A multimodal EMG and IMU dataset for assessing the quality of exercises designed for spatially constrained environments

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.