t-SNE and PCA in Ensemble Learning based Human Activity Recognition with Smartwatch

Smartwatch based Human Activity Recognition (HAR) is gaining popularity due to habitual unhealthy behavior of the population and the rich in-built sensors of smartwatch. Raw sensor data is not well suited for the classifiers to identify similar activity patterns. According to the HAR literature handcrafted features are beneficial to properly identify the activities, which is time consuming and need expert domain knowledge. Automatic feature extraction libraries give high-dimensional feature sets that increase the computation and memory cost. In this work, we present an Ensemble Learning framework that exploit dimensional reduction and visualization to improve performance specification. Specifically, using Time Series Feature Extraction Library (TSFEL), the high dimensional features are extracted automatically. Then, to reduce the dimension of the feature set and proper visualization, Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used respectively. The relevant extracted features using PCA are fed to an ensemble of three different Machine Learning (ML) classifiers to identify six different human physical activities. We also compare the proposed method with three popularly used shallow ML methods. Self collected human activity smartwatch sensor signal is used to establish the feasibility of the proposed framework. We observe that the proposed framework outper-forms the existing state-of-the-art benchmark frameworks, with an accuracy of 96%.