR]

With the emergence of more and more lightweight, convenient and cheap surface electromyography signal (sEMG) snsors, gesture recognition based on sEMG sensors has attracted much attention of researchers. In this study, combined with the sEMG sensor, a novel dynamic hand gesture recognition approach is proposed for effective and accurate dynamic gesture prediction. Here, a portable sEMG sensor (Myo wristband) is adopted to acquire the multi-channel sEMG signals of dynamic hand gestures and the continuous wavelet transformation (CWT) is proposed for data preprocessing to acquire the time-frequency maps. Due to the success of powerful contextual feature representation capability of deep convolutional neural networks (DCNNs), a deep residual attention network is proposed for accurate prediction of time-frequency maps. To effectively extract the key spatial and channel features from multi-channel sEMG signals, a residual attention network is proposed to act as the backbone network for effective feature representation. Besides, In the proposed recognition network, a multi-scale feature enhancement (MFE) module and an attention fusion block (AFB) are proposed, which respectively improve the multi-scale expression ability of the network and effectively realize multi-scale feature enhancement, respectively. Experimental results show that the proposed recognition network could achieve a superior detection ability compared with other state-of-the-art recognition models. The source code and dataset are available at https://github.com/lyangucas92/Ges_Net. [ABSTRACT FROM AUTHOR]

Advanced intelligent control (AIC) is a rapidly evolving and complex field that poses significant challenges. By combining DLNet and the GJK algorithm, a two-level self-collision detection algorithm (DLGJK) is developed to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages in terms of the speed with which the roadmap is constructed, path planning and path length. "A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning" [[15]] introduces AI technology into a control system based on the Gilbert-Johnson-Keerthi (GJK) algorithm. [Extracted from the article]

Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks. [ABSTRACT FROM AUTHOR]

The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential in retinal vessel segmentation tasks, but have limited feature representation ability. In addition, they don't effectively consider the information at multiple scales when performing feature fusion, resulting in low fusion efficiency. In this paper, a newly model, named DEF-Net, is designed to segment retinal vessels automatically, which consists of a dual-encoder unit and a decoder unit. Fused with recurrent network and convolution network, a dual-encoder unit is proposed, which builds a convolutional network branch to extract detailed features and a recurrent network branch to accumulate contextual features, and it could obtain richer features compared to the single convolution network structure. Furthermore, to exploit the useful information at multiple scales, a multi-scale fusion block used for facilitating feature fusion efficiency is designed. Extensive experiments have been undertaken to demonstrate the segmentation performance of our proposed DEF-Net. [ABSTRACT FROM AUTHOR]

10.3390/machines9100216 5 Shen P., Wang Y.W., Chen Y., Fu P.Q., Zhou L.J., Liu L.J. Hydrodynamic Bearing Structural Design of Blood Pump Based on Axial Passive Suspension Stability Analysis of Magnetic-Hydrodynamic Hybrid Suspension System. Research and experiments on mechatronics systems, which are a synergistic integration of mechanical engineering, electronic control, and systems concepts, have contributed significantly to the design of systems, devices, processes, and products. Based on the analysis of the static moment safety protection of the human-machine linkage model, the motor driving force required in the rehabilitation process was calculated to achieve the purpose of rationalizing the rehabilitation movement of the patient's lower limb. In [[9]], the mechanism of the lower limb rehabilitation module was simplified and the geometric relationship of the human-machine linkage mechanism was deduced. [Extracted from the article]

This article proposes a novel distributed data-driven bipartite consensus tracking scheme for bipartite consensus tracking problems of multi-agent systems with bounded disturbances and coopetition networks. The proposed scheme only uses the input/output data of each agent without requiring the agents' dynamics. We obtain the equivalent dynamic linearization data model for a controlled plant using the dynamic linearization technique based on the pseudo partial derivative. Considering the cooperative and competitive interactions among agents, the proposed method ensures that agents with adversarial relationships implement bipartite consensus tracking tasks even if only a subset of agents can access the information from the virtual leader. Moreover, the strict proof process of convergence properties reveals that the tracking error coverages to a small range around the origin. We also establish a set of software and hardware platform to demonstrate the effectiveness of the proposed distributed data-driven bipartite consensus tracking method. [ABSTRACT FROM AUTHOR]

