Wearable surface deformation myography (sDMG) system for recognition of locomotion modes.

This study designs a wearable sensing system for locomotion mode recognition using lower-limb skin surface curvature deformation caused by the morphological changes of musculotendinous complexes and soft tissues. Flexible bending sensors are embedded into stretch pants, enabling curvature deformations of specific skin segments above lower-limb muscle groups to be captured in a noncontact manner. To evaluate the performance of this system, we conducted experiments on eight able-bodied subjects completing seven common locomotive activities, including walking, running, ramp ascending/descending, stair ascending/descending, and standing. The system measured seven channels of deformation signals from two cross-sections on the shank and the thigh. The collected signals were distinguishable across different locomotion modes and exhibited consistency when monitoring steps. Using selected time-domain features and a linear discriminant analysis (LDA) classifier enabled the proposed system to continuously recognize locomotion modes with an average accuracy of 96.5%. Furthermore, the system maintains recognition performance with 95.7% accuracy even after removing and reapplying the sensors. Finally, we conducted comparison experiments to analyze how window length, feature selection, and the number of channels affect recognition performance, providing insights for optimization. We believe that this novel signal platform holds great potential as a valuable supplementary tool in wearable human motion detection, enriching the information diversity for motion analysis, and enabling new possibilities for further advancements and applications in fields including biomedical engineering, textiles, and computer graphics.

Comparison of Wrist and Forearm EMG for Multi-day Biometric Authentication.

Recently, electromyography (EMG) has been established as a promising new biometric trait that provides a unique dual mode security: biometrics and knowledge. For authentication that is used daily and long-term by general consumers, the wrist is a suitable location, which could be easily integrated into the existing form of smartwatches and fitness trackers. However, current EMG-based biometrics still follow the historical path of powered prosthetics research, where EMG signals were usually recorded from forearm positions. Moreover, the robustness of EMG processing algorithms across multiple days is still an open problem that needs to be addressed before for long-term reliable use. This study intends to investigate the difference in authentication performance between wrist and forearm EMG signals, in a within-day and two cross-day analyses. Our open dataset (GRABMyo dataset) was used to examine this difference, which contains forearm and wrist EMG data collected from 43 participants over three different days with long separation (Days 1, 8, and 29). The results showed wrist EMG signals led to at least comparable with forearm EMG signals in within-day Equal-error rate (EER). In cross-day analysis, the EER of the wrist EMG signals was higher than that of forearm signals. In general, the low median EER (<0.1) of wrist EMG in cumulative cross-day analysis demonstrates the promise of using wrist EMG signals for authentication in long-term applications.

Design trends in actuated lower-limb prosthetic systems: a narrative review.

INTRODUCTION: Actuated lower limb prostheses, including powered (active) and semi-active (quasi-passive) joints, are endowed with controllable power and/or impedance, which can be advantageous to limb impairment individuals by improving locomotion mechanics and reducing the overall metabolic cost of ambulation. However, an increasing number of commercial and research-focused options have made navigating this field a daunting task for users, researchers, clinicians, and professionals. AREAS COVERED: The present paper provides an overview of the latest trends and developments in the field of actuated lower-limb prostheses and corresponding technologies. Following a gentle summary of essential gait features, we introduce and compare various actuated prosthetic solutions in academia and the market designed to provide assistance at different levels of impairments. Correspondingly, we offer insights into the latest developments of sockets and suspension systems, before finally discussing the established and emerging trends in surgical approaches aimed at improving prosthetic experience through enhanced physical and neural interfaces. EXPERT OPINION: The ongoing challenges and future research opportunities in the field are summarized for exploring potential avenues for development of next generation of actuated lower limb prostheses. In our opinions, a closer multidisciplinary integration can be found in the field of actuated lower-limb prostheses in the future.

Voice disorder classification using convolutional neural network based on deep transfer learning.

Voice disorders are very common in the global population. Many researchers have conducted research on the identification and classification of voice disorders based on machine learning. As a data-driven algorithm, machine learning requires a large number of samples for training. However, due to the sensitivity and particularity of medical data, it is difficult to obtain sufficient samples for model learning. To address this challenge, this paper proposes a pretrained OpenL3-SVM transfer learning framework for the automatic recognition of multi-class voice disorders. The framework combines a pre-trained convolutional neural network, OpenL3, and a support vector machine (SVM) classifier. The Mel spectrum of the given voice signal is first extracted and then input into the OpenL3 network to obtain high-level feature embedding. Considering the effects of redundant and negative high-dimensional features, model overfitting easily occurs. Therefore, linear local tangent space alignment (LLTSA) is used for feature dimension reduction. Finally, the obtained dimensionality reduction features are used to train the SVM for voice disorder classification. Fivefold cross-validation is used to verify the classification performance of the OpenL3-SVM. The experimental results show that OpenL3-SVM can effectively classify voice disorders automatically, and its performance exceeds that of the existing methods. With continuous improvements in research, it is expected to be considered as auxiliary diagnostic tool for physicians in the future.

Pathological voice classification based on multi-domain features and deep hierarchical extreme learning machine.

