A Survey on Radar-Based Continuous Human Activity Recognition

Abstract

Radar-based human motion and activity recognition is currently a topic of great research interest, as the aging population increases and older individuals prefer an independent lifestyle. This technology has a wide range of applications, such as fall detection in assisted living, gesture recognition for human-machine interfaces, and many more. Numerous studies exist on various approaches for radar-based activity capture and classification. However, most of these employ rather artificial data, often obtained in laboratory environments, and typically collected under particular conditions. Specifically, most research so far has aimed at distinguishing a predefined set of single activities with a defined start, stop and duration. This paper aims at drawing the attention to a so far less researched issue, one that will be of vital importance for future real-world application of radar-based human activity recognition: continuous activity recognition, i.e. recognizing specific activities in a stream of several sequential activities with unknown duration and arbitrary transitions between different classes of activities. A review on the current state of the art in this relatively new topic is given, followed by a discussion on future research directions.INDEX TERMS Radar applications, radar signal processing, continuous human activity recognition, activities of daily living.This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Anchor-Based, Real-Time Motion Compensation for High-Resolution mmWave Radar

“…Collectively, the wide generalizability, low algorithmic and hardware complexity, and real-time nature yield a distinct advantage over existing edge perception systems, with equivalent or superior motion suppression performance. While motion compensation is often associated with SAR, the proposed approach instead targets edge sensing applications for which hardware/resource complexity and response time is critical, including short-range automotive radar target detection and pedestrian micro-Doppler analysis/classification [3], human activity recognition [21], [66], early-warning collision detection and proximity sensing [66], industrial self-navigating autonomous robots, and wearable personal electronics (e.g., for gesture sensing, augmented reality, health monitoring, etc.) [67], among others.…”