Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

Abstract

Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents’ health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident’s activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.

Health Monitoring Using Smart Home Technologies: Scoping Review

“…Spinal cord injury, muscular dystrophy, multiple sclerosis, polio >45, 2 males, 3 females 5 Chen et al [14] Healthy >18 11 Bock et al [15] Chronic diseases >55 10 Fritz and Dermody [16] Chronic diseases >70 34 Skubic et al [17] Healthy >18, 72 males, 191 females 263 Dawadi et al [18] Chronic diseases >65, 7 males, 30 females 37 Choi et al [19] No data No data 6 Clemente et al [20] Healthy and cardiac conditions No data 32 Pigini et al [21] Healthy >65 13 Monteriù et al [22] No data >65 13 Grgurić et al [23] Healthy >70, 1 male, 1 female 2 Dasios et al [24] Healthy >30, 11 males, 12 females 23 Marcelino et al [25] Chronic diseases >65, 1 female 1 Yu et al [26] Proof of concept (n=7) Depression >65 20 Kim et al [27] Healthy >18 29 Alberdi Aramendi et al [10] N/A N/A a 0 Hassan et al [28] No data >65 1 Shirali et al [29] No data >60, 10 males, 12 females 22 Jung [30] N/A No data 0 Alsina-Pagès et al [31] Healthy No data 1 Mahmoud et al [32] Algorithm evaluation (n=6) Healthy >18 1 Jakkula and Cook [33] Healthy >18 40 Rashidi et al [34] Healthy No data 40 Singla et al [35] N/A N/A 0 Damodaran et al [36] No data No data 19 Hamad et al [37] Dementia No data 12 Enshaeifar et al [38]…”

“…From 2013 to 2020, the proof of concept improved from synthetic data to real-world data, single indi-Alberdi Aramendi et al [10], Kim et al [27], Hassan et al [28], Shirali et al machine as the typical model with vidual to multi-individual, but the [29], Jung [30], Alsina-many of the studies; the recent objectives more or less-the same Pagès et al [31], Mahmoud et al [32] study used the parallel activity log inference algorithm activity recognition, anomaly detection, pattern recognition to improve the quality of life of older individuals…”

“…All the studies reported that IoT improved the quality of care, increased participants' sense of comfort, enabled early detection, and increased participants' understanding of the impact of health events on overall health. The health indicators specifically measured through smart home technologies included fall detection [24], functional health decline/improvement [10], high-level activities of daily living/instrumental activities of daily living [34,35,48,50,59,[61][62][63], leisure services [59], loneliness [55], medical services [17,21,30,64], patient health status [17,21,30,64], perception [58], physical activity [48], sedentary behaviors [24,62], medication adherence [62], movement patterns [29], sequence of gestures [61], sleep…”