A new approach for physical human activity recognition from sensor signals based on motif patterns and long-short term memory

Abstract

Numerous studies have been carried out in recent years on the recognition, tracking, and discrimination of human activities. Automatic recognition of physical activities is often referred to as human activity recognition (HAR). There are generally vision-based and sensor-based approaches for activity recognition. The computer vision-based approach generally works well in laboratory conditions, but it can fail in real-world problems due to clutter, variable light intensity, and contrast. Sensor-based HAR systems are realized by continuously monitoring and analyzing physiological signals measured from heterogeneous sensors connected to the person's body. In this study, the Motif Patterns (MP) approach, which extracts features from sensor signals, is proposed for HAR. The success of the HAR systems depends on the effectiveness of the features extracted from the signals. The LSTM network is a special kind of recurrent neural network that has been used to make very successful predictions on time series data where long-term dependencies are. The LSTM network type offers a successful solution approach to solving long-term dependencies problems such as human activity recognition. The classification process was carried out with Long-Short Term Memory (LSTM) using MP features extracted from accelerometer, gyroscope, and magnetometer sensor signals. A large dataset of 9120 signals was used to test the proposed approach. A high success rate of 98.42 % was achieved with the proposed MP + LSTM method. As a result, it has been seen that the proposed approach has been obtained with a high success rate for HAR using sensor signals.