Knowledge Mapping of Human Activity Recognition Techniques for Assistive Living

Abstract

Purpose: Human Activity Recognition (HAR) is a subject of research that identifies an individual’s activities for assistive living. The proliferation of ICT and sensor technology prompted HAR to flourish beyond unfathomable levels, having immense human-centric applications. The development of accurate HAR systems involves complex statistical and computational tasks from signal acquisition to activity classification. This research aims to conduct a systematic review of recent techniques proposed for each stage of HAR application development. Methodology: The review is conducted following Kitchenham principles, using Scopus and Web of Science databases. Firstly, research questions were formulated, followed by the search strategy definition. Based on assessment criteria, 193 papers are shortlisted and thoroughly analyzed to extract research- related information. Results: The techniques identified in 193 articles are comprehensively mapped from four aspects: data acquisition, data preprocessing and feature engineering, learning algorithm, and evaluation. Each technique is examined for its strengths and limitations to assist application developers in selecting the best one for their needs. The prevailing challenges and upcoming research opportunities are thoroughly explored. Conclusion: The ever-expanding literature in the field necessitated an update to the status of HAR literature. Compared to other reviews that focused on specific methods, fields of application, and datatypes, to the best of our understanding, this is the first evaluation of its kind that provides a broader mapping of HAR approaches. The findings of this analysis will provide researchers and newcomers in the field an up-to-date and holistic view of the complete body of work in this area.