Deep Temporal Conv-LSTM for Activity Recognition

Human activity recognition has gained interest from the research community due to the advancements in sensor technology and the improved machine learning algorithm. Wearable sensors have become more ubiquitous, and most of the wearable sensor data contain rich temporal structural information that describes the distinct underlying patterns and relationships of various activity types. The nature of those activities is typically sequential, with each subsequent activity window being the result of the preceding activity window. However, the state-of-the-art methods usually model the temporal characteristic of the sensor data and ignore the relationship of the sliding window. This research proposes a novel deep temporal Conv-LSTM architecture to enhance activity recognition performance by utilizing both temporal characteristics from sensor data and the relationship of sliding windows. The proposed architecture is evaluated based on the dataset consisting of transition activities—Smartphone-Based Recognition of Human Activities and Postural Transitions dataset. The proposed hybrid architecture with parallel features learning pipelines has demonstrated the ability to model the temporal relationship of the activity windows where the transition of activities is captured accurately. Besides that, the size of sliding windows is studied, and it has shown that the selection of window size is affecting the accuracy of the activity recognition. The proposed deep temporal Conv-LSTM architecture can achieve an accuracy score of 0.916, which outperformed the state-of-the-art accuracy.