Abstract
Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods.
Highlights
Human activity recognition (HAR) is one of the efforts that can be completed in developing an intelligent environment that can be utilized for monitoring daily activities [1]
Abnormal behaviour is refers to abnormal activity that caused by cognitive decline and it cause early symptoms of dementia at 30-50 years old [5]
The Long Short Term Memory (LSTM) model was built with 1 LSTM layer of 512 neurons, 2 dense layers with different activation function for each experiments with dropout 0.8, and in the last layer is 1 dense layer with softmax activation
Summary
Human activity recognition (HAR) is one of the efforts that can be completed in developing an intelligent environment that can be utilized for monitoring daily activities [1]. Abnormal behaviour is defined as a decrease in physical activity in carrying out daily routines [4]. Symptoms of dementia can be identified by monitoring behavioral variations such as disturbance in sleep, difficulty in walking and inability to complete an activity. The research area of HAR shows that modeling activities sequentially over time or time series modeling can be used as indicators to early detection of dementia [8]
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