Baseline Model Training in Sensor-Based Human Activity Recognition: An Incremental Learning Approach

Abstract

Human activity recognition (HAR) based on wearable sensors has attracted significant research attention in recent years due to its advantages in availability, accuracy, and privacy-friendliness. HAR baseline model is essentially a general-purpose classifier trained to recognized multiple activity patterns of most user types. It provides the input for subsequent steps of model personalization. Training a good baseline model is of fundamental importance because it has significant impacts on the ultimate HAR accuracy. In practice, baseline model training in HAR is a non-trivial problem that faces two challenges: insufficient training data and biased training data. This paper proposes a novel baseline model training scheme to tackle the two challenges using Deep InfoMax (DIM)-based unsupervised feature extraction and Broad Learning System (BLS)-based incremental learning, respectively. Experimental results demonstrate that the proposed scheme outperform conventional methods in terms of overall accuracy, computational efficiency, and the ability to adapt to dynamic scenarios with changing data characteristics. INDEX TERMS Human activity recognition, baseline model, broad learning system, DIM, incremental learning.

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“…Some studies have recognized human activity with sensors [30]- [44]. For example, the study in [32] proposed an action tutor system that achieved high-level evaluation of human action movements with the aid of Kinect.…”