Early detection and diagnosis of COVID-19, as well as the exact separation of non-COVID-19 cases in a non-invasive manner in the earliest stages of the disease, are critical concerns in the current COVID-19 pandemic. Convolutional Neural Network (CNN) based models offer a remarkable capacity for providing an accurate and efficient system for the detection and diagnosis of COVID-19. Due to the limited availability of RT-PCR (Reverse transcription-polymerase Chain Reaction) tests in developing countries, imaging-based techniques could offer an alternative and affordable solution to detect COVID-19 symptoms. This paper reviewed the current CNN-based approaches and investigated a custom-designed CNN method to detect COVID-19 symptoms from CT (Computed Tomography) chest scan images. This study demonstrated an integrated method to accelerate the process of classifying CT scan images. In order to improve the computational time, a hardware-based acceleration method was investigated and implemented on a reconfigurable platform (FPGA). Experimental results highlight the difference between various approximations of the design, providing a range of design options corresponding to both software and hardware. The FPGA-based implementation involved a reduced pre-processed feature vector for the classification task, which is a unique advantage of this particular application. To demonstrate the applicability of the proposed method, results from the CPU-based classification and the FPGA were measured separately and compared retrospectively. [ABSTRACT FROM AUTHOR]

This article proposes a distributed model‐free adaptive consensus tracking control (DMFACTC) approach for a class of unknown heterogeneous nonlinear discrete‐time multi‐agent systems (MASs) with sensor saturation and measurement disturbance to perform consensus tracking tasks. Meanwhile, both fixed and switching topologies are considered, where only a subset of agents can acquire the desired trajectory information in each topology. A time‐varying linear data model for each agent is first established by utilizing the dynamic linearization method to formulate this algorithm. Merely, the input data and the saturated output data with measurement disturbance of each agent are applied to construct the DMFACTC algorithm without employing any dynamics model information of MASs. The convergence of the designed scheme is strictly proved. It illustrates that the output saturation and switching topologies do not affect the stability of MASs. Moreover, even if the sensor saturation, measurement disturbance, and switching topologies happen simultaneously, the DMFACTC also guarantees that the tracking errors of MASs converge to a small range around the origin. Furthermore, two numerical simulations and a realistic filling system simulation further verify the correctness and effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

The capacities of parallel mechanisms are limited by their height for the narrow space applications, such as the shipboard stability platforms, household simulators, aerospace mechanisms, etc. This paper proposes a novel foldable six-DOF parallel manipulator which has three main limbs with each actuated by two actuators. With the ability to fold in the vertical direction, this mechanism can be deployed from a height of about 0.278 m to 2.218 m, and the required driving stroke is only 0.67 m by the analysis results of the workspaces. However, this stroke enlargement leads to large driving forces. In order to improve the capacity for the heavy loading of this foldable mechanism, each leg is assisted by a balance spring. Static and dynamics models are built for the calculation of the driving forces and constraint forces. Two methods to calculate the optimal balance force for objective driving force are also proposed and based on the designed trace in the workspace, the springs' linear stiffness is optimized. The simulation results demonstrate that the actuators' driving forces are much reduced, and the homogeneity between the fully folded and deployed configuration is much improved by adding balance springs. [ABSTRACT FROM AUTHOR]

This paper presents a miniature hybrid capsule robot for minimally invasive in-vivo interventions such as capsule endoscopy within the GI (gastrointestinal) tract. It proposes new modes of operation for the hybrid robot namely hybrid mode and anchoring mode. The hybrid mode assists the robot to open an occlusion or to widen a narrowing. The anchoring mode enables the robot to stay in a specific place overcoming external disturbances (e.g. peristalsis) for a better and prolonged observation. The modelling of the legged, hybrid and anchoring modes are presented and analysed. Simulation results show robot propulsions in various modes. The hybrid capsule robot consisting four operating modes is more effective for the locomotion and observation within GI tract when compared to the locomotion consisting a single mean of locomotion as the hybrid robot can switch among the operating modes to suit the situation/task. [ABSTRACT FROM AUTHOR]

Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively. [ABSTRACT FROM AUTHOR]

