Monitoring of Older Adults’ Daily Activity and Sleep with Xiaomi Mi Band 2

Miranda-Duro, María del Carmen; Nieto-Riveiro, Laura; Groba, Betania; Canosa, N.

doi:10.3390/engproc2021007050

Cited by 2 publications

(1 citation statement)

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“…Nowadays, wearable devices such as smartwatches and activity trackers, monitor and estimate some basic activity levels related to physical activity or sleep quality [12]. Even though they are good candidates for tracking the daily routine and studies have shown their utility in monitoring activities [13] in the case of older adults, only a few solutions go beyond physical and sleep activities [14]. Moreover, the daily routine defined as frequent sequences of activities, with a similar timespan is not identified and exploited even though they may be very relevant in managing chronic diseases or dementia often encountered in the elderly population [15].…”

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confidence: 99%

Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns

Chifu

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et al. 2022

Sensors

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The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.

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