Inertial Sensors for Human Motion Analysis: A Comprehensive Review
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Abstract (arXiv)
Inertial motion analysis is having a growing interest during the last decades due to its advantages over classical optical systems. The technological solution based on inertial measurement units allows the measurement of movements in daily living environments, such as in everyday life, which is key for a realistic assessment and understanding of movements. This is why research in this field is still developing and different approaches are proposed. This presents a systematic review of the different proposals for inertial motion analysis found in the literature. The search strategy has been carried out on eight different platforms, including journal articles and conference proceedings, which are written in English and published until August 2022. The results are analyzed in terms of the publishers, the sensors used, the applications, the monitored units, the algorithms of use, the participants of the studies, and the validation systems employed. In addition, we delve deeply into the machine learning techniques proposed in recent years and in the approaches to reduce the estimation error. In this way, we show an overview of the research carried out in this field, going into more detail in recent years, and providing some research directions for future work
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2024.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Datacited by 17
- 2024Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantificationcited by 1
- 2025TrackStudio: An Integrated Toolkit for Markerless Trackingcited by 0
- 2026Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learningcited by 0
- 2026Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning12 Feb 2026
- 2025TrackStudio: An Integrated Toolkit for Markerless Tracking10 Nov 2025
- 2024RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data27 Nov 2024
- 2024Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantification22 May 2024