ArticleScientific data2025
A Human Motion Data Capture Study The University of Liverpool Rehabilitation Exercise Dataset.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Automated Control of Rehabilitation Process in Physical Therapy Using a Novel Human Skeleton-Based Balanced Time Warping Algorithm.Sensors (Basel, Switzerland) · 2025Article
- A Human Motion Data Capture Study The University of Liverpool Rehabilitation Exercise Dataset.Scientific data · 2025Article
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Authors and funding
4 authors.
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Abstract
The increasing accessibility of motion tracking technologies has resulted in a large amount of research focused on delivering exercise-based interventions remotely, coined under the term telerehabilitation. High quality human motion data is an essential component in the development and evaluation of Human Action Recognition (HAR) research, which plays a large role in exercise-based telerehabilitation. However, there is a lack of such human motion datasets for this domain, which hinders fast progress. This work presents a new human motion dataset named University of Liverpool Rehabilitation Exercise Dataset (UL-RED) containing 22 non-specialised exercises across 10 subjects and three data modalities: marker-based and marker-less motion tracking, and depth data. This dataset is the first to include motion repetitions of varying motion speeds, where subjects performed repetitions at a normal, fast, and slow pace. A total of 1,320 recordings were collected across the three data modalities, with over three hours of marker-based and marker-less motion tracking. This dataset is not only useful in the telerehabilitation landscape, but also within the wider field of HAR.
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