A Real-Time Hand Gesture Recognition System for Low-Latency HMI via Transient HD-SEMG and In-Sensor Computing.

In real-time human-machine interaction (HMI) applications, hand gesture recognition (HGR) requires high accuracy with low latency. Surface electromyography (sEMG), a physiological electrical signal reflecting muscle activation, is extensively used in HMI. Recently, transient sEMG, generated during the gesture transitions, has been employed in HGR to achieve lower observational latency compared to steady-state sEMG.

Attention-guided graph structure learning network for EEG-enabled auditory attention detection.

: Decoding auditory attention from brain signals is essential for the development of neuro-steered hearing aids. This study aims to overcome the challenges of extracting discriminative feature representations from electroencephalography (EEG) signals for auditory attention detection (AAD) tasks, particularly focusing on the intrinsic relationships between different EEG channels.: We propose a novel attention-guided graph structure learning network, AGSLnet, which leverages potential relationships between EEG channels to improve AAD performance.

Extended residual learning with one-shot imitation learning for robotic assembly in semi-structured environment.

Introduction: Robotic assembly tasks require precise manipulation and coordination, often necessitating advanced learning techniques to achieve efficient and effective performance. While residual reinforcement learning with a base policy has shown promise in this domain, existing base policy approaches often rely on hand-designed full-state features and policies or extensive demonstrations, limiting their applicability in semi-structured environments.

Methods: In this study, we propose an innovative Object-Embodiment-Centric Imitation and Residual Reinforcement Learning (OEC-IRRL) approach that leverages an object-embodiment-centric (OEC) task representation to integrate vision models with imitation and residual learning.

Correction: Stretchable and durable HD-sEMG electrodes for accurate recognition of swallowing activities on complex epidermal surfaces.

[This corrects the article DOI: 10.1038/s41378-023-00591-3.].

Stretchable and durable HD-sEMG electrodes for accurate recognition of swallowing activities on complex epidermal surfaces.

Surface electromyography (sEMG) is widely used in monitoring human health. Nonetheless, it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces such as the larynx, because regular sEMG electrodes have stiff structures. In this study, we developed a stretchable, high-density sEMG electrode array via layer-by-layer printing and lamination.

Compensatory movement detection by using near-infrared spectroscopy technology based on signal improvement method.

Introduction: Compensatory movements usually occur in stroke survivors with hemiplegia, which is detrimental to recovery. This paper proposes a compensatory movement detection method based on near-infrared spectroscopy (NIRS) technology and verifies its feasibility using a machine learning algorithm. We present a differential-based signal improvement (DBSI) method to enhance NIRS signal quality and discuss its effect on improving detection performance.

LiDAR-Based Hand Contralateral Controlled Functional Electrical Stimulation System.

Contralateral controlled functional electrical stimulation (CCFES) can induce simultaneous movements in patients' bilateral hands. It has been clinically proven to be effective in improving hand motor control and dexterity. sEMG and bending sensor-based data gloves for detecting patients' motor intent have been developed with limitations.

Design and Testing of a Dynamic Orthosis to Reduce Glenohumeral Subluxation With Omnidirectional Shoulder Motion.

Objective: This paper aimed to develop an orthosis to apply a compensating force to improve the stability of the glenohumeral joint without resisting arm movement.

Methods: The proposed orthosis was based on a parallelogram structure to provide a pair of compensating forces to the glenohumeral joint center. Theoretical analysis was used to evaluate the additional moments caused by glenohumeral joint center shifting.

Interpretable evaluation for the Brunnstrom recovery stage of the lower limb based on wearable sensors.

With the increasing number of stroke patients, there is an urgent need for an accessible, scientific, and reliable evaluation method for stroke rehabilitation. Although many rehabilitation stage evaluation methods based on the wearable sensors and machine learning algorithm have been developed, the interpretable evaluation of the Brunnstrom recovery stage of the lower limb (BRS-L) is still lacking. The paper propose an interpretable BRS-L evaluation method based on wearable sensors.

STAnet: A Spatiotemporal Attention Network for Decoding Auditory Spatial Attention From EEG.

Objective: Humans are able to localize the source of a sound. This enables them to direct attention to a particular speaker in a cocktail party. Psycho-acoustic studies show that the sensory cortices of the human brain respond to the location of sound sources differently, and the auditory attention itself is a dynamic and temporally based brain activity.

A model for predicting ground reaction force and energetics of human locomotion with an elastically suspended backpack.

This paper presents an actuated spring-loaded inverted pendulum model with a vertically constrained suspended load mass to predict the vertical GRF and energetics of walking and running. Experiments were performed to validate the model prediction accuracy of vertical GRF. The average correlation coefficient was greater than 0.

Auditory Attention Detection with EEG Channel Attention.

Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party.

Evaluation of the Load Reduction Performance Via a Suspended Backpack With Adjustable Stiffness.

Backpacks are essential for travel but carrying a load during a long journey can easily cause muscle fatigue and joint injuries. Previous studies have suggested that suspended backpacks can effectively reduce the energy cost while carrying loads. Researchers have found that adjusting the stiffness of a suspended backpack can optimize its performance.