The intelligent data-driven screening of pathological voice signals is a non-invasive and real-time tool for computer-aided diagnosis that has attracted increasing attention from researchers and clinicians. In this paper, the authors propose multi-domain features and the hierarchical extreme learning machine (H-ELM) for the automatic identification of voice disorders. A sufficient number of sensitive features are first extracted from the original voice signal through multi-domain feature extraction (i.e., features of the time domain and the sample entropy based on ensemble empirical mode decomposition and gammatone frequency cepstral coefficients). To eliminate redundancy in high-dimensional features, neighborhood component analysis is then applied to filter out sensitive features from the high-dimensional feature vectors to improve the efficiency of network training and reduce overfitting. The sensitive features thus obtained are then used to train the H-ELM for pathological voice classification. The results of the experiments showed that the sensitivity, specificity, F1 score, and accuracy of the H-ELM were 99.37%, 98.61%, 99.37%, and 98.99%, respectively. Therefore, the proposed method is feasible for the initial classification of pathological voice signals.

Adaptive Quadruped Balance Control for Dynamic Environments Using Maximum-Entropy Reinforcement Learning.

External disturbance poses the primary threat to robot balance in dynamic environments. This paper provides a learning-based control architecture for quadrupedal self-balancing, which is adaptable to multiple unpredictable scenes of external continuous disturbance. Different from conventional methods which construct analytical models which explicitly reason the balancing process, our work utilized reinforcement learning and artificial neural network to avoid incomprehensible mathematical modeling. The control policy is composed of a neural network and a Tanh Gaussian policy, which implicitly establishes the fuzzy mapping from proprioceptive signals to action commands. During the training process, the maximum-entropy method (soft actor-critic algorithm) is employed to endow the policy with powerful exploration and generalization ability. The trained policy is validated in both simulations and realistic experiments with a customized quadruped robot. The results demonstrate that the policy can be easily transferred to the real world without elaborate configurations. Moreover, although this policy is trained in merely one specific vibration condition, it demonstrates robustness under conditions that were never encountered during training.

Successful intravenous Alteplase for a centenarian woman with acute ischaemic stroke.

We report a case of a 105-year-old woman who was treated with Alteplase for acute ischaemic stroke (AIS). On arrival, this centenarian woman had sudden-onset aphasia and right-sided hemiparesis, her baseline National Institutes of Health Stroke Scale (NIHSS) score was 21. An acute noncontrast brain CT scan showed only mild white matter disease. After assessing the patient's risk-benefit ratio, the patient was administered intravenous Alteplase (0.9 mg/kg) 2.5 hours after symptom onset. Her right hemiparesis and aphasia eased within 24 hours after Alteplase treatment. At discharge, her NIHSS score had decreased to 1, leaving only mild right facial palsy unresolved. Few clinical data are available on centenarian patients with AIS who undergo intravenous thrombolysis. There are no data on what is the best method to use to manage centenarians. Our successful thrombolysis case indicates that biologically young centenarians may profit from thrombolysis, despite the very high age.

Hyperuricemia and prognosis of acute ischemic stroke in diabetic patients.

OBJECTIVES: The relationship between hyperuricemia (HUA) and prognosis of acute ischemic stroke (AIS) is unclear. This prospective cohort study aims to evaluate potential value of HUA as a prognostic factor for AIS independent of diabetic status. METHODS: A total of 1041 consecutive patients aged from 25 to 96 with AIS were included. 340 (32.7%) had diabetes and 246 (23.6%) had HUA. Diabetic patients were stratified by gender or age. Multivariate logistic regression was used to analyse the association between HUA and prognosis of AIS. RESULTS: HUA independently predicted poor discharge outcome of AIS in diabetic patients [OR (95% CI): 2.061 (1.042-4.077), p < 0.05]. Among diabetics, HUA selectively predicted a poor functional outcome of AIS at discharge in patients aged ≤75 years [OR (95% CI): 2.381 (1.115-5.085), p < 0.05]. Furthermore, in patients aged ≤75 years, HUA independently predicted poor discharge outcome of AIS in male diabetics [OR (95% CI): 2.684 (1.001-7.200), p < 0.05]. No association between HUA and prognosis of AIS was observed at 3-month, 6-month and 12-month follow-up, either in diabetics or nondiabetics. CONCLUSIONS: HUA independently predicted poor in-hospital outcome of AIS in diabetic patients, especially in patients aged ≤ 75 years.

The value of the ucN13-P15 interpeak latency predicted acute posterior circulation ischemia and the chronic outcome.

OBJECTIVE: To determine the sensitivity and specificity of the ucN13-P15 (CV2-Fz) and IcN13-P15 (CV7-Fz) interpeak latencies when used to predict acute posterior circulation ischemia. METHODS: A total of 426 consecutive patients who were hospitalized within 3 days of the onset of stroke were prospectively enrolled in the study. Of these patients, 110 had infarct lesions in the posterior circulation territory. The ucN13-P15 and IcN13-P15 interpeak latencies on admission were assessed, and the chronic outcome was assessed at 3 months. RESULTS: The ucN13-P15 and IcN13-P15 interpeak latencies (both P < 0.001) were prolonged in patients with posterior circulation ischemia than in those with anterior circulation ischemia. The optimal cutoff score of the baseline ucN13-P15 IPL (≥ 3.35 milliseconds) had a sensitivity of 76.8% and a specificity of 79.9%. The IcN13-P15 IPL (≥ 4.225 milliseconds) had a sensitivity of 56.1% and a specificity of 93.2%. CONCLUSIONS: The ucN13-P15 interpeak latency must be appraised in light of its diagnostic value in acute posterior circulation ischemia. However, the ucN13-P15 interpeak latency was not independently predictive of a favorable outcome.