Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures

Abstract

Objective: In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this work is concerned with solving the intra motion category classification problem of gait recognition. Methods: New gait classification approaches utilizing physical features, subspace features and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological and assisted walks. Results: The proposed approaches are shown to outperform existing methods for radarbased gait recognition which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual a classification accuracy on the order of 80% can be expected. Conclusion: Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Significance: Radar-based sensing of gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

“…Seifert [127] used radar-based applications to perform unobtrusive person identification based on In-home gait analysis. A K-band radar was used to collect data from four test subjects.…”

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

“…The spectrogram has been employed in gesture recognition [11] and human activity recognition (HAR) [12]. It has been used for the detection of gait asymmetry in [3,13], distinguishing between armed and unarmed persons for security services [14], and fall detection [15][16][17], as well.…”

Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network

“…In 2018, Ann-Kathrin et al studied motion classification based on radar micro-Doppler characteristics [ 6 ]. This paper presents a new classification method based on physical features, subspace features and harmonic models.…”

“…Most gait recognition methods that combine radar sensors with DL mainly use convolution neural networks (CNN) to extract and recognize features of micro-Doppler signatures [ 6 , 7 , 31 ]. The birth of the 3-D spatiotemporal CNN provides a better method for processing the gait information of a time sequence [ 32 , 33 , 34 ].…”