The scarcity of high-quality surface electromyography (sEMG) data, caused by ethical constraints, privacy concerns, and noise interference, poses significant challenges for developing robust deep learning models in sEMG analysis. Multi-channel sEMG signals exhibit complex inter-channel correlations reflecting neuromuscular coordination. However, existing generative methods suffer from error accumulation in sequential channel generation, insufficient inter-channel relationship modeling, and lack of physiological constraints, producing data-driven valid but physiologically implausible signals that compromise biological fidelity for clinical applications. To address these fundamental limitations, we propose a Muscle Synergy-Constrained Conditional GAN (MS-cGAN) framework that simultaneously generates multi-channel sEMG signals while preserving bio-mechanical fidelity. Firstly, A novel Graph Convolutional Network (GCN)-based generator architecture specifically tailored for sparse sEMG signals, which enables the generator to capture and model complex inter-channel relationship features through graph-based representation learning, thereby circumventing error accumulation issues by leveraging the inherent inter-channel correlations. Secondly, Integration of Muscle Synergy (MS) prior constraints as dynamic loss functions based on MS theory, which enforces generator optimization within a physiologically plausible parameter space and ensures signals maintain synergistic consistency with underlying physiological mechanisms. Lastly, experiments on IRASS datasets and public datasets (NinaPro DB1 and DB2) demonstrate that MS-cGAN significantly improves signal authenticity and enhances downstream task performance compared to traditional GANs and state-of-the-art diffusion models. The generated data effectively supplement scarce sEMG datasets and improve kinematic prediction precision for deep learning models.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.

The key to achieving assist-as-needed (AAN) control in rehabilitation robots lies in accurately predicting patient motion intentions. This study, for the first time, redefines motion intention prediction from the perspective of sequence-to-sequence translation by analogizing sEMG signals and joint angles to the source language and target language, respectively. The proposed 3DCNN-TF model achieves precise translation of neural control signals into kinematic representations. This model comprises three modules: an sEMG “sentence” generation module that compiles multiple sEMG sliding windows into a “sentence,” a 3DCNN module based on muscle anatomy and electrode placement to extract muscle synergy features from each “word” in the “sentence,” and a Transformer (TF) module that autoregressively generates the next joint angle as the translation result. Experimental results indicate that the 3DCNN-TF model achieves superior overall performance compared to eight baseline models and existing studies in continuously predicting wrist and knee flexion/extension angles across varying speeds. Moreover, the 3DCNN-TF achieves an optimal balance between prediction accuracy and computational efficiency while exhibiting exceptional robustness and generalizability. Specifically, the 3DCNN-TF achieves average nRMSE and R2values of (6.2%/95.5%) and (5.5%/96.2%) on wrist and knee datasets, respectively, with an average training time of less than two minutes. Additionally, the 3DCNN-TF can predict joint angles up to 300 ms in advance without compromising accuracy, which is critical for real-time AAN control in rehabilitation robots.

Accurately decoding human motion intentions from surface electromyography (sEMG) is essential for myoelectric control and has wide applications in rehabilitation robotics and assistive technologies. However, existing sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate, or purely data-driven models that lack physiological consistency. This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning, thereby preserving physiological consistency while achieving accurate motion estimation. The PENN employs a recursive temporal structure to propagate historical estimates and a lightweight convolutional neural network for residual correction, leading to robust and temporally coherent estimations. A two-phase training strategy is designed for PENN. Experimental evaluations on six healthy subjects show that PENN outperforms state-of-the-art baseline methods in both root mean square error (RMSE) and R2metrics.

