Machine Learning Techniques for Sensor-based Human Activity Recognition
  with Data Heterogeneity -- A Review
Xiaozhou Ye Kouichi Sakurai Nirmal Nair Kevin I-Kai Wang
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous
computing, analysing behaviours through multi-dimensional observations. Despite
research progress, HAR confronts challenges, particularly in data distribution
assumptions. Most studies often assume uniform data distributions across
datasets, contrasting with the varied nature of practical sensor data in human
activities. Addressing data heterogeneity issues can improve performance,
reduce computational costs, and aid in developing personalized, adaptive models
with less annotated data. This review investigates how machine learning
addresses data heterogeneity in HAR, by categorizing data heterogeneity types,
applying corresponding suitable machine learning methods, summarizing available
datasets, and discussing future challenges.