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

Abstract

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.

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

“…In the intricacies between the neuromuscular system and motion, sEMG offers significant data pertaining to activation patterns, muscular strength, and the phenomenon of fatigue [8]. In the past, researchers have investigated the complex association between sEMG characteristics and gait measures using conventional statistical techniques, which have generally provided only partial or fragmented understanding [9,10]. Numerous studies have substantiated that the onset of sarcopenia can be discerned through the use of sEMG techniques or via the analysis of gait parameters [11].…”

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals

“…Assistive exoskeletons, artificial neural prostheses, and rehabilitative orthotic training devices, among other rehabilitation equipment, are all realized through human-computer interaction control technology [1,2]. Biometric signals contain rich information on limb movements, by analyzing these signals for human movement intention recognition, a more real-time and coordinated human-computer interaction control can be achieved [3]. Specifically, human-computer interaction methods based on sEMG signals are advanced and unaffected by limb integrity, making them suitable not only for general users but also for meeting the human-computer interaction needs of people with disabilities [4].…”