A Novel Transformer-Based Approach for Simultaneous Recognition of Hand Movements and Force Levels in Amputees Using Flexible Ultrasound Transducers.

Accurate hand motion intention recognition is essential for the intuitive control of intelligent prosthetic hands and other human-machine interaction systems. Sonomyography, which can detect the changes in muscle morphology and structure precisely, is a promising signal source for fine hand movement recognition. However, sonomyography measured by traditional rigid ultrasound probes may suffer from poor acoustic coupling because the rigid probe surfaces cannot accommodate the curvilinear shape of the human body, particularly in the case of small and irregular residual limbs in amputees. In this study, we used a self-designed lightweight, flexible, and wearable ultrasound transducer to acquire muscle ultrasound images, and proposed a sonomyography transformer (SMGT) model for simultaneous recognition of hand movements and force levels. The performance of SMGT was systematically compared to two commonly used image processing methods, HOG and Gray Gradient, as well as a deep CNN model, in simultaneously recognizing ten classes of hand/finger movements and three force levels. Additionally, ten subjects including seven non-disabled subjects and three trans-radial amputees who are the end users of prosthetic hands were recruited to evaluate the effectiveness of SMGT. Results showed that our proposed method achieved average classification accuracies of 98.4% ± 0.6% and 96.2% ± 3.0% in non-disabled subjects and amputee subjects, respectively, which are much higher than those of other methods. This study provided a valuable approach for ultrasound-based hand motion recognition that may promote the applications of intelligent prosthetic hands.

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition.

Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects' thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.