Optimizing the feature representation and decoding efficiency of the steady-state visual evoked potentials (SSVEP) is critical to enhance the performance of neural signal decoding systems. Current deep learning models usually overlook the physical topological information of EEG channels, resulting in suboptimal feature extraction and limited recognition performance. To address these challenges, this study proposes a synergistically designed SSVEP recognition framework to alleviate data insufficiency, improve the feature representation, and enhance decoding efficiency. Specifically, a slicing-and-scaling technique is adopted to improve the model generalization under limited-sample scenarios. A graph-based spatial filter leverages the topological relationships among EEG channels to suppress redundant information and enhance spatial feature quality. A lightweight convolutional neural network (CNN) with fewer parameters is developed to efficiently extract discriminative temporal–spatial features for accurate SSVEP classification. Experimental results on two public benchmark datasets and one self-collected dataset demonstrate that the proposed framework outperforms baseline deep learning models, yielding improvements of at least 6.8 %, 8.5 %, and 0.5 % in peak average classification accuracy, respectively. The maximum average information transfer rates (ITRs) achieved on the three datasets were 221.4 ,106.7 , and 133.9 , respectively. By simultaneously reducing model complexity and improving decoding performance, the proposed framework offers an effective and promising approach for efficient neural signal decoding in SSVEP recognition.

In rehabilitation applications, encouraging patients to actively participate in training is essential for effective recovery. However, personalized control design in robot-assisted therapy remains challenging due to variations in patients' motor capabilities. To address this issue, this paper proposes an assist-as-needed (AAN) control framework that integrates a hybrid fuzzy-transformer neural network (HFTN) with a fuzzy echo state network (FESN)-based variable impedance controller to ensure personalized support and active engagement. The HFTN integrates fuzzy logic with transformer architectures in parallel paths, establishing a novel neuro-fuzzy musculoskeletal (MSK) model that maps surface electromyography (sEMG) signals to joint torque through combined uncertainty and temporal modeling for enhanced real-time estimation. The variable impedance controller constructs the stiffness and damping matrices of the robotic system through the FESN and develops an adaptive update law for the FESN output weights, effectively addressing instability issues in variable stiffness control. Furthermore, driven by physiologically estimated joint torques from the HFTN, the adaption of the FESN reservoir states enables real-time modulation of stiffness and damping, facilitating transitions between human-dominated and robot-dominated modes. This realizes the AAN concept, ensuring personalized and responsive assistance. Various experiments on an upper limb rehabilitation robot were conducted to validate the effectiveness of both the neuro-fuzzy MSK model and the AAN controller in delivering optimal assistance while promoting active user participation.

Post-stroke upper limb dysfunction severely impacts patients' daily life quality. Utilizing sEMG signals to predict patients' motion intentions enables more effective rehabilitation by precisely adjusting the assistance level of rehabilitation robots. Employing the muscle synergy (MS) features can establish more accurate and robust mappings between sEMG and motion intentions. However, traditional matrix factorization algorithms based on blind source separation still exhibit certain limitations in extracting MS features. This paper proposes four deep learning models to extract MS features from four distinct perspectives: spatiotemporal convolutional kernels, compression and reconstruction of sEMG, graph topological structure, and the anatomy of target muscles. Among these models, the one based on 3DCNN predicts motion intentions from the muscle anatomy perspective for the first time. It reconstructs 1D sEMG samples collected at each time point into 2D sEMG frames based on the anatomical distribution of target muscles and sEMG electrode placement. These 2D frames are then stacked as video segments and input into 3DCNN for MS feature extraction. Experimental results on both our wrist motion dataset and public Ninapro DB2 dataset demonstrate that the proposed 3DCNN model outperforms other models in terms of prediction accuracy, robustness, training efficiency, and MS feature extraction for continuous prediction of wrist flexion/extension angles. Specifically, the average nRMSE and R2values of 3DCNN on these two datasets are (0.14/0.93) and (0.04/0.95), respectively. Furthermore, compared to existing studies, the 3DCNN outperforms musculoskeletal models based on direct collocation optimization, physics-informed GANs, and CNN-LSTM-based deep Kalman filter models when evaluated on our dataset.

