Class-Incremental Continual Learning for Human Activity Recognition with Motion Sensors

Many real-world applications, such as smart homes, personal healthcare and fitness tracking, benefit from sensor-based human activity recognition (HAR), which identifies the patterns of human activities. Machine learning models are trained on the data collected from sensors, mostly the motion sensors, embedded in wearable devices. However, in this approach, a model cannot learn new tasks independently without total re-learning. The continual learning approach has emerged to tackle this problem. Various techniques have been proposed to enable continual learning, as it has been widely studied in computer vision. This paper suggests a framework for assessing how well different settings of a replay-based technique perform over a large HAR dataset under class incremental continual learning scenarios. Experimental results show that a larger sample size and random sampling method for replay data selection provide accuracy results which are close to the upper bound where all data is available at the start.