Recognition of Real-life Activities with Smartphone Sensors using Deep Learning Approaches

Due to its vast applications in various industrial sectors, sensor-based human activity recognition (SHAR) has become a prevalent study issue in machine learning (ML) and deep learning (DL). With the improvement of numerous wearable sensors, many effective use cases have recently been revealed. According to recent research, real-world data contains more contextual information than data acquired in a laboratory environment. Three deep learning models were used to investigate real-life activities using smartphone sensors in this study. As two fundamental deep learning approaches, a convolutional neural network (CNN) and a long short-term memory (LSTM) network are used to achieve recognition. In addition, we introduced the Att-CNN-LSTM network as a hybrid DL model to handle the SHAR challenge using an attention mechanism. On a public dataset called real-life HAR (RL-HAR), these three deep learning models were evaluated using four assessment indicators: accuracy, precision, recall, and F1-score. According to the experimental data, the suggested Att-CNN-LSTM surpasses existing baseline deep learning models with the highest average accuracy of 95.76%.