Research on an Anomaly Detection Method for Physical Condition Change of Elderly People in Care Facilities

Abstract

Currently, the shortage of care workers for the elderly has become a big problem, and more streamlined care operations are needed. In care facilities, care workers are required to use their subjective experience to detect anomalies in physical condition of care receivers, including serious or insigni cant deterioration or behavioral and psychological symptoms of dementia, which can decrease the work ef ciency. Therefore, we aim to create a model using objective data for detecting anomalies in physical condition. In this study, data from 13 subjects in a care facility were collected, and isolation forest models were constructed for each subject. The subjectʼs anomalies in physical condition were documented in a care record by a nurse and used as reference for model evaluation. Recall and speci city were used to evaluate the model, expressed as the percentage of detection success for abnormal or normal conditions. Data collected for 1 to 60 days were used to train the isolation models, and the relationship between the amount of training data and model performance was simulated. Heart rate, respiratory rate, and time of getting out of bed were collected from a sensor placed on the subjectʼs bed and used as the model features. In addition, dietary intake information was collected from the care record. Analysis of the evaluation results showed recall and speci city of 45.6 ± 46.7% and 83.88 ± 6.06%, respectively, for the model constructed using training data of 60 days. For future studies, we will continue to collect data and increase the number of participants to improve the robustness and accuracy of the proposed anomaly detection system.

Redesigned Monitoring System for Television Usage and Light/Dark Room for Older People Living Alone

“…In addition, the new system does not display the past status records of the sensors. Shiotani and Yamaguchi [9] created a model to detect anomalies in the physical condition of older people in care facilities. For this, they used the heart rate, respiratory rate, and time of getting out of bed of the older people [9].…”

“…Shiotani and Yamaguchi [9] created a model to detect anomalies in the physical condition of older people in care facilities. For this, they used the heart rate, respiratory rate, and time of getting out of bed of the older people [9]. Similarly, past sensor status could be helpful in monitoring the daily life of older people.…”

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“…A shallow sample close to the root will have a score close to 1 [69]. The selection of the Isolation Forest algorithm for detecting changes in productivity was motivated by [70]. Isolation Forest works well for low-dimension data, even with unimportant attributes and contexts where anomalies or changes are absent in the training set [67].…”

“…Isolation Forest works well for low-dimension data, even with unimportant attributes and contexts where anomalies or changes are absent in the training set [67]. Based on these capabilities, Shiotani and Yamaguchi [70] demonstrated the ability of the Isolation Forest to detect changes in patients' physical condition (heart rate and dietary intake) in a smart care facility.…”