A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment
| العنوان: | A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment |
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| المؤلفون: | Ali Nadian-Ghomsheh, Bahar Farahani, Mohammad Kavian |
| المصدر: | Multimedia Tools and Applications |
| بيانات النشر: | Springer US, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Information privacy, Internet of things, Computer Networks and Communications, Computer science, Process (engineering), Context (language use), 02 engineering and technology, Computer security, computer.software_genre, Domain (software engineering), Telerehabilitation, Privacy-preserving, Health care, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Data Protection Act 1998, Range of motion measurement, Service system, 1194: Secured and Efficient Convergence of Artificial Intelligence and Internet of Things, business.industry, 020207 software engineering, Physical rehabilitation, Hardware and Architecture, Healthcare industry, Internet of Things, business, computer, Software |
| الوصف: | The healthcare industry requires the integration of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), to their full potential, particularly during this challenging time and the recent outbreak of the COVID-19 pandemic, which resulted in the disruptions in healthcare delivery, service operations, and shortage of healthcare personnel. However, every opportunity has barriers and bumps, and when it comes to IoT healthcare, data privacy is one of the main growing issues. Despite the recent advances in the development of IoT healthcare architectures, most of them are invasive for the data subjects. In this context, the broad applications of AI in the IoT domain have also been hindered by emerging strict legal and ethical requirements to protect individual privacy. Camera-based solutions that monitor human subjects in everyday settings, e.g., for Online Range of Motion (ROM) detection, are making this problem even worse. One actively practiced branch of such solutions is telerehabilitation, which provides remote solutions for the physically impaired to regain their strength and get back to their normal daily routines. The process usually involves transmitting video/images from the patient performing rehabilitation exercises and applying Machine Learning (ML) techniques to extract meaningful information to help therapists devise further treatment plans. Thereby, real-time measurement and assessment of rehabilitation exercises in a reliable, accurate, and Privacy-Preserving manner is imperative. To address the privacy issue of existing solutions, this paper proposes a holistic Privacy-Preserving (PP) hierarchical IoT solution that simultaneously addresses the utilization of AI-driven IoT and the demands for data protection. Furthermore, the efficiency of the proposed architecture is demonstrated by a novel machine learning-based system that allows immediate assessment and extraction of ROM as the critical information for analyzing the progress of patients. |
| اللغة: | English |
| تدمد: | 1573-7721 1380-7501 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d157bd6c5c614f3ea968e0fb5c0662cc http://europepmc.org/articles/PMC7882231 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....d157bd6c5c614f3ea968e0fb5c0662cc |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15737721 13807501 |
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