When ehealth meets iot: A smart wireless system for post-stroke home rehabilitation
Abstract
In recent years, the emerging framework of the Internet of Things has been leading the technological landscape in a number of different fields and applications, from autonomous and connected vehicles to wearable devices. The healthcare system is benefiting from this continuously evolving environment since it leverages the opportunities offered by the ubiquitous and pervasive presence of connected objects and smart services. This attitude has given rise to the concept of eHealth, thus enabling new approaches and solutions for healthcare. In this framework we propose SmartPants, an IoT-based wireless system specifically designed for the remote rehabilitation of lower limbs in post-stroke patients. The platform consists of multiple nodes used to monitor physical therapy and a software platform that provides real-time feedback on the execution by recognizing the type of exercise currently being performed by the patient. Our experimental results, evaluated through appropriate metrics, show that the proposed movement recognition algorithm provides very good results in terms of classification performance, independent of the considered classifier, with an average true positive rate of about 91 percent and an overall accuracy of around 96.5 percent.
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All of the reviewed reasons resulted in IoT systems locally processing data.
“All of these reasons resulted in IoT systems locally processing data [41,56,55].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [55]
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Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., & McDonald-Maier, K. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103197 · Cited by 180 · see citing works
“Random Forrest (RF) and more specifically Random Trees (DT) were also massively employed [44,46,47,49,55,60,61,65,67,69].”
BackgroundMethodResultInfluentialExplainable AI for 6G Use Cases: Technical Aspects and Research Challenges
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- Authors
- Bisio, I., Garibotto, C., Lavagetto, F., & Sciarrone, A.
- Affiliations
- University of Genoa (IT)
- Venue
- IEEE Wireless Communications
- Published
- 2019-12
- Volume
- 26
- Issue
- 6
- Pages
- 24-29
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W2997454486
- Cited by
- 79
- References
- 14
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 15 · 2023: 13 · 2022: 12 · 2021: 15 · 2020: 6
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Wireless Communications (publishedVersion)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- Internet of Things, e-health, stroke rehabilitation, lower limb rehabilitation, wireless monitoring system, motion recognition, exercise classification, real-time feedback
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