Joint injuries, and their long-term consequences, present a substantial global health burden. Wearable prophylactic braces are an attractive potential solution to reduce the incidence of joint injuries by limiting joint movements that are related to injury risk. Given human motion and ground reaction forces, we present a computational framework that enables the design of personalized braces by optimizing the distribution of microstructures and elasticity. As varied brace designs yield different reaction forces that influence kinematics and kinetics analysis outcomes, the optimization process is formulated as a differentiable end-to-end pipeline in which the design domain of microstructure distribution is parameterized onto a neural network. The optimized distribution of microstructures is obtained via a self-learning process to determine the network coefficients according to a carefully designed set of losses and the integrated biomechanical and physical analyses. Since knees and ankles are the most commonly injured joints, we demonstrate the effectiveness of our pipeline by designing, fabricating, and testing prophylactic braces for the knee and ankle to prevent potentially harmful joint movements.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Recent advances in WiFi-based device-free localization (DFL) mainly focus on stationary scenarios and ignore the environmental dynamics, hindering the large-scale implementation of the DFL technique. In order to enhance the localization performance in nonstationary environments, in this article, a novel multidomain collaborative extreme learning machine (MC-ELM)-based DFL framework is proposed. Specifically, the whole environment is first divided into several subdomains depending on the distributions of the collected data using a clustering algorithm, and a corresponding number of local DFL models are then built to represent these subdomains separately. Finally, a global DFL model is achieved by seamlessly integrating all the local DFL models in a global optimization manner. The created MC-ELM-based DFL model also can be incrementally updated with sequentially coming data without retraining to track the environmental dynamics. Extensive experiments in several indoor environments demonstrate the robustness and generalization of the proposed MC-ELM-based DFL framework.

Brain-computer interface (BCI) can provide a direct communication path between the human brain and an external device. The steady-state visual evoked potential (SSVEP)-based BCI has been widely explored in the past decades due to its high signal-to-noise ratio and fast communication rate. Several spatial filtering methods have been developed for frequency detection. However the temporal knowledge contained in the SSVEP signal is not effectively utilized. In this study, we propose a canonical correlation analysis (CCA)-based spatio-temporal filtering method to improve target classification. The training signal and two types of template signals (i.e. individual template and artificial sine-cosine reference) are first augmented via temporal information. Three sets of augmented data are then concatenated by trials. The CCA is performed twice, between the newly obtained training data and each template. The trained four spatial filters can be applied in the following test process. A public benchmark dataset was used to evaluate the performance of the proposed method and the other three comparing methods, such as CCA, MsetCCA, and TRCA. The experimental results indicate that the proposed method yields significantly higher performance. This paper also explored the effects of the number of electrodes and training blocks on classification accuracy. The results further demonstrated the effectiveness of the proposed method in SSVEP detection.

Abstract Energy inequality caused by the process of cluster head election has a large influence on energy efficiency and the network lifetime of wireless sensor networks (WSNs). To this end, a novel concept of EI ec is proposed to evaluate the equality degree of energy consumption. Related theorems for establishing the candidate set of cluster heads are proposed, with the aim of promoting energy equality in each cluster. Subsequently, a novel energy‐efficiency‐adaptive cluster formation mechanism based on economic (ECFE) theory is proposed and detailed. Finally, extensive experiments are carried out to assess its energy efficiency and the network performance by comparisons with the existing classic and latest intelligent clustering algorithms. The results indicate that ECFE improves not only the energy efficiency but also the network performance effectively.

Convolutional neural networks (CNNs) have been widely utilized to identify hand gestures from surface electromyography (sEMG) signals. However, due to the nonstationary characteristics of sEMG, the classification accuracy usually degrades significantly in the daily living environment involving complex hand movements. To further improve the reliability of a classifier, unconfident classifications are expected to be identified and rejected. In this study, we propose a novel approach to estimate the probability of correctness for each classification. Specifically, a confidence estimation model is established to generate confidence scores (ConfScore) based on posterior probabilities of CNN, and an objective function is designed to train the parameters of this model. In addition, a comprehensive metric that combines the true acceptance rate (TAR) and the true rejection rate (TRR) is proposed to evaluate the rejection performance of ConfScore, so that the tradeoff between system security and control lag could be fully considered. The effectiveness of ConfScore is verified using data from public databases and our online platform. The experimental results illustrate that ConfScore can better reflect the correctness of CNN classifications than traditional confidence features, i.e., maximum posterior probability and entropy of the probability vector. Moreover, the rejection performance is observed to be less sensitive to variations in rejection thresholds.