Purpose: Research shows that people with disabilities have same desire for travel and tourism as others, but many constraints prevent them from independent travelling. Although the disabled tourism market is expanding, still there is lack of empirical research on impact analysis of assistive technology (AT) assisted travel and tourism with focus on the people with dementia (PWD). This study investigates the impacts of AT assisted travel and tourism for the PWD. Methods: The questionnaire-based survey gathered data from 327 PWD. SPSS is used to perform exploratory factor analysis and canonical coefficient analysis for investigating the usability of AT assistance in travel and tourism. Results: Results reveal four motivational (facilitated travel and tourism, cost effectiveness, easier communication and needs compatibility) and three achievements dimensions (improved achievements, improved independence and improved safety). Independent travel is desired by the PWD without any external help and ATs can help them in doing so. The use of AT can further accommodate in choosing the tourism activities, attractions and leisure management. The PWD feel improvements in their achievements through the use of ATs in travel and tourism. Conclusions: AT support, resultantly improves the confidence and self-esteem of the PWD and motivate them towards rehabilitation and independent living. This study provides finer empirical understanding of the needs, motivations and achievements of the people with dementia (PWD) who use assistive technologies (ATs) for travel and tourism. Usually PWD have different motivations and accessibility needs, their travel and tourism related behaviours might also differ from the people not identified as having dementia. The ATs (facilitated travel and tourism, cost effectiveness, easier communication and needs compatibility) are the motivational factors for the PWD and should be focused during the development of ATs. These factors will allow PWD to better plan their travel and tourism activities without the help of their caregivers. The AT assisted communication opportunities will help PWD to eliminate their feeling of social isolation from rest of the community. AT supported travel and tourism results into sense of improved achievements, improved independence and improved safety on the part of the PWD. Resultantly, they feel more motivated to do activities, which otherwise they might be reluctant to perform. The sense of safety, independence and achievement does impact PWD psychologically and would help them towards rehabilitation. This study provides pointers for the dementia policy makers related to AT supported travel and tourism based on the real needs of the PWD. [ABSTRACT FROM AUTHOR]

• Developing a low cost, portable, easy to use digital technology for sophisticated assessment of gait to improve diagnosis and management of abnormalities at home or in clinics. • A tool to visualize gait that consists of five approaches. ○ Real-time dial visualization. ○ Visualization of individual leg time variation. ○ Visualization of both legs asymmetry. ○ Boxplot visualization. ○ Evaluation considering all features. Gait abnormalities are common in clinical practice and there is a global imperative to improve technologies that facilitate their detection, evaluation, monitoring and management. Real time evaluation using digital technology supports the development of digital healthcare. Currently gait assessment relies on visual observation of structured clinical tests such as the "Timed Get up and Go Test." Gold standard methods such as "Qualisys Motion Capture System" require sophisticated equipment in gait laboratories. These are not widely available due to expense, analysis time and requirement of trained technicians. Developing low cost, portable, easy to use digital technology is important to enable sophisticated assessment of gait at home or in clinics. Common measures for quantification of gait include symmetry angle, ratio and index. These measurements may be difficult to interpret by users as stand-alone values. To facilitate the evaluation and interpretation of locomotive information, a tool to visualize gait in real-time is proposed. The proposed tool consists of five approaches (1: Real-time dial visualization, 2: Visualization of individual leg time variation, 3: Visualization of both legs asymmetry, 4: Boxplot visualization, and 5: Evaluation considering all features). Results show that wearable Inertial Measurement Unit (IMU) can be used for extraction of objective gait features. This system opens possibilities for home-based assessment of gait without the requirement and expense of an elaborate laboratory setup and supports the development of digital healthcare. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPS) have gained an increasing attention recently for their immense potential towards the next generation smart systems that integrate cyber technology into the physical processes. However, CPS did not initiate either smart factories or smart manufacturing, and vice versa. Historically, the smart factory was initially studied with the introduction of the Internet of Things (IoT) in manufacturing, and later became a key part of Industry 4.0. Also emerging are other related models such as cloud manufacturing, social manufacturing and proactive manufacturing with the introduction of cloud computing (broadly, the Internet of Services, IoS), social networking (broadly, the Internet of People, IoP) and big data (broadly, the Internet of Content and Knowledge, IoCK), respectively. At present, there is a lack of a systemic and comprehensive study on the linkages and relations between these terms. Therefore, this study first presents a comprehensive survey and analysis of the CPS treated as a combination of the IoT and the IoS. Then, the paper addresses CPS-based smart manufacturing as an eight tuple of CPS,IoT, IoS and IoCK as elements. Further, the paper extends the eight-tuple CPS-based manufacturing to social-CPS (SCPS)-based manufacturing, termed wisdom manufacturing, which forms a nine tuple with the addition of one more element, the IoP and which is based on the SCPS instead of CPS. Both architectures and characteristics for smart and wisdom manufacturing are addressed. As such, these terms' linkages are established and relations are clarified with a special discussion. This study thus contributes as a theoretical basis and as a comprehensive framework for emerging manufacturing integration. [ABSTRACT FROM AUTHOR]

