Remote heart rate monitoring with contactless ambient technology using machine learning for aging population
K. Wang, S. Cao, J. Kaur, P. P. Morita
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AbstractAs the global population continue to age, tracking vital signs such as heart rate becomes crucial (Dias & Paulo Silva Cunha, 2018). However, traditional methods for monitoring these vital signs, particularly through wearable sensors, often present challenges. As noted in the study (Vijayan, Connolly, Condell, McKelvey, & Gardiner, 2021), wearables can be cumbersome for users, particularly older adults who may find these devices uncomfortable or intrusive, potentially leading to low adherence and compromising the reliability of health data collection. However, the rise of non-invasive Ambient Assisted Living (AAL) technology offers a more accessible solution (Lussier et al., 2020). By utilizing sensors embedded within the living environment, AAL systems can continuously gather vital health data without requiring direct interaction or causing discomfort to the individual. This research focuses on merging effortless smart home ambient technology and machine learning to predict the heart rate of old individuals. The primary aim is to accurately monitor heart rate during daily activities using non-intrusive, remote methods facilitated by AAL technology, without requiring active participation from the subjects.Keywords: ambient assisted living, smart home, machine learning, heart rate monitoring
K. Wang, S. Cao, J. Kaur, P. P. Morita (2024). Remote heart rate monitoring with contactless ambient technology using machine learning for aging population. Gerontechnology, 23(2), 1-1
https://doi.org/10.4017/gt.2024.23.s.886.opp