A multimodal EMG and IMU dataset for assessing the quality of exercises designed for spatially constrained environments
Description
We present a multimodal exercise dataset collected to support autonomous feedback systems for training in spatially constrained environments. Twenty healthy adults performed a structured set of whole-body exercises in limited space. The dataset includes surface EMG from four muscles, IMU kinematics (quaternions), wrist heart rate, and expert annotations of movement quality against predefined biomechanical criteria. Raw signals were filtered, segmented, synchronized, and EMG envelopes were derived. This resource enables development and validation of machine-learning models for automated assessment of exercise quality when professional supervision is not available.
Files
Steps to reproduce
Multimodal data were collected from 20 healthy adults during a structured 30-minute protocol of specialized, whole-body exercises conducted within a defined limited space to simulate ICE conditions. Surface electromyography signals were recorded with a Diers iEMG system from four muscles (biceps brachii, triceps brachii, rectus femoris, gastrocnemius lateralis) at 200 Hz and 1 kHz. Kinematic data were captured as quaternions using four Xsens MTx inertial measurement units at 100 Hz. Wrist heart rate was recorded using a Garmin Venu 2 Plus (software v19.05), providing PPG-derived heart rate and timestamps. Physiotherapists provided expert annotations on exercise quality based on direct observation during the sessions.
Institutions
- Ceske Vysoke Uceni Technicke v Praze Fakulta biomedicinskeho inzenyrstviKladno