Auditory Attention Detection via Cross-Modal Attention.

Humans show a remarkable perceptual ability to select the speech stream of interest among multiple competing speakers. Previous studies demonstrated that auditory attention detection (AAD) can infer which speaker is attended by analyzing a listener's electroencephalography (EEG) activities. However, previous AAD approaches perform poorly on short signal segments, more advanced decoding strategies are needed to realize robust real-time AAD.

Two-branch 3D convolutional neural network for motor imagery EEG decoding.

The original motor imagery electroencephalography (MI-EEG) data contains not only temporal features but also a large number of spatial features related to the distribution of electrodes on the brain. However, in the process of MI-EEG decoding, most of the current convolutional neural network (CNN) based methods do not make the utmost of the spatial features related to electrode distribution.In this study, we adopt a concise 3D representation for the MI-EEG data to take full advantage of the spatial features and propose a two-branch 3D CNN (TB-3D CNN) for the 3D representation of MI-EEG data.

Mechanical Efficiency Investigation of an Ankle-Assisted Robot for Human Walking With a Backpack-Load.

The purpose of this work is to investigate the efficiency of wearable assistive devices under different load-carriage walking. We designed an experimental platform with a lightweight ankle-assisted robot. Eight subjects were tested in three experimental conditions: free walk with load (FWL), power-off with load (POFL), and power-on with load for different levels of force at a walking speed of 3.

A novel motor imagery EEG decoding method based on feature separation.

. Motor imagery electroencephalography (EEG) decoding is a vital technology for the brain-computer interface (BCI) systems and has been widely studied in recent years. However, the original EEG signals usually contain a lot of class-independent information, and the existing motor imagery EEG decoding methods are easily interfered by this irrelevant information, which greatly limits the decoding accuracy of these methods.

Motor Imagery EEG Decoding Method Based on a Discriminative Feature Learning Strategy.

With the rapid development of deep learning, more and more deep learning-based motor imagery electroencephalograph (EEG) decoding methods have emerged in recent years. However, the existing deep learning-based methods usually only adopt the constraint of classification loss, which hardly obtains the features with high discrimination and limits the improvement of EEG decoding accuracy. In this paper, a discriminative feature learning strategy is proposed to improve the discrimination of features, which includes the central distance loss (CD-loss), the central vector shift strategy, and the central vector update process.

Individualized Gait Generation for Rehabilitation Robots Based on Recurrent Neural Networks.

Individualized reference gait patterns for lower limb rehabilitation robots can greatly improve the effectiveness of rehabilitation. However, previous methods can only generate customized gait patterns at several fixed discrete walking speeds and generating gaits at continuously varying speeds and stride lengths remains unsolved. This work proposes an individualized gait pattern generation method based on a recurrent neural network (RNN), which is proficient in series modeling.

Computer-aided diagnosis of pectus excavatum using CT images and deep learning methods.

Pectus excavatum (PE) is one of the most common chest wall defects. Accurate assessment of PE deformities is critical for effective surgical intervention. Index-based evaluations have become the standard for objectively estimating PE, however, these indexes cannot represent the whole information of chest CT images and may associated with significant error due to the individual differences.

A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot.

Electroencephalography (EEG) can be used to control machines with human intention, especially for paralyzed people in rehabilitation exercises or daily activities. Some effort was put into this but still not enough for online use. To improve the practicality, this study aims to propose an efficient control method based on P300, a special EEG component.

Physiological and biomechanical effects on the human musculoskeletal system while carrying a suspended-load backpack.

Many people need to carry heavy loads in a backpack to perform occupational, military, or recreational tasks. Suspended-load backpacks have been shown to reduce dynamic peak forces acting on the body and lower an individual's metabolic cost during walking. However, little is known about the physiological and biomechanical effects of a suspended-load backpack on the human musculoskeletal system.

A Novel Quantitative Spasticity Evaluation Method Based on Surface Electromyogram Signals and Adaptive Neuro Fuzzy Inference System.

Stroke patients often suffer from spasticity. Before treatment of spasticity, there are often practical demands for objective and quantitative assessment of muscle spasticity. However, the common quantitative spasticity assessment method, the tonic stretch reflex threshold (TSRT), is time-consuming and complicated to implement due to the requirement of multiple passive stretches.

Multi-projection of unequal dimension optimal transport theory for Generative Adversary Networks.

As a major step forward in machine learning, generative adversarial networks (GANs) employ the Wasserstein distance as a metric between the generative distribution and target data distribution, and thus can be viewed as optimal transport (OT) problems to reflect the underlying geometry of the probability distribution. However, the unequal dimensions between the source random distribution and the target data, result in often instability in the training processes, and lack of diversity in the generative images. To resolve the challenges, we propose here a multiple-projection approach, to project the source and target probability measures into multiple different low-dimensional subspaces.

Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors.

Background: Compensations are commonly observed in patients with stroke when they engage in reaching without supervision; these behaviors may be detrimental to long-term functional improvement. Automatic detection and reduction of compensation cab help patients perform tasks correctly and promote better upper extremity recovery.

Objective: Our first objective is to verify the feasibility of detecting compensation online using machine learning methods and pressure distribution data.