sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton

GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals

Low Back Pain (LBP) is the most prevalent musculoskeletal condition worldwide and a leading cause of disability, significantly affecting mobility, work productivity, and overall quality of life. Due to its high prevalence and substantial economic burden, LBP presents a critical global public health challenge that demands innovative diagnostic and therapeutic solutions. This study introduces a novel deep-learning approach for diagnosing LBP intensity using electroencephalography (EEG) signals and surface electromyography (sEMG) signals from back muscles. A GAN-Convolution-Transformer-based model, named GLEAM (GAN-ConvoLution-sElf Attention-ETLSTM), is designed to classify LBP intensity into four categories: no LBP, mild LBP, moderate LBP, and intolerable LBP. A denoising GAN is central to the model's functionality, playing a pivotal role in enhancing the quality of EEG and sEMG signals by removing noise, resulting in cleaner and more accurate input data. Various features are extracted from the GAN-denoised EEG and sEMG signals, and the combined features from both EEG and sEMG are used for LBP detection. After the feature extraction, the CNN is employed to capture local temporal patterns within the data, allowing the model to focus on smaller, region-specific trends in the signals. Subsequently, the self-attention module identifies global correlations among these locally extracted features, enhancing the model's ability to recognize broader patterns. The proposed ETLSTM network performs the final classification, which achieves an impressive LBP detection accuracy of 98.95%. This research presents several innovative contributions: (i) the development of a novel denoising GAN for cleaning EEG and sEMG signals, (ii) the design and integration of a new ETLSTM architecture as a classifier within the GLEAM model, and (iii) the introduction of the GLEAM hybrid deep learning framework, which enables robust and reliable LBP intensity assessment.

Preference-based assistance optimization for lifting and lowering with a soft back exosuit

Wearable robotic devices have become increasingly prevalent in both occupational and rehabilitative settings, yet their widespread adoption remains inhibited by usability barriers related to comfort, restriction, and noticeable functional benefits. Acknowledging the importance of user perception in this context, this study explores preference-based controller optimization for a back exosuit that assists lifting. Considering the high mental and metabolic effort discrete motor tasks impose, we used a forced-choice Bayesian Optimization approach that promotes sampling efficiency by leveraging domain knowledge about just noticeable differences between assistance settings. Optimizing over two control parameters, preferred settings were consistent within and uniquely different between participants. We discovered that overall, participants preferred asymmetric parameter configurations with more lifting than lowering assistance, and that preferences were sensitive to user anthropometrics. These findings highlight the potential of perceptually guided assistance optimization for wearable robotic devices, marking a step toward more pervasive adoption of these systems in the real world.

Design, Simulation and Functional Testing of a Novel Ankle Exoskeleton with 3DOFs

This paper presents a study on developing a new exoskeleton for ankle joint rehabilitation with three degrees of freedom (3 DOFs). The primary attention is paid to the process of designing and modelling the device aimed at restoring the lost functions of joint mobility. The authors conducted a complex analysis of the functional requirements of the exoskeleton based on research into the potential user's needs, which allowed for the development of a conceptual model of the proposed device. In this study, a prototype of the exoskeleton is designed using modern additive technologies. The prototype underwent virtual testing in conditions maximally close to reality, which confirmed its effectiveness and comfort of use. The main results of this study indicate the promising potential of the proposed solution for application in rehabilitation practices, especially for patients with ankle joint injuries and diseases.

Skin preparation-free, stretchable microneedle adhesive patches for reliable electrophysiological sensing and exoskeleton robot control

High-fidelity and comfortable recording of electrophysiological (EP) signals with on-the-fly setup is essential for health care and human-machine interfaces (HMIs). Microneedle electrodes allow direct access to the epidermis and eliminate time-consuming skin preparation. However, existing microneedle electrodes lack elasticity and reliability required for robust skin interfacing, thereby making long-term, high-quality EP sensing challenging during body movement. Here, we introduce a stretchable microneedle adhesive patch (SNAP) providing excellent skin penetrability and a robust electromechanical skin interface for prolonged and reliable EP monitoring under varying skin conditions. Results demonstrate that the SNAP can substantially reduce skin contact impedance under skin contamination and enhance wearing comfort during motion, outperforming gel and flexible microneedle electrodes. Our wireless SNAP demonstration for exoskeleton robot control shows its potential for highly reliable HMIs, even under time-dynamic skin conditions. We envision that the SNAP will open new opportunities for wearable EP sensing and its real-world applications in HMIs.