TransAct: Transfer learning enabled activity recognition

Abstract

Activity recognition using smartphone has great potential in many applications like healthcare, obesity management, abnormal behavior detection, public safety and security etc. Typical activity detection systems are built on to recognize a limited set of activities that are present in the training and testing environments. However, these systems require similar data distributions, activity sets and sufficient labeled training data in both training and testing phases. Therefore, inferring new activities is challenging in practical scenarios where training and testing environments are volatile, data distributions are diverge and testing environment has new set of activities with limited training samples. The shortage of labeled training data samples also degrades the activity recognition performance. In this work, we address these challenges by augmenting the Instance based Transfer Boost algorithm with k-means clustering. We evaluated our TransAct model with three public datasets - HAR, MHealth and DailyAndSports and demonstrated that our TransAct model outperforms traditional activity recognition approaches. Our experimental results show that our TransAct model achieves ≈ 81% activity detection accuracy on average.