To develop multi-functionalhuman-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.

Data-driven spatial filtering approaches have been widely used for steady-state visual evoked potentials (SSVEPs) detection toward the brain-computer interface (BCI). The existing methods tend to learn the spatial filter parameters for a certain stimulation frequency only using the training trials from the same stimulus, which may ignore the information from the other stimuli. In this paper, we propose a novel multi-objective optimisation-based spatial filtering method for enhancing SSVEP recognition. Spatial filters are defined via maximising the correlation among the training data from the same stimulus whilst minimising the correlation from different stimuli. We collected SSVEP signals using 16 electrodes from six healthy subjects at 4 different stimulation frequencies: 14Hz, 15Hz, 16Hz, and 17Hz. The experimental study was implemented, and our method can achieve an average recognition accuracy of 94.17%, which illustrates its effectiveness.

In this paper, we propose a novel iterative learning control (ILC) scheme for precise state tracking of pneumatic muscle (PM) actuators. Two critical issues are considered in our scheme: 1) state constraints on PM position and velocity; 2) uncertainties of the PM model. Based on the three-element form, a PM model is constructed that takes both parametric and nonparametric uncertainties into consideration. By introducing the composite energy function (CEF) approach incorporated with a barrier Lyapunov function (BLF), full state constraints of PM will not be violated and uncertainties are effectively compensated. Through rigorous analysis, we show that under proposed ILC scheme, uniform convergence of PM state tracking errors are guaranteed. Simulation results validate the performance of the proposed scheme.

EMG-driven musculoskeletal model has been broadly used to detect human intention in rehabilitation robots. This approach computes muscle-tendon force and translates it to the joint kinematics. However, the muscle-tendon parameters of the musculoskeletal model are difficult to measure in vivo and varied across subjects. In this study, a direct collocation (DC) method is proposed to optimize the subject-specific parameters in a wrist musculoskeletal model. The resultant optimized parameters are used to estimate the wrist flexion/extension motion. The estimation performance is compared with the parameters optimized by the genetic algorithm. Experiment results show that the DC methods have a similar performance compared with GA, in which the mean correlation are 0.96 and 0.93 for the genetic algorithm and DC method respectively. But the direction collocation method requires less optimization time.

In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in an energy-efficient state. In the proposed scheme, production indicators prediction models are first developed using a kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multiobjective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of the approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which an improved association rule mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out.

In the past decades, classical machine learning (ML) methods have been widely investigated in wrist kinematics estimation for the control of prosthetic hands. Currently deeper structures have shown great potential to further improve prediction accuracy. In this paper we present a single stream convolutional neural network (CNN) for mapping surface electromyography (sEMG) to wrist angles within three degrees-of-freedom (DOFs). Two types of two dimensional (2D) sEMG images are constructed in time domain and spectrum as CNN inputs, respectively. Six typical linear and nonlinear ML models are implemented for comparison, where four efficient time-spatial hand-crafted features are extracted to represent feature engineering. Experiment results with four able-bodied participants illustrate that CNN with 2D spectrum sEMG images can achieve highest accuracy in most testing sessions. In other sessions, it is still competitive to the most promising ML techniques. The core strength of deep learning (DL), i.e. feature learning via deep structures and efficient algorithms, is verified to be more powerful than classical feature engineering, particularly in smaller datasets.

Based on an EMG-adjusted method in neuromusculoskeletal model, this study aims to predict the individual muscle force in shoulder and elbow during table tennis strokes. Muscle force estimation makes muscle activation analysis more physiological in sports. Twenty subjects, divided into professional group and amateur group, were adopted in this study. They were asked to do a basic stoke motion: backhand block. Surface electromyography (sEMG) of nine muscles was recorded, as well as the motion data collected by three inertial sensors. A Hill-type musculotendon model was then adopted to estimate individual muscle force by combining adjusted sEMG and motion data. The result shows that the method can estimate individual muscle force during table tennis strokes accurately, and the two groups show significant difference in muscle force of shoulders and elbows.

The participant's reputation is vital to improve the quality of service for Mobile Crowd Sensing (MCS). A multidimensional reputation evaluation model was proposed in this paper to evaluate the participant's reputation more objectively. Different from the existing strategies, the service delay and the count of the successful as well as the failed transactions were additionally utilized to evaluate the participant's reputation. An algorithm based on Analytic Hierarchical Process (AHP) was presented to establish the reputation evaluation weight matrix. Besides, a fuzzy logic based mechanism was proposed to normalize the value of the four criteria and a dual-threshold mechanism was designed to achieve admission control more properly. Finally, extensive simulations were conducted and the simulation results confirmed the effectiveness of the reputation evaluation model.

Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors.

Ambulatory blood pressure is critical in predicting some major cardiovascular events; therefore, cuff-less and noninvasive beat-to-beat ambulatory blood pressure measurement is of great significance. Machine-learning methods have shown the potential to derive the relationship between physiological signal features and ABP. In this paper, we apply random forest method to systematically explorer the inherent connections between photoplethysmography signal, electrocardiogram signal and ambulatory blood pressure. To archive this goal, 18 features were extracted from PPG and ECG signals. Several models with most significant features as inputs and beat-to-beat ABP as outputs were trained and tested on data from the Multi-Parameter Intelligent Monitoring in Intensive Care II database. Results indicate that compared with the common pulse transit time method, the RF method gives a better performance for one-hour continuous estimation of diastolic blood pressure and systolic blood pressure under both the Association for the Advancement of Medical Instrumentation and British Hyper-tension Society standard.

Bioelectrical signal analysis is gaining significant interests from both academics and industries due to its capability for improved diagnosis and therapy of chronic diseases. In practice, different bio-signals, such as EEG, ECG, EOG and EMG, are usually contaminating each other, and the measured signal is the linear combination of them. It is critical to separate them since analysis of one type or several of them separately is of more interest. In the case of multichannel recording, several blind source separation methods are available to extract its original components. However, for single channel scenarios, the problem has yet to be well studied. Therefore in this paper, we explore blind source separation and artefact cancellation for a single channel signal by combining signal decomposition method singular spectrum analysis (SSA) with different blind source separation methods, such as principal component analysis (PCA), maximum noise fraction (MNF), independent component analysis (ICA) and canonical correlation analysis (CCA). We also systematically compare the separation performance by combing different decomposition methods (wavelet transform (WT), ensemble empirical mode decomposition (EEMD) and SSA) with blind source separation methods (PCA, MNF ICA and CCA). The good simulation results have demonstrated the effectiveness and efficiency of the proposed method.

Sensor-to-segment calibration is a critical step for motion reconstruction from inertial and magnetic measurement units (IMMUs). In this paper, a novel sensor-to-segment calibration protocol is proposed. The protocol consists of three stages that allow for in situ calibration. After the sensor units are attached to the body, predefined postures and movements are used for sensor calibration. Acceleration and angular velocity measurements are used to estimate axes of functional frame (FF) by Principal Component Analysis (PCA). Finally, Levenberg-Marquardt optimization is used to identify rotation matrices between the expected FF and their estimations with respect to the sensor frame. Validation of the method demonstrates its practical value and how the proposed protocol reduces the extent of cross-talk for evaluating joint kinematics.

