Synchronized AMG and EMG Dataset of Lower-limb Muscle Activities in Everyday Training

2026-08-12

AI Summary

This study addresses the lack of synchronized multimodal data for elucidating the coordination mechanisms of lower-limb muscle groups during everyday activities, a gap that has hindered reproducible analyses in motor impairment, rehabilitation, and athletic performance. To this end, we present a novel multimodal dataset comprising 1,918 trials from 30 healthy adults across 16 task conditions, uniquely integrating acceleromyography (AMG), surface electromyography (EMG), and optical motion capture (MoCap) with clearly defined sensor placement, temporal alignment, and frequency-band processing protocols. Using this dataset, we establish a cross-subject benchmark for joint angle estimation, achieving mean absolute errors between 8.840° and 9.591° across four models. Ablation studies further confirm AMG’s capacity to effectively capture muscle activity at the mechanical level.

Abstract

Understanding how lower-limb muscle groups coordinate is important for studying movement impairment, rehabilitation, and physical performance. Reproducible analysis of this coordination requires multimodal recordings that relate local muscle-related signals with body-level kinematics. Complementing neural-level electrical activation captured by EMG, AMG provides a valuable mechanical approach to monitoring muscle activity. Here, we introduce a synchronized, multimodal dataset for healthy-adult lower-limb activities. For data collection on the left leg, 16 triaxial accelerometers were evenly divided into four muscle-site clusters for AMG recording, complemented by four surface EMG channels. A 15-marker optical motion-capture (MoCap) system captured lower-body kinematics, with the resulting marker trajectories used to compute bilateral knee and ankle joint angles. Our dataset contains 1,918 trials from 30 subjects across 16 task conditions. We benchmark the dataset by estimating four joint angles from 300 ms windows of the 5-100 Hz band-pass-filtered AMG data and assess matched EMG features in a separate modality ablation. In the primary cross subject benchmark, the four reference models achieved mean absolute errors of 8.840$^\circ$-9.591$^\circ$. The benchmark and ablation results characterize performance across subjects, tasks, and joint angles and examine the effects of sensor configuration, modality, the number of training subjects, and frequency representation. The release includes documented timing definitions, processed data, and reproducible benchmark resources. https://dongxutang918-afk.github.io/SAME-Limb/

Problem

• synchronized dataset
• lower-limb muscle activity
• multimodal recording
• movement coordination
• rehabilitation

Innovation

• synchronized multimodal dataset
• acceleromyography (AMG)
• surface electromyography (sEMG)
• joint angle estimation
• lower-limb muscle coordination