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Advances in wearable technology and applications in physical medicine and rehabilitation
Open accessAbstract
The development of miniature sensors that can be unobtrusively attached to the body or can be part of clothing items, such as sensing elements embedded in the fabric of garments, have opened countless possibilities of monitoring patients in the field over extended periods of time. This is of particular relevance to the practice of physical medicine and rehabilitation. Wearable technology addresses a major question in the management of patients undergoing rehabilitation, i.e. have clinical interventions a significant impact on the real life of patients? Wearable technology allows clinicians to gather data where it matters the most to answer this question, i.e. the home and community settings. Direct observations concerning the impact of clinical interventions on mobility, level of independence, and quality of life can be performed by means of wearable systems. Researchers have focused on three main areas of work to develop tools of clinical interest: 1)the design and implementation of sensors that are minimally obtrusive and reliably record movement or physiological signals, 2)the development of systems that unobtrusively gather data from multiple wearable sensors and deliver this information to clinicians in the way that is most appropriate for each application, and 3)the design and implementation of algorithms to extract clinically relevant information from data recorded using wearable technology. Journal of NeuroEngineering and Rehabilitation has devoted a series of articles to this topic with the objective of offering a description of the state of the art in this research field and pointing to emerging applications that are relevant to the clinical practice in physical medicine and rehabilitation.
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“Finally, applications in rehabilitation of remote monitoring systems relying on wearable sensors [36] have largely relied upon inertial sensors for movement detection and tracking.”
From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [36]
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Self-citationEskofier, B. M., Paulus, J., Paulsen, U., Burkart, M., Wullich, B., & Huppert, V. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943654 · Cited by 2
Mondal, S. I., & Ghosh, R. P. (2016) · International Conference on Microelectronics, Computing and Communications · doi:10.1109/microcom.2016.7522409 · Cited by 2
Wearable Systems for Monitoring Mobility Related Activities
Zijlstra, W., Becker, C., & Pfeiffer, K. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-561-2.ch312 · Cited by 2
Metadata
- Authors
- Bonato, P.
- Affiliations
- Harvard University (US)
- Corresponding author
- Bonato, Paolo
- ORCID
- Bonato, Paolo
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2005-02-25
- Volume
- 2
- Issue
- 1
- Pages
- 2-2
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC552335
- OpenAlex
- W2095736475
- Cited by
- 385
- References
- 25
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 22 · 2023: 18 · 2022: 20 · 2021: 14 · 2020: 16 · 2019: 17 · 2018: 31 · 2017: 22 · 2016: 23 · 2015: 22 · 2014: 22 · 2013: 20 · 2012: 21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- wearable sensors, rehabilitation medicine, remote patient monitoring, mobility assessment, physiological signal recording, sensor fusion, quality of life
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