Quality of sleep is an important index of wellbeing and health. Irregular sleep patterns are often associated with stress and disorders such as cardiovascular disease, diabetes, depression, sleep apnea and obesity. In addition to key physiological indices, body movements and posture during sleep are also important for assessing causal relationship of irregular sleep patterns and underlying health issues. In this paper, we explore the feasibility of using a single accelerometer strapped onto the chest to detect posture and cardio-respiratory parameters during sleep. An efficient movement detector suitable for on-node implementation is developed to distinguish static postures from dynamics movements. When in static postures, a linear discriminant analysis (IDA) classifier is used to further divide the static postures into four common sleeping positions. Simultaneously, both heart rate and respiratory rate are extracted from the acceleration signal. A small cohort of 7 healthy subjects were recruited for lab-controlled experiments to evaluate the performance of our proposed methods. ECG signal and K4b2 system's V02 measurements were also collected to extract heart rate and respiratory rate as the ground truth for comparison. An overall classification accuracy of 99% is achieved for recognising the correct sleeping positions. Good matches to ground truths were also obtained for the derived cardiac and respiratory rates.

Humanoid robots have been used in a wide range of applications including entertainment, healthcare, and assistive living. In these applications, the robots are expected to perform a range of natural body motions, which can be either preprogrammed or learnt from human demonstration. This paper proposes a strategy for imitating dynamic walking gait for a humanoid robot by formulating the problem as an optimization process. The human motion data are recorded with an inertial sensor-based motion tracking system (Biomotion+). Joint angle trajectories are obtained from the transformation of the estimated posture. Key locomotion frames corresponding to gait events are chosen from the trajectories. Due to differences in joint structures of the human and robot, the joint angles at these frames need to be optimized to satisfy the physical constraints of the robot while preserving robot stability. Interpolation among the optimized angles is needed to generate continuous angle trajectories. The method is validated using a NAO humanoid robot, with results demonstrating the effectiveness of the proposed strategy for dynamic walking.

Sensor based motion capture system enables motion analysis with applications ranging from entertainment, healthcare and robotics. It can provide an intuitive interface for human to provide motion training and control a humanoid robot. In this paper, we propose a novel framework for the imitation of human motion for a humanoid robot. In the proposed framework, human motion data is directly captured from a wireless, wearable motion capture platform (Biomotion+). The reconstructed posture is then converted into joint angle trajectories. Due to the structural differences between the joints of the robot and those of the human, the trajectories are then optimized to satisfy the mechanical constraints of the robot and to maintain appropriate balance. To validate the proposed framework, different motion trajectories were verified. The results demonstrate the stability and effectiveness of the proposed framework to reproduce realistic human motion for a humanoid robot and the potential for a tele-rehabilitation application. The proposed framework offers a new way of imitating human motion for a humanoid robot.

The aim of this work is to provide a humanoid robot that is able to replicate human's upper body movements by using motion capture data acquired from Biomotion+, developed by the Hamlyn Centre. This work proposes an upper limb motion imitation module for a humanoid robot. The module calculates joint angle trajectories, based on motion capture data, and sends these trajectories to a humanoid robot. The experimental results have demonstrated the effectiveness of the module which can achieve reasonable postural similarity of generated robot motions, compared to the captured human movements.

Pervasive human motion capture in the workplace facilitates detailed analysis of the actions of individual subjects and team interaction. It is also important for ergonomic studies for assessing instrument design and workflow analysis. However, a busy, dynamic, team-based environment, such as the operating theatre poses a number of challenges for the currently used marker-based and sensor-based motion capture systems. Occlusions and sensor drift can affect the accuracy of the estimated motion. In this paper, we present a motion capture system that uses a vision-based head detection algorithm and a markerless inertial motion capture for estimating the motion of multiple people. The pose estimation obtained through inertial sensors is combined with location obtained through vision-based tracking to reconstruct the motion of each subject. A multi-target Kalman filter is used to track the movement of each subject. To handle the close proximity of the subjects, visual features associated with the body are used for data association. Experimental results demonstrate the accuracy of the proposed system.

