ActiveSelfHAR: Incorporating Self Training into Active Learning to Improve Cross-Subject Human Activity Recognition

Deep learning-based human activity recognition (HAR) methods have shown great promise in the applications of smart healthcare systems and wireless body sensor network (BSN). Despite their demonstrated performance in laboratory settings, the real-world implementation of such methods is still hindered by the cross-subject issue when adapting to new users. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning's benefit of sparsely acquiring data with actual labels and self- training's benefit of effectively utilizing unlabeled data to enable the deep model to adapt to the target domain, i.e., the new users. In this framework, the model trained in the last iteration or the source domain is first utilized to generate pseudo labels of the target-domain samples and construct a self-training set based on the confidence score. Second, we propose to use the spatio-temporal relationships among the samples in the non-self-training set to augment the core set selected by active learning. Finally, we combine the self-training set and the augmented core set to fine-tune the model. We demonstrate our method by comparing it with state-of-the-art methods on two IMU-based datasets and an EMG-based dataset. Our method presents similar HAR accuracies with the upper bound, i.e. fully supervised fine-tuning with less than 1\% labeled data of the target dataset and significantly improves data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into smart healthcare systems and BSN

Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study

Assessing the progression of muscle fatigue for daily exercises provides vital indicators for precise rehabilitation, personalized training dose, especially under the context of Metaverse. Assessing fatigue of multi-muscle coordination-involved daily exercises requires the neuromuscular features that represent the fatigue-induced characteristics of spatiotemporal adaptions of multiple muscles and the estimator that captures the time-evolving progression of fatigue. In this paper, we propose to depict fatigue by the features of muscle compensation and spinal module activation changes and estimate continuous fatigue by a physiological rationale model. First, we extract muscle synergy fractionation and the variance of spinal module spikings as features inspired by the prior of fatigue-induced neuromuscular adaptations. Second, we treat the features as observations and develop a Bayesian Gaussian process to capture the time-evolving progression. Third, we solve the issue of lacking supervision information by mathematically formulating the time-evolving characteristics of fatigue as the loss function. Finally, we adapt the metrics that follow the physiological principles of fatigue to quantitatively evaluate the performance. Our extensive experiments present a 0.99 similarity between days, a over 0.7 similarity with other views of fatigue and a nearly 1 weak monotonicity, which outperform other methods. This study would aim the objective assessment of muscle fatigue.Comment: submitted to IEEE JBH

miRNA-544a Regulates the Inflammation of Spinal Cord Injury by Inhibiting the Expression of NEUROD4

Background/Aims: To explore the potential role of miR-544a in spinal cord injury and the possible mechanism involved. Methods: We established a mouse model with spinal cord injury to examine the changes in grip force recovery of the forelimb or the posterior limb of the mouse. Microarray was performed to achieve differentiated miRNAs in the mice. The expressions of miR-544a, MCP-1, IL36B and IL17B after spinal cord injury were detected by qRT-PCR. Subsequently, miR-544a was overexpressed to observe changes in inflammation and grip strength after spinal cord injury. Target gene of miR-544a was then predicted using bioinformatics technology. Finally, dual luciferase reporter gene assay was used to verify the binding of miR-544a to its target gene. Results: Using mice models with spinal cord injury, we found that the strength of their four limbs began to recover 7 days after injury. The results of microarray and qRT-PCR confirmed that mir-544a level in mice with spinal cord injury decreased with increase of injury time, while the levels of inflammatory genes MCP-1 (monocyte chemoattractant protein-1), IL1 (interleukin-1) and TNF-α (tumor necrosis factor alpha) IL36B (interleukin-36 beta) and IL17B (interleukin-17 beta) were significantly increased. However, overexpression of miR-544a in the mice significantly reduced the level of inflammation and restored their grip strength in their four limbs. Finally, we found that miR-544a can bind to the NEUROD4 (Neurogenic differentiation 4) 3’UTR (Untranslated Region) region through bioinformatics website prediction, which was further confirmed by dual luciferase reporter assay. NEUROD4 level was significantly reduced following the overexpression of miR-544a. Conclusion: The expression of miR-544a was significantly decreased after spinal cord injury. High expression of miR-544a could alleviate the inflammation caused by spinal cord injury and promote the recovery of spinal cord via the inhibition of NEUROD4

sEMG-Based Gesture Recognition with Convolution Neural Networks

The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy

Reference Frame Unification of IMU-Based Joint Angle Estimation: The Experimental Investigation and a Novel Method

Inertial measurement unit (IMU)-based joint angle estimation is an increasingly mature technique that has a broad range of applications in clinics, biomechanics and robotics. However, the deviations of different IMUs’ reference frames, referring to IMUs’ individual orientations estimating errors, is still a challenge for improving the angle estimation accuracy due to conceptual confusion, relatively simple metrics and the lack of systematical investigation. In this paper, we clarify the determination of reference frame unification, experimentally study the time-varying characteristics of reference frames’ deviations and accordingly propose a novel method with a comprehensive metric to unify reference frames. To be specific, we firstly define the reference frame unification (RFU) and distinguish it with drift correction that has always been confused with the term RFU. Secondly, we design a mechanical gimbal-based experiment to study the deviations, where sensor-to-body alignment and rotation-caused differences of orientations are excluded. Thirdly, based on the findings of the experiment, we propose a novel method to utilize the consistency of the joint axis under the hinge-joint constraint, gravity acceleration and local magnetic field to comprehensively unify reference frames, which meets the nonlinear time-varying characteristics of the deviations. The results on ten human subjects reveal the feasibility of our proposed method and the improvement from previous methods. This work contributes to a relatively new perspective of considering and improving the accuracy of IMU-based joint angle estimation

The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy

Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing the time-varying changes of the extracted features, and proposes a training strategy that can improve the classifiers’ robustness against muscle fatigue. In particular, we first select some features that are commonly used in fatigue-related studies and use them to classify gait phases under muscle fatigue. Then, we analyze the time-varying characteristics of extracted features, with the aim of explaining the performance of the classifiers. Finally, we propose a training strategy that effectively improves the robustness against muscle fatigue, which contributes to an easy-to-use method. Ten subjects performing prolonged walking are recruited. Our study contributes to a novel perspective of designing gait phase classifiers under muscle fatigue