Surface electromyogram (sEMG) based hand gesture recognition for prosthesis or armband is an important application of the human-machine interface. However, the measurement location of sensors greatly influences the hand gesture performance, especially with the inter-day or inter-subject validation protocols. Therefore, we acquired two-day hand gesture data of 41 subjects with a 256 (16×16) channel high-density sEMG electrode array. With the acquired data, we initially compared the support vector machine (SVM) and other four state-of-art classifiers under three validation protocols, i.e., intra-day, inter-day and inter-subject validation protocols. Then, we screened 14 feature optimization techniques, including 5 feature-projection methods and 9 feature-ranking approaches. To present the accuracy tendency with varying measure locations, we systematically explored the 10-hand gesture performance using data of 16 prosthesis measurement locations (PMLs) and 15 armband measurement locations (AMLs). As a result, the SVM classifier was suitable for the intra-day and inter-day validation protocols and the 2-dimensional convolutional neural network was selected for the inter-subject validation protocol. The mean accuracies of the hand gesture classification ranged from 95.68% to 99.12% (intra-day validation), from 68.41% to 88.02% (inter-day validation) and from 63.39% to 86.33% (inter-subject validation) for the prosthesis application. In addition, for the armband application, the mean accuracies ranged from 96.25% to 97.43% (intra-day validation), from 67.44% to 75.83% (inter-day validation) and from 65.53% to 75.40% (inter-subject validation). The accuracy is greatly correlated with the measurement location, which is highly associated with the neuromuscular structures of human bodies. In summary, our work can serve as a factor-screening tool for users customizing their systems according to their physical conditions and requirements.

The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely scarcity , quality , and heterogeneity , hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this paper, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.

Measuring the physical, physiological, behavioural or chemical characteristics of an individual as biometrics for personal identification has attracted increasing attentions in smart environment applications. Noncancelability and cross-application invariance are two flaws of traditional DNA, face and fingerprint-based biometrics, because users cannot volitionally change the biometric template. In this work, we acquired high-density surface electromyogram (HD-sEMG) signals encoded by gesture passwords as biometrics. The different sEMG patterns under different motor tasks allow users to enroll multiple accounts using sEMG under different hand gestures as biometrics. By simply changing to a new gesture password, users can cancel the original template once it is compromised. Even if impostors enter the correct gesture password, the individual differences of HD-sEMG as the second defense, can still achieve excellent performance. To improve the current state-of-the-art identification accuracy, we acquired 256-channel forearm HD-sEMG and decoded high-resolution neuromuscular information in temporal-spectral-spatial domain. We achieved a high identification accuracy of 99.85% on a 200-account (20 subjects × 10 accounts per subject) recognition task, with training and testing data acquired 3–25 days apart. Moreover, to address the concern of “unknown identities,” we applied an “authentication + identification” validation, achieving a high accuracy of 93.81% on a 200-account ((16 enrolled subjects + 4 unknown subjects) × 10 accounts per subject) task. Our work substantially improves the current state-of-the-art accuracy for cross-day sEMG biometric identification (improved from ~ 88% to > 99%, with a similar number of identified classes).

Human is capable of producing versatile and precise speech owing to the complex neuromuscular systems that control the movement of facial and neck muscle groups. Silent speech recognition based on surface electromyography (sEMG) has been widely researched, which enables speech communication in the absence of an audible acoustic signal. However, the differences in the activities of facial and neck muscle groups under different phoneme pronunciation conditions have not been well investigated. The present work aims to quantify the macroscopic spatial patterns of the muscle activations at the phoneme level during the movement of facial and neck speech articulator muscle groups (SAMGs) using 320-channel high-density (HD) electrode arrays. 10 subjects performed 14 vowel speech tasks and 15 consonant speech tasks in audible speech and silent speech modes, respectively. Specifically, the root-mean-square heat maps of sEMG signals were used to characterize the SAMGs activation patterns. The results show that the muscle activation regions are overlapped under different phoneme pronunciation conditions and symmetric between the left and right sides of face and neck with the average correlation coefficient > 0.9. In addition, the best result can achieve a classification accuracy of 85.78% for consonant and 79.42% for vowel in silent speech mode. The overall results can provide a reference to the hardware or electrode design of portable device for practical use in future work.

In most real world rehabilitation training, patients are trained to regain motion capabilities with the aid of functional/epidural electrical stimulation (FES/EES), under the support of gravity-assist systems to prevent falls. However, the lack of motion analysis dataset designed specifically for rehabilitation-related applications largely limits the conduct of pilot research. We provide an open access dataset, consisting of multimodal data collected via 16 electromyography (EMG) sensors, 6 inertial measurement unit (IMU) sensors, and 230 insole pressure sensors (IPS) per foot, together with a 26-sensor motion capture system, under different MOVEments and POstures for Rehabilitation Training (MovePort). Data were collected under diverse experimental paradigms. Twenty four participants first imitated multiple normal and abnormal body postures including (1) normal standing still, (2) leaning forward, (3) leaning back, and (4) half-squat, which in practical applications, can be detected as feedback to tune the parameters of FES/EES and gravity-assist systems to keep patients in a target body posture. Data under imitated abnormal gaits, e.g., (1) with legs raised higher under excessive electrical stimulation, and (2) with dragging legs under insufficient stimulation, were also collected. Data under normal gaits with low, medium and high speeds are also included. Pathological gait data from a subject with spastic paraplegia further increases the clinical value of our dataset. We also provide source codes to perform both intra- and inter-participant motion analyses of our dataset. We expect our dataset can provide a unique platform to promote collaboration among neurorehabilitation engineers.