This paper studies neural network-based tracking control of underactuated systems with unknown parameters and with matched and mismatched disturbances. Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances. Novel auxiliary control variables are designed to establish the controllability of the non-collocated subset of the underactuated systems. The approximation errors and the matched and mismatched external disturbances are efficiently counteracted by appropriate design of robust compensators. Stability and convergence of the time-varying reference trajectory are shown in the sense of Lyapunov. The parameter updating laws for the designed control schemes are derived using the projection approach to reduce the tracking error as small as desired. Unknown dynamics of the non-collocated subset is approximated through neural networks within a local region. Finally, simulation studies on an underactuated manipulator and an underactuated vibro-driven system are conducted to verify the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This study explores important factors of assistive technology (AT) and evaluates their relative impact on AT effectiveness and retention. Questionnaire based survey is used for data collection from 327 people with dementia (PWD). This empirical study uses statistical techniques including exploratory factor analysis for factor identification, linear regression for impact study, Kruskal Wallis H and Mann Whitney U tests for the statistical significant study in terms of demographic and characteristics. The exploratory factor analysis results into 11 factors: operational support, physical support, psychological support, social support, cultural match, reduced external help, affordability, travel help, compatibility, effectiveness and retention. The results reveal that social support, psychological support and travel help and reduced external help strongly impact on AT effectiveness and retention. The users are motivated by socialization support through using AT without any external help. The use of AT during travelling improves confidence of the PWD and helps them psychologically. [ABSTRACT FROM AUTHOR]

Despite the acknowledged importance of knowledge management (KM), many employees avoid practicing KM at the individual level. This avoidance often leads to loss of the intellectual capital due to employee turnover. Many potential behavioural remedies are still untapped in the existing literature. This study extends the construct of knowledge oriented leadership (KOL) and examines its role in predicting KM behaviour among employees of the hospitality sector, at the individual level. It also investigates the mediating effect of employee work attitudes including affective commitment, creative self-efficacy and work engagement, by using partial least square for structure equation modelling. This study finds that KOL positively affects KM behaviour, affective commitment, creative self-efficacy and employee work engagement. Work attitudes (affective commitment, creative self-efficacy, work engagement) mediate the relationship of KOL and KM behaviour. Furthermore the direct positive effect of employee affective commitment, creative self-efficacy and work engagement on KM behaviour is also significant. This study contributes to the body of knowledge by extending the construct of KOL, and by investigating the connection between KOL, work attitudes, and KM behaviour among hospitality employees, at the individual level. [ABSTRACT FROM AUTHOR]

Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using the Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two inertial measurement unit sensors through Bluetooth connectivity. The data is collected from 12 young (ten for modeling and two for validation) and 20 older subjects. We analyze the data collected from real world for stride, step, stance, and swing gait features. We validate our method with the measurements of gait features. The generalized Procrustes analysis is used to estimate a standard normal mean gait shape (NMGS) for ten young subjects. Each gait feature of both young and older subjects is then converted to find the best match with the NMGS using the ordinary Procrustes analysis. The shape distance between the NMGS and each gait shape is estimated using Riemannian shape distance, Riemannian size-and-shape distance, Procrustes size-and-shape distance, and root-mean-square deviation. A t-test is performed to provide statistical evidence of gait shape differences between young and older gaits. A mean form, which is considered as a standard normal mean gait form (NMGF), and inter-feature distances are estimated from the set of ten young subjects. The form difference is estimated between the NMGF and individual gaits of young and older. The degree of abnormality is then estimated for individual features and the result is plotted to visualize the feature in a gait. Experimental results demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The link quality protocols of the IEEE 802.15.4 show low performance in terms of reliability in a hospital environment. To achieve the optimal performance of these protocols, this paper proposes a fuzzy logic-based solution using a detailed empirical reliability assessment. This paper provides a solution through four steps: 1) identifying the suitable IEEE 802.15.4 protocols and validating their suitability for patient monitoring systems through simulation in Castalia 3.2 with the OMNet++ platform; 2) providing a detailed discussion and analysis regarding link quality mechanisms used; 3) creating a real-time test bed to perform the empirical experiments to find the actual link quality estimation for different hospital environments; and 4) proposing a fuzzy logic system (FLS), which maps the results of empirical experiments with the proposed FLS to obtain the optimal results. For empirical experiments, we divide the communication in the hospital into four environmental scenarios, including inside ward, corridor, ward to corridor, and ward to ward. Both mobile and static scenarios are considered with line of sight and non-line of sight. Different link quality threshold values for link quality indicator (LQI) and received signal strength indicator (RSSI) are found for the variety of hospital scenarios. A strong correlation is noticed between LQI and packet reception rate in most of the scenarios, whereas a weak correlation is found between RSSI and packet reception rate. Finally, the results are mapped to our proposed FLS to obtain the optimal performance. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic state feedback control problem for a class of interconnected large-scale switched high-order nonlinear systems with unknown control direction and time-varying time-delay. The adaptive laws are designed to estimate the bounds of switched parameters under arbitrary switching for subsystems. The Nussbaum function is used to deal with the unknown control direction problem. By combining the backstepping and homogeneous domination technique, the decentralised adaptive control strategies are developed and the resulting closed-loop system is asymptotically stable. Finally, a simulation example is given and the results show the effectiveness of the proposed control design method. [ABSTRACT FROM AUTHOR]

This paper studies the distributed adaptive fuzzy containment tracking control for a class of high-order stochastic pure-feedback nonlinear multiagent systems with multiple dynamic leaders and performance constraint requirement. The control inputs are quantized by hysteresis quantizers. Mean value theorems are used to transfer the nonaffine systems into affine forms and a nonlinear decomposition is employed to solve the quantized input control problem. With a novel structure barrier Lyapunov function, the distributed control strategy is developed. It is strictly proved that the outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders, the containment tracking errors satisfy the performance constraint requirement and the resulting leader-following multiagent system is stable in probability based on Lyapunov stability theory. At last, simulation is provided to show the validity and the advantages of the proposed techniques. [ABSTRACT FROM AUTHOR]

This paper studies the nonlinear dynamics of a two-degree-of-freedom vibro-driven capsule system. The capsule is capable of rectilinear locomotion benefiting from the periodic motion of the driving pendulum and the sliding friction between the capsule and the environmental surface in contact. Primary attentions are devoted to the dynamic analysis of the motion and stick-slip effect of the capsule system. Following a modal decoupling procedure, a profile of periodic responses is obtained. Subsequently, this work emphasizes the influences of elasticity and viscosity on the dynamic responses in a mobile system, whose implicit qualitative properties are identified using bifurcation diagrams and Poincaré sections. A locomotion-performance index is proposed and evaluated to identify the optimal viscoelastic parameters. It is found that the dynamic behaviour of the capsule system is mainly periodic, and the desired forward motion of the capsule can be achieved through optimal selection of the elasticity and viscosity coefficients. In view of the stick-slip motion, the critical equilibrium and its dynamic behaviours, different regions of oscillations of the driving pendulum are identified, with the attention focusing on the critical region where linearities are absent and nonlinearities dominate the dynamic behaviour of the pendulum. The conditions for stick-slip motions to achieve a pure forward motion are investigated. The proposed approach can be adopted in designing and selecting of suitable operating parameters for vibro-driven or joint-actuated mechanical systems. [ABSTRACT FROM AUTHOR]

