Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments
- PMID: 33148315
- PMCID: PMC7640711
- DOI: 10.1186/s12984-020-00779-y
Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments
Abstract
Background: Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.
Methods: A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.
Results: Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.
Conclusion: This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.
Keywords: Accelerometer; Activities of daily living; Algorithms; Disabled persons; Gyroscope; Inertial measurement unit; Machine learning; Patients; Pattern recognition; Rehabilitation.
Conflict of interest statement
The authors declare that they have no competing interests.
Figures


Similar articles
-
Protocol of a systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments.Syst Rev. 2018 Oct 24;7(1):174. doi: 10.1186/s13643-018-0824-4. Syst Rev. 2018. PMID: 30355320 Free PMC article.
-
Accuracy of Sensor-Based Measurement of Clinically Relevant Motor Activities in Daily Life of Children With Mobility Impairments.Arch Phys Med Rehabil. 2024 Jan;105(1):27-33. doi: 10.1016/j.apmr.2023.05.015. Epub 2023 Jun 15. Arch Phys Med Rehabil. 2024. PMID: 37329967
-
Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective.J Med Internet Res. 2023 Jul 27;25:e44428. doi: 10.2196/44428. J Med Internet Res. 2023. PMID: 37498655 Free PMC article.
-
Applications of wearable sensors in upper extremity MSK conditions: a scoping review.J Neuroeng Rehabil. 2023 Nov 18;20(1):158. doi: 10.1186/s12984-023-01274-w. J Neuroeng Rehabil. 2023. PMID: 37980497 Free PMC article.
-
Trade-Offs Between Simplifying Inertial Measurement Unit-Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations.JMIR Mhealth Uhealth. 2025 Jun 3;13:e58078. doi: 10.2196/58078. JMIR Mhealth Uhealth. 2025. PMID: 40460316 Free PMC article.
Cited by
-
Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series.Int J Nurs Stud Adv. 2022 Dec;4:100081. doi: 10.1016/j.ijnsa.2022.100081. Epub 2022 May 25. Int J Nurs Stud Adv. 2022. PMID: 35642184 Free PMC article.
-
Using Inertial Sensors to Determine Head Motion-A Review.J Imaging. 2021 Dec 6;7(12):265. doi: 10.3390/jimaging7120265. J Imaging. 2021. PMID: 34940732 Free PMC article. Review.
-
A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment.Sensors (Basel). 2023 Apr 14;23(8):4000. doi: 10.3390/s23084000. Sensors (Basel). 2023. PMID: 37112339 Free PMC article.
-
Healthcare Application of In-Shoe Motion Sensor for Older Adults: Frailty Assessment Using Foot Motion during Gait.Sensors (Basel). 2023 Jun 8;23(12):5446. doi: 10.3390/s23125446. Sensors (Basel). 2023. PMID: 37420613 Free PMC article.
-
Sensor-based outcomes to monitor everyday life motor activities of children and adolescents with neuromotor impairments: A survey with health professionals.Front Rehabil Sci. 2022 Oct 12;3:865701. doi: 10.3389/fresc.2022.865701. eCollection 2022. Front Rehabil Sci. 2022. PMID: 36311205 Free PMC article.
References
-
- World Health Organization. Towards a Common Language for Functioning, Disability and Health ICF. 2002. https://www.who.int/classifications/icf/icfbeginnersguide.pdf. Accessed 21 Mar 2017.
-
- World Health Organization. World report on disability. 2011. https://www.who.int/disabilities/world_report/2011/en/. Accessed 5 July 2017.
-
- Del Din S, Hickey A, Woodman S, Hiden H, Morris R, Watson P, et al. Accelerometer-based gait assessment: Pragmatic deployment on an international scale. In: Proc IEEE Stat Signal Process Workshop. 2016; p. 1–5.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources