LSTM and HMM Comparison for Home Activity Anomaly Detection
Soon-Chang Poh, Yi-Fei Tan, Xiaoning Guo, Soon-Nyean Cheong, Chee‐Pun Ooi, Wooi-Haw Tan · 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2019
Behavioral changes in daily home activities may be linked with health problems. Therefore, anomaly detection on sequence pattern of home activities is important for healthcare monitoring. In this paper, an anomaly detection method based on Long Short-Term Memory (LSTM) neural network is proposed to detect anomalies on sequence pattern of home activities. A comparison study of LSTM and Hidden Markov Model (HMM) was conducted to evaluate their performance under different training set size and model's hyperparameters. The experimental results demonstrated that LSTM is comparable to HMM in detecting anomalies on sequence pattern of home activities. The test accuracies of the best LSTM and HMM models are both 87.50%.