Gesture Recognition through Mechanomyogram Signals: An Adaptive Framework for Arm Posture Variability.

In hand gesture recognition, classifying gestures across multiple arm postures is challenging due to the dynamic nature of muscle fibers and the need to capture muscle activity through electrical connections with the skin. This paper presents a gesture recognition architecture addressing the arm posture challenges using an unsupervised domain adaptation technique and a wearable mechanomyogram (MMG) device that does not require electrical contact with the skin. To deal with the transient characteristics of muscle activities caused by changing arm posture, Continuous Wavelet Transform (CWT) combined with Domain-Adversarial Convolutional Neural Networks (DACNN) were used to extract MMG features and classify hand gestures.

Transcranial direct current stimulation-induced changes in motor cortical connectivity are associated with motor gains following ischemic stroke.

Understanding the response of the injured brain to different transcranial direct current stimulation (tDCS) montages may help explain the variable tDCS treatment results on poststroke motor gains. Cortical connectivity has been found to reflect poststroke motor gains and cortical plasticity, but the changes in connectivity following tDCS remain unknown. We aimed to investigate the relationship between tDCS-induced changes in cortical connectivity and poststroke motor gains.

Single-Channel sEMG-Based Estimation of Knee Joint Angle Using a Decomposition Algorithm With a State-Space Model.

Accurate human motion estimation is crucial for effective and safe human-robot interaction when using robotic devices for rehabilitation or performance enhancement. Although surface electromyography (sEMG) signals have been widely used to estimate human movements, conventional sEMG-based methods, which need sEMG signals measured from multiple relevant muscles, are usually subject to some limitations, including interference between sEMG sensors and wearable robots/environment, complicated calibration, as well as discomfort during long-term routine use. Few methods have been proposed to deal with these limitations by using single-channel sEMG (i.

The Microscopic Mechanism and Rheological Properties of SBS-Modified Asphalt with Warm Mixing Fast-Melting.

To overcome the shortcomings of traditional wet styrene-butadiene-styrene (SBS) modification technology, such as its high energy consumption and thermal decomposition, a warm mix and fast-melting SBS modifier was developed. Based on the theory of rheology, a dynamic shear rheometer (DSR) was applied to investigate the viscoelastic properties of the warm mix and fast-melting SBS-modified asphalt using a frequency scanning test. Atomic force microscopy (AFM) was used to reveal the modification mechanism of the SBS-modified asphalt.

Video-Based Quantification of Gait Impairments in Parkinson's Disease Using Skeleton-Silhouette Fusion Convolution Network.

Gait impairments are among the most common hallmarks of Parkinson's disease (PD), usually appearing in the early stage and becoming a major cause of disability with disease progression. Accurate assessment of gait features is critical to personalized rehabilitation for patients with PD, yet difficult to be routinely carried out as clinical diagnosis using rating scales relies heavily on clinical experience. Moreover, the popular rating scales cannot ensure fine quantification of gait impairments for patients with mild symptoms.

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network.

Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use.

Quantification of Motor Function Post-Stroke Using Novel Combination of Wearable Inertial and Mechanomyographic Sensors.

Subjective clinical rating scales represent the gold-standard for diagnosis of motor function following stroke. In practice however, they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution. Automated systems have been proposed for empirical quantification but have not significantly impacted clinical practice.

A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners.

Active impedance control of a knee-joint orthosis during swing phase.

In this paper, an active impedance control strategy for a knee-joint orthosis is proposed to assist individuals suffering from lower-limb muscular weaknesses during the swing phase of walking activities. The goal of the proposed strategy is to decrease the human effort required for ensuring a successful knee joint movement during walking without sacrificing the wearer's control priority. In this study, a gait-phase based desired knee-joint admittance model is designed by analyzing the kinematic and kinetic characteristics of the wearer's shank-foot segment during walking.