Gait classification based on micro-Doppler features

Yang, Le; Li, Gang; Ritchie, Matthew; Fioranelli, Francesco; Griffiths, Hugh

doi:10.1109/radar.2016.8059301

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…According to the above results, the classification accuracy rate of the system proposed in previous studies [17,24,30] cannot meet the basic classification requirements in the case of non-target micro-motion interference. However, the system proposed in this paper can resist interference effectively and still have high classification accuracy under interference.…”

Section: Experimental Evaluationmentioning

confidence: 93%

“…Next, we compare the other three human motion state classification systems with the proposed system in the scenarios with non-target micro-motion interference. In a previous study [30], the authors used S-band continuous wave radar with a carrier frequency of 9.8 GHz to collect target human motion data, and classified the three motion states of walking with no bag, walking with one bag held by one hand, and walking with one bag held by both hands. Firstly, the micro-Doppler signatures are obtained by performing time-frequency analysis on the radar data.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference

Zhao

Liu

et al. 2019

Sensors

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Human motion classification based on micro-Doppler effect has been widely used in various fields. However, the motion classification performance would be greatly degraded if the wireless environment has non-target micro-motion interference. In this case, the interference signal aliases with the signal of target human motions and then generates cross-terms, making the signals hard to be used to identify target human motions. Existing methods do not consider this non-target micro-motion interference and have poor resistance to such interference. In this paper, we propose a target human motion classification system that can work in the scenarios with non-target micro-motion interference. Specifically, we build a continuous wave radar transceiver working in a low-frequency radar band using the software defined radio equipment Universal Software Radio Peripheral (USRP) N210 to collect signals. Moreover, we use Empirical Mode Decomposition and S-transform successively to remove non-target micro-motion interference and improve the time-frequency resolution of the raw signal. Then, an Energy Aggregation method based on S-method is proposed, which can suppress cross-terms and background noise. Furthermore, we extract a set of features and classify four human motions by adopting Bagged Trees. Extensive experiments using the test-bed show that under the scenarios with non-target micro-motion interference, 97.3% classification accuracy can be achieved.

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Section: Experimental Evaluationmentioning

confidence: 93%

Section: Experimental Evaluationmentioning

confidence: 99%

Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference

Zhao

Liu

et al. 2019

Sensors

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“…The overall accuracy of the proposed solution improved from 80% to 98% when multi-sensory approach was adopted. Another example of radar sensing for activity recognition is in [36]. This work focused on the identification of three kind of human gaits, i.e., walking without carrying hand-bag, walking with holding a hand-bag Figure 5.…”

Section: Daily Routine Activity Recognitionmentioning

confidence: 99%

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

Shah

Fioranelli

2019

IEEE Aerosp. Electron. Syst. Mag.

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131

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“…Existing radar systems can be used to monitor activities [12,[16][17][18][19][20], but it could create a paradigm shift in health monitoring moving from reactive technologies to preventive. If they are made smart enough to learn the daily activity pattern of an end user, and identify deviations/anomalies linked to declining health, they could foresee the occurrence of possible critical events (e.g.…”

Section: Existing Sensing Modalitiesmentioning

confidence: 99%

Radar for assisted living in the context of Internet of Things for Health and beyond

Kernec¹,

Fioranelli²,

Yang³

et al. 2018

EasyChair Preprints

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This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare.

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Gait classification based on micro-Doppler features

Cited by 12 publications

References 13 publications

Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference

Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

Radar for assisted living in the context of Internet of Things for Health and beyond

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