Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task

TL;DR

This survey reviews the evolution of sensor-based human activity recognition, highlighting challenges, recent advances, and future directions, while providing practical guidance for developing real-world HAR systems.

Contribution

It offers a comprehensive overview of HAR history, current state, and future prospects, along with a practical tutorial for practitioners to implement effective HAR solutions.

Findings

Performance on benchmarks has plateaued despite increased data and complexity.

Incorporation of world knowledge from foundational models is a promising new paradigm.

The tutorial aids practitioners in deploying HAR systems in real-world scenarios.

Abstract

In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to recognize activities. Substantial gains have been made since the days of hand-crafting heuristics as features, yet, progress has seemingly stalled on many popular benchmarks, with performance falling short of what may be considered 'sufficient'-- despite the increase in computational power and scale of sensor data, as well as rising complexity in techniques being employed. The HAR community approaches a new paradigm shift, this time incorporating world knowledge from foundational models. In this paper, we take stock of sensor-based HAR -- surveying it from its beginnings to the current state of the field, and charting its future. This is accompanied by a hands-on tutorial, through which we guide practitioners in developing HAR systems for real-world application scenarios. We provide a compendium for novices and experts alike, of methods that aim at finally solving the activity recognition problem.