This paper studies the issue of adaptive trajectory tracking control for an underactuated vibro-driven capsule system and presents a novel motion generation framework. In this framework, feasible motion trajectory is derived through investigating dynamic constraints and kernel control indexes that underlie the underactuated dynamics. Due to the underactuated nature of the capsule system, the global motion dynamics cannot be directly controlled. The main objective of optimization is to indirectly control the friction-induced stick-slip motions to reshape the passive dynamics and, by doing so, to obtain optimal system performance in terms of average speed and energy efficacy. Two tracking control schemes are designed using a closed-loop feedback linearization approach and an adaptive variable structure control method with an auxiliary control variable, respectively. The reference model is accurately matched in a finite-time horizon. The key point is to define an exogenous state variable whose dynamics is employed as a control input. The tracking performance and system stability are investigated through rigorous theoretic analysis. Extensive simulation studies are conducted to demonstrate the effectiveness and feasibility of the developed trajectory model and optimized adaptive control system. [ABSTRACT FROM AUTHOR]

Motivated by the desire to optimally control the friction-induced stick-slip locomotion and sufficiently improve the energy efficacy, a novel trajectory synthesis and optimization scheme is proposed in this paper for a underactuated microrobotic system with dynamic constraints and couplings. The nonlinear microrobotic model utilizes combined tangential-wise and normal-wise vibrations for underactuated locomotion, which features a generic significance for the studies on microrobotic systems. Specifically, an analytical two-stage velocity trajectory is constructed under control indexes and physical constraints. Subsequently, the dynamic coupling behavior and the qualitative variation laws are characterized through rigorous bifurcation analysis. The synthesized trajectory is optimized and tuned via rigorous analysis based on the robot dynamics. The proposed trajectory planning mechanism provides a promising approach in determining the optimal viscoelastic parameters and trajectory parameters such that the optimal locomotion indexes can be met. Simulation results are presented to demonstrate the efficacy and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

Sensor-based human activity recognition (HAR) has received considerable attention due to its wide applications in health care. Each sensor modality has its advantages and limitations. Single sensor modalities sometimes may not cope with complex situations in practice. To resolve this challenge, we design and develop a practical hybrid sensory HAR system for older people. To enhance the performance of the system, we propose a unique data fusion method through combining both wearable sensors and ambient sensors. The wearable sensors in this paper are used for identifying the specific daily activities. The ambient sensors delivering the occupant’s room-level daily routine provide a more comprehensive surveillance with the wearable sensors together; meanwhile, the captured room-level location information is also used in the data fusion to trigger the sub classification models pretrained by wearable data. We also explore a new feature set extracted from wearable sensors to improve the system performance. We experimentally evaluate our system by applying four typical mutual information-based feature selection methods and the support vector machines classification algorithm instead of other complex algorithms, with the aim of exploring a practical way to improve recognition accuracy. The ground-truth data are gathered from 21 subjects, including 17 daily activities with the sample size of 2,142,000. The experimental results demonstrate the effectiveness of our method. The new feature set help improve the accuracy to 96.82% ± 0.15 from 89.81% ± 0.54 using wearable data only; and the data fusion with ambient information achieves a further increased accuracy of 98.32%. [ABSTRACT FROM AUTHOR]

Our aim is to maximize the interpretable information for gait analysis. To achieve this, it is important to find the optimal sensor placement and the parameters that influence the extraction of automatic gait features. We investigated the effect of different anatomical foot locations on inertial measurement unit (IMU) sensor output. We designed and developed an android app to collect real-time synchronous sensor output. We selected a set of five anatomical foot locations covering most of the foot regions to place wearable IMU sensors for data collection. Each participant performed a trial in a straight corridor comprising 25 strides of normal walking, a turn-around, and another 25 strides. We proposed an automatic gait features extraction method to analyze the data for stride number, distance, speed, length and period of stride, stance, and swing phases during walking. The highest accuracy for detecting stride number was in location 1 (first cuneiform) followed by location 5 (Achilles Tendon) and 4 (Talus). Location 1 was the closest to correlate estimate to the measured distance travelled. The accuracy of detecting number of strides on average is 95.47% from accelerometer data and 93.60% from gyroscope data and closest to the 60:40% split for average stance and swing for 15 subjects. To validate our results, we conducted trials using the Qualisys motion capture instrument and from our sensors concurrently. The average accuracy of the result is 97.77% with 95% confidence interval 0.767 for estimated and 99.01% with 95% confidence interval 0.266 for period. [ABSTRACT FROM PUBLISHER]

This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment. [ABSTRACT FROM AUTHOR]

