Liang, J.; Au, A.; Chen, M.; Chan, C.; Zheng, J.; DeVries, Z.; Xiao, Y.; Dhesi, P. Skeleton-based Privacy-Preserving Smart Activity Sensor for Senior Care and Patient Monitoring. Preprints2024, 2024010108. https://doi.org/10.20944/preprints202401.0108.v1
APA Style
Liang, J., Au, A., Chen, M., Chan, C., Zheng, J., DeVries, Z., Xiao, Y., & Dhesi, P. (2024). Skeleton-based Privacy-Preserving Smart Activity Sensor for Senior Care and Patient Monitoring. Preprints. https://doi.org/10.20944/preprints202401.0108.v1
Chicago/Turabian Style
Liang, J., Ying Xiao and Paeton Dhesi. 2024 "Skeleton-based Privacy-Preserving Smart Activity Sensor for Senior Care and Patient Monitoring" Preprints. https://doi.org/10.20944/preprints202401.0108.v1
Abstract
This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.
Keywords
Activity Sensor; Fall Detection; Fall Risk Assessment; Privacy Protection; Smart Senior Care; Medical Alert System; Personal Emergency Response system; Remote Patient Monitoring, Remote Therapeutic Monitoring
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