Myoelectric signal analysis provides insight into neural control during muscle contraction and it has been widely used to identify the intention of performing different movements for patients with disabilities. Previous studies have demonstrated that detailed neural control information could be extracted from high-density surface electromyography (EMG) signals. However, this imposes practical constraints for routine applications. In this paper, we present an analysis framework using low-density EMG with example experiments demonstrating the control of forearm functional movement Eight channel surface EMG signals are used with subjects performing 6 different forearm and hand movements. Data analysis consisting of feature selection and pattern classification based on KNN, linear discriminant analysis and support vector machine is then performed. High classification accuracy has been achieved for all the subjects, illustrating the practical value of the method proposed.

Real-time shape sensing and state acquisition is important for closed-loop control of hyper-redundant snake robots in minimally invasive surgery. Due to the miniaturized size of such minimally invasive surgery robots, it is not feasible to use existing angular sensors involving rotary encoders. With recent advances of the MEMS technology, micro inertial sensors have shown their potential for robot state estimation. Previous studies have demonstrated that accurate joint angles can be estimated for one degree-of-freedom (DoF) joints. However, higher DoF joints of the robot can impose a number of challenges to the current joint angle estimation methods. This paper presents a micro-sensing platform and shape reconstruction algorithm for minimally invasive surgery snake robot with two DoF joints. The method incorporates both gravitational and gyroscopic sensing for calculating the rotation difference between any consecutive robot segments. The gyroscope measurements are first used as the input to predict the rotation difference by direct orientation integration. The orientation difference is then derived from the consecutive acceleration vectors to update the prediction through a complementary filter. To demonstrate the performance of our proposed approach, a robot prototype with two universal joints was fabricated. Detailed experimental results have demonstrated that high accuracy can be achieved by using the proposed method for joint angle estimation.

Detailed motion reconstruction is a prerequisite of biomotion analysis and physical function assessment for a variety of scenarios. For example, biomechanical analysis can be used to assess physical activity to diagnose pathological conditions, to provide an objective measure of biomechanics for peri-operative care, and to monitor patients with mobility issues. Unfortunately, current motion capture systems cannot perform biomechanical analysis continuously in the patient's natural environment. In this paper, a pose estimation scheme from a sparse network of accelerometer-based wearable sensors, which does not impose restrictions upon the patient's daily life, is presented. In the proposed method, a marker-based motion capture system is used for acquiring the 3D motion data, and partial least squares regression (PLSR) is used to establish the implicit model between 3D body pose and the wearable sensor measurements. A linear constant velocity process model and measurement model are designed and a Kalman filter is then deployed to estimate the posture. Experimental results demonstrate the strength of the technique and how it can be used to estimate detailed 3D motion from a sparse set of sensors.

Accurate recovery of the 3D structure of a deforming surgical environment during minimally invasive surgery is important for intra-operative guidance. One key component of reliable reconstruction is accurate camera pose estimation, which is challenging for monocular cameras due to the paucity of reliable salient features, coupled with narrow baseline during surgical navigation. With recent advances in miniaturized MEMS sensors, the combination of inertial and vision sensing can provide increased robustness for camera pose estimation particularly for scenes involving tissue deformation. The aim of this work is to propose a robust framework for intra-operative free-form deformation recovery based on structure-from-motion. A novel adaptive Unscented Kalman Filter (UKF) parameterization scheme is proposed to fuse vision information with data from an Inertial Measurement Unit (IMU). The method is built on a compact scene representation scheme suitable for both surgical episode identification and instrument-tissue motion modelling. Detailed validation with both synthetic and phantom data is performed and results derived justify the potential clinical value of the technique.