Recently, adaptive duty cycle-based medium access control (MAC) protocols based on the IEEE 802.15.4 standard have been proposed to address the quality of service (QoS) requirements, including a specified and combined set of time bounded data transmission services, data rate, reliability, and energy consumption for wireless body area sensor networks. However, the current protocols suffer limitations in the context of providing a combined set of QoS. Moreover, for efficient energy consumption, these protocols adjust duty cycle values on estimations, such as active periods, buffer occupancy, and collision rates. These estimations consume resources in terms of delay and energy, which makes them hardly acceptable for medical applications. In this context, we propose a tele-medicine protocol (TMP) under the IEEE 802.15.4 slotted CSMA/CA with beacon enabled mode on the basis of a novel idea which combines two optimizations methods, i.e., MAC layer parameter tuning optimization and duty cycle optimization. The TMP duty cycle is adjusted by three factors: offered network traffic load, delay-reliability factor, and superframe duration. The proposed protocol provides the required set of QoS (delay, reliability, and efficient energy consumption) at the same time for patient monitoring applications. Performance of the proposed protocol is computed on the basis of average end-to-end delay, reliability, packet delivery ratio, collision rate, and energy consumption by varying the number of nodes and offered network traffic. The TMP performed well in terms of delay, reliability, energy consumption, and collision rate in comparison of the other existing protocols under the constraints of patient monitoring applications. [ABSTRACT FROM PUBLISHER]

Background This study presents an overview of recent research activities in assistive technology ( AT) for people with dementia. Bibliometric studies are used to explore breadth and depth of different research areas, yet this method has not yet been fully utilised in AT research for people with dementia. Methods The bibliometric method was used for collecting studies related to AT. Based on inclusion/exclusion criteria, the AT studies with a focus on people with dementia are considered. Study Scope The study is based on factors such as number of publications, citations per paper, collaborative research output, P-Index, major research and application areas and national dementia strategies. Data Collection Data were collected from 2000 to 2014 in AT research. The top 10 countries are selected based on their research outputs. Results USA emerged as the leading contributor with 503 publications and an annual growth rate of 16%, followed by UK with 399 publications and growth rate of 22%. Germany with 101 publications is on the 6th place, but it has a higher citation rate 16.43% as compared to USA (13.34%). Although all 10 countries show good collaborative research output, Italy, Spain and the Netherlands emerge as top collaborative research contributors with high percentages (84%, 84% and 79%). All the top 10 countries, except Canada, Germany and Spain, have national dementia strategies in place. Conclusion The overall analysis shows that USA and UK are working extensively in AT research for people with dementia. Both these countries also have well established national dementia strategies. [ABSTRACT FROM AUTHOR]

The recent developments in technology have made noteworthy positive impacts on the human-computer interaction (HCI). It is now possible to interact with computers using voice commands, touchscreen, eye movement, hand gesture, etc. This paper compiles some of the innovative HCI progresses in various areas, e.g., specialised input/output devices, virtual or augmented reality, wearable technology, etc. It also identifies some future research directions. [ABSTRACT FROM AUTHOR]

Many studies use surface electromyogram (sEMG) signals to estimate the joint angle, for control of upper-limb exoskeletons and prostheses. However, several practical factors still affect its clinical applicability. One of these factors is the load variation during daily use. This paper demonstrates that the load variation can have a substantial impact on performance of elbow angle estimation. This impact leads an increase in mean RMSE (Root-Mean-Square Error) from 7.86^\circ to 20.44^\circ in our experimental test. Therefore, we propose three methods to address this issue: 1) pooling the training data from all loads together to form the pooled training data for the training model; 2) adding the measured load value (force sensor) as an additional input; and 3) developing a two-step hybrid estimation approach based on load and sEMG. Experiments are conducted with five subjects to investigate the feasibility of the proposed three methods. The results show that the mean RMSE is reduced from 20.44^\circ to 13.54^\circ using method one, 10.47^\circ using method two, and 8.48^\circ using method three, respectively. Our study indicates that 1) the proposed methods can improve performance and stability on joint angle estimation and 2) sensor fusion (sEMG sensor and force sensor) is an efficient way to resolve the adverse effect of load variation. [ABSTRACT FROM AUTHOR]