Abstract
The number of elderly people suffering from dementia, a senile disease, is increasing day by day due to the rapid aging of the population. As a result, social and economic costs are also gradually increasing. To prevent such monetary losses, a system that can operate at a low cost is needed to care for dementia patients. Therefore, this research proposes a sensor-based deviant behavior detection system that allows caregivers to easily manage dementia patients even if they are not in the same location as their dementia patients at a low cost. In this research, the autoencoder and the LSTM models were used together, because deviance behavior is difficult to obtain labeled data. The autoencoder model is a representative unsupervised learning model, which can be used to extract characteristics of data, and was used to learn characteristics of normal behavioral data. The LSTM model is used to determine the deviant behavior from output outlier data that exceeds the threshold in the autoencoder. As a result of the experiment, each model achieved more than 96% and more than 99% accuracy. This research is expected to help caregivers of dementia patients manage the elderly with dementia more inexpensively and efficiently.
Highlights
Due to improvements in living environments and medical development, rapid population aging is progressing worldwide
This research detected the deviant behavior of dementia patients through data generated from sensors that can be attached to objects in nursing homes or homes
It can be seen that the autoencoder model can find a pattern of normal behavior, and it can be seen that the LSTM model makes the results generated in the autoencoder more sophisticated
Summary
Due to improvements in living environments and medical development, rapid population aging is progressing worldwide. This research defines and creates behaviors that can be obtained from daily life of dementia patients according to the schedule of nursing home using general sensor data. The data were generated assuming that the elderly or patients in the nursing hospital behaved according to the same schedule as Fig. 5 In cases where the results of the calculated outlier exceed the constant standard value, it again enters the input into the LSTM model, and the result of 0 (normal), 1 (insomnia) and 2 (repeat behavior) are output Both models receive the same input data, so the input size is the same as 36. The LSTM model has one 32-size hidden layer and has an output size of 3
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