Accurate human movement estimation techniques are widely used in various applications, such as robotics, human-machine interaction, sports, and rehabilitation. With rapid advances in microsensors, human movement estimation using wearable micro inertial sensors has become an active research topic. The main challenges for the wearable sensor motion estimation are the inertial sensor drift problem and the linear acceleration interference problem. Because of the agility in movement, upper limb motion estimation has been regarded as the most difficult problem in human motion estimation. In this paper, we take the upper limb as our research subject and present a novel upper limb movement estimation algorithm to cope with these two challenges by adaptive fusion of sensor data and human skeleton constraint. In the sensor fusion part, a quaternion-based unscented Kalman filter is invoked to fuse the gyroscope, accelerometer, and magnetometer measurement information. In the Kalman filter framework, an acceleration interference detection scheme is implemented based on the exponentially discounted average of the normalized innovation squared (NIS). According to the detection results, the process and measurement noise levels are scaled up or down automatically. To further compensate for the drift, we present a novel solution by modeling geometrical constraint in the elbow joint and fuse the constraint to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results in comparison to the BTS SMART-D optical motion tracker.

Human back movement estimation is clinically important for assessing patients with back pain. Most current techniques are limited to simple spinal movement angles without consideration of surrounding muscle movement and backplane rotation and torsion. These three dimensional analysis is fraught with difficulties due to the complex nature of the movement and sensor placement. In this paper, a consistent method based on multiple Body Sensor Network (BSN) nodes for the measurement of 3D bending and twist of the back is proposed. In our method, five BSN nodes, each consisting of a three axis accelerometer, a gyroscope and a magnetometer, are placed at the human back. Euler angles are then defined to represent the orientation for human back segments, kinematics analysis is then derived. An unscented Kalman filter (UKF) is deployed to estimate the defined Euler angles. Detailed experimental results have shown the feasibility and effectiveness of the proposed measurement and analysis framework.

Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.

Human motion capture technologies are widely used in interactive game and learning, animation, film special effects, health-care and navigation. Because of the agility, upper limb motion estimation is the most difficult in human motion capture. Traditional methods always assume that the movements of upper arm and forearm are independent and estimate their movements separately; therefore, the estimated motion are always with serious distortion. In the paper, we proposed a novel ubiquitous upper limb motion estimation method using wearable micro-sensors, which concentrated on modeling the relationship of the movements between upper arm and forearm. Exploration of the skeleton structure of upper limb as a link structure with 5 degrees of freedom was firstly proposed to model human upper limb motion. After that, parameters were defined according to Denavit-Hartenberg convention, forward kinematic equations of upper limb were derived, and an Unscented Kalman filter was invoked to estimate the defined parameters. The experimental results have shown the feasibility and effectiveness of the proposed upper limb motion capture and analysis algorithm.

Tracking multiple maneuvering targets remains a challenge because of clutter and spurious targets. We propose a Signature Driven multiple target Tracking (SDT) method which uses target signature in spectral, spatial and temporary spaces as well as the Markov property of target movement, so that the data association process in SDT is very efficient and effective. The experimental results have shown outstanding performance.

Hip angle is a major parameter in gait analysis while gait analysis plays important role in health-care, animation and other applications. Accurate and robust estimation of hip angle in ambulatory environment remains a challenge because the non-linear nature of thigh movement has not been well studied yet. Although piece-wise linear model is effective to approximate the non-linear model, the current solutions, Gaussian Particle Filter (GPF), is suffering from heavily computation load, which makes the ambulatory hip angle estimation in real time impossible. In this paper, we propose to use Discrete Wavelet Transform to detect major gait events from the measurements of the wearable accelerometer that are attached to the thigh. Based on the detection result, a corresponding linear hip angle dynamic is selected and an Unscented Kalman Filter (UKF) is invoked to estimate the hip angle. The experimental results have shown that the proposed methods can achieve robust and accurate hip angle estimation, and with much less computation loads over the previous work on the ambulatory gait analysis.

This paper presents the method for the detection and localization of moving targets in passive infrared (PIR) sensor networks in both indoor and outdoor settings. It reports our design and implementation of PIR sensor network, especially, we proposed a detection algorithm, which uses Adaptive Threshold with Constant False Alarm Rate; and developed a localization algorithm using direction search in the grid space of the network. The experimental results have shown that our PIR sensor network can detect and locate the moving targets with reasonable accuracy.