A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques

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.

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Chifu

Pop

Demjen

et al. 2022

“…Context features were the day of the week, weather, season, noise levels, visitor presence, etc. In [ 11 ], normal behavior patterns were defined as lists of activities that a resident performs in their house, with the time of the day and duration. Lists were made from recorded data.…”

Section: Related Workmentioning

confidence: 99%

“…What is unusual behavior of a resident? It is behavior that deviates from their routine [ 11 ]. For example, if a resident leaves home frequently but suddenly is at home almost all the time, it could indicate social isolation.…”

Section: Introductionmentioning

confidence: 99%

Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences

Maučec

Donaj

2021

The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. Approaches that recognize activities from simple environment sensors have been shown to perform well. It is also important to know the habits of a resident to distinguish between common and uncommon behavior. In this paper, we propose a novel approach to discover a person’s common daily routines. The approach consists of sequence comparison and a clustering method to obtain partitions of daily routines. Such partitions are the basis to detect unusual sequences of activities in a person’s day. Two types of partitions are examined. The first partition type is based on daily activity vectors, and the second type is based on sensor data. We show that daily activity vectors are needed to obtain reasonable results. We also show that partitions obtained with generalized Hamming distance for sequence comparison are better than partitions obtained with the Levenshtein distance. Experiments are performed with two publicly available datasets.

“…Several state-of-the-art studies [14,[16][17][18] are addressing the detection of older adults' daily routines. However, most approaches use small sequences of activities or even one type of activity and do not consider the entire day.…”

Section: Introductionmentioning

confidence: 99%

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

Chifu

Pop

Rancea

et al. 2022

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%.