A novel algorithm which combined the merits of the Clustering strategy and the Compressive Sensing-based (CS-based) scheme was proposed in this paper. The lemmas for the relationship between any two adjacent layers, the optimal size of clusters, the optimal distribution of the Cluster Head (CH) and the corresponding proofs were presented firstly. In addition, to alleviate the "Hot Spot Problem" and reduce the energy consumption resulted from the rotation of the role of CHs, a third role of Backup Cluster Head (BCH) as well as the corresponding mechanism to rotate the roles between the CH and BCH were proposed. Subsequently, the Energy-Efficient Compressive Sensing-based clustering Routing (EECSR) protocol was presented in detail. Finally, extensive simulation experiments were conducted to evaluate its energy performance. Comparisons with the existing clustering algorithms and the CS-based algorithm verified the effect of EECSR on improving the energy efficiency and extending the lifespan of WSNs.
Abstract-Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.
Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power to weight ratio features. However, the nonlinearity and time-varying nature of PAMs makes it challenging to maintain highperformance tracking control. In this paper, a High-Order Pseudo-Partial Derivative based Model-Free Adaptive Iterative Learning Controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations, meanwhile, a high-order estimation algorithm is designed to estimate the pseudo-partial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller is verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFA ILC can track the desired trajectory with improved convergence and tracking performance. Index Terms-Pneumatic artificial muscle, model-free adaptive control, iterative learning control, convergence. I. INTRODUCTION NEUMATIC artificial muscle (PAM) is a tube-like actuator that largely mimics biological human muscle functions [1]. Compared to traditional electrical motors and hydraulic actuators, the lightweight, high compliance and high power-to-weight ratio of PAMs [2] have fueled their popularity among assistive exoskeletons and rehabilitation robots, such as the upper limb exoskeleton series RUPERT [3] and the lower limb orthotics KAFO [4]. However, unlike the conventional actuators adopted in Lokomat [5] and ArmeoPower [6], the nonlinear and time-varying nature of PAMs may cause Manuscript
Motor system uses muscle synergies as a modular organization to simplify the control of movements. Motor cortical impairments, such as stroke and spinal cord injuries, disrupt the orchestration of the muscle synergies and result in abnormal movements. In this paper, the alterations of muscle synergies in subacute stroke survivors were examined during the voluntary reaching movement. We collected electromyographic (EMG) data from 35 stroke survivors, ranging from Brunnstrom Stage III to VI, and 25 age-matched control subjects. Muscle synergies were extracted from the activity of 7 upper-limb muscles via nonnegative matrix factorization under the criterion of 95% variance accounted for. By comparing the structure of muscle synergies and the similarity of activation coefficients across groups, we can validate the increasing activation of pectoralis major muscle and the decreasing activation of elbow extensor of triceps in stroke groups. Furthermore, the similarity of muscle synergies was significantly correlated with the Brunnstrom Stage (R = 0.52, p < 0.01). The synergies of stroke survivors at Brunnstrom Stage IV–III gradually diverged from those of control group, but the activation coefficients remained the same after stroke, irrespective of the recovery level.
Human body orientation estimation from microinertial/magnetic sensor units is highly important for synthetic environments, robotics, and other human-computer interaction applications. In practice, the main challenge is how to deal with linear acceleration interference and magnetic disturbance which always cause significant attitude-estimation errors. In this paper, we present a novel quaternion-based Kalman filter with vector selection scheme for accurate human body orientation estimation using an inertial/magnetic sensor unit. In the proposed algorithm, the gyroscope measurement is used as an input to construct the linear process equation, and the accelerometer and magnetometer measurements are manipulated to establish the linear pseudomeasurement equation. A linear Kalman filter is then deployed to estimate the body orientation. In the Kalman filter framework, a vector selection scheme is designed to protect the algorithm against undesirable conditions such as temporary intensive movement and magnetic disturbance and enable it to acquire more accurate orientation estimation. The experimental results have shown that the proposed algorithm can provide accurate attitude estimations with regard to the ground truth.
Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNN-LSTM hybrid model to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN, LSTM and several representative machine learning approaches, particularly when complex wrist movements are activated. Index Terms-sEMG, wrist kinematics estimation, deep learning, convolutional neural network, long short-term memory network, hybrid model.
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