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. 2022 Jan 27;22(3):992.
doi: 10.3390/s22030992.

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Viorica Rozina Chifu  1 , Cristina Bianca Pop  1 , David Demjen  2 , Radu Socaci  3 , Daniel Todea  1 , Marcel Antal  1 , Tudor Cioara  1 , Ionut Anghel  1 , Claudia Antal  1
Affiliations

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Viorica Rozina Chifu et al. Sensors (Basel). .

Abstract

As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers' interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95.

Keywords: Beacons; Markov model; activities of daily living; daily routine; deviations from routines; entropy rate and cosine functions.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Daily activities and transition probabilities (A1 to A2 transitions are marked with yellow).
Figure 2
Figure 2
Activity selection in baseline detection process.
Figure 3
Figure 3
Experimental system for ADL monitoring.
Figure 4
Figure 4
Variation of assessed distance from the Beacon compared with the actual one.
Figure 5
Figure 5
Location results on x and y-axis using trilateration for three Beacons.
Figure 6
Figure 6
Activity of daily living identification rules.
Figure 7
Figure 7
Activities of daily living monitored for a day.
Figure 8
Figure 8
Daily routine extracted for older adult M1.
Figure 9
Figure 9
Deviation detection process on the testing data set for older adult M1.
Figure 10
Figure 10
Example of day with activity sequence anomalies for the older adult M1.
Figure 11
Figure 11
Example of day classified as featuring activity duration anomalies.
Figure 12
Figure 12
Parameter learning features: (left) Learning time and (right) probability activity transition (sleep -> eating).
See this image and copyright information in PMC

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