Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural Networks

Le, Hoang Thanh; Phung, Son Lam; Bouzerdoum, A.; Tivive, Fok Hing Chi

doi:10.1109/icassp.2018.8461847

Cited by 27 publications

(13 citation statements)

References 12 publications

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“…Over the past decade, much work has been done in human motion classifications which include daily activities of walking, kneeling, sitting, standing, bending, falling, etc. [6][7][8][9][10][11][12][13][14][15][16][17][18]. Distinguishing among the different motions is viewed as an inter-class classification [6][7][8][9][10][11][12], whereas the intra-class classification amounts to identifying the different members of the same class, e.g., classifying normal and abnormal gaits [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8][9][10][11][12][13][14][15][16][17][18]. Distinguishing among the different motions is viewed as an inter-class classification [6][7][8][9][10][11][12], whereas the intra-class classification amounts to identifying the different members of the same class, e.g., classifying normal and abnormal gaits [13][14][15][16][17][18]. There are two main approaches of human motion classifications, namely those relying on handcrafted features that relate to human motion kinematics [7,8,[13][14][15], and others which are data driven and include low-dimension representations [6,16], frequency-warped cepstral analysis [12], and neural networks [9-11, 17, 18].…”

Section: Introductionmentioning

confidence: 99%

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Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Amin

Zeng

Shan

2019

2019 IEEE Radar Conference (RadarConf)

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We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler (MD) signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies that are generated during each gesture act. We detect the positive and negative frequency envelopes of MD separately, and form a feature vector of their augmentation. We use the k-nearest neighbor (kNN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Amin

Zeng

Shan

2019

2019 IEEE Radar Conference (RadarConf)

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“…Seyfioglu et al [9] proposed a deep convolutional autoencoder architecture for similar aided and unaided human activities with MD signatures. Le et al [24] developed a Bayesian-optimized CNN model for human motion classification with a Doppler radar.…”

Section: Activity Recognitionmentioning

confidence: 99%

A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

Jing

2019

Remote Sensing

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Short-range radar has become one of the latest sensor technologies for the Internet of Things (IoT), and it plays an increasingly vital role in IoT applications. As the essential task for various smart-sensing applications, radar-based human activity recognition and person identification have received more attention due to radar’s robustness to the environment and low power consumption. Activity recognition and person identification are generally treated as separate problems. However, designing different networks for these two tasks brings a high computational complexity and wastes of resources to some extent. Furthermore, there are some correlations in activity recognition and person identification tasks. In this work, we propose a multiscale residual attention network (MRA-Net) for joint activity recognition and person identification with radar micro-Doppler signatures. A fine-grained loss weight learning (FLWL) mechanism is presented for elaborating a multitask loss to optimize MRA-Net. In addition, we construct a new radar micro-Doppler dataset with dual labels of activity and identity. With the proposed model trained on this dataset, we demonstrate that our method achieves the state-of-the-art performance in both radar-based activity recognition and person identification tasks. The impact of the FLWL mechanism was further investigated, and ablation studies of the efficacy of each component in MRA-Net were also conducted.

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“…To avoid such drawbacks, deep learning methods were adopted to extract the appropriate motion characters automatically [15][16][17][18][19][20][21]. Convolutional neural network (CNN) is one of the most utilized deep learning structures to improve the classification accuracy for multiple human motion types [22].…”

Section: Introductionmentioning

confidence: 99%

Human Motion Serialization Recognition With Through-the-Wall Radar

Yang

Chen

Wang³

et al. 2020

IEEE Access

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Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this paper addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition. INDEX TERMS Human motion recognition, ultra wide band through-the-wall radar, auto-encoder network, gated recurrent unit.

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Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural Networks

Cited by 27 publications

References 12 publications

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

Human Motion Serialization Recognition With Through-the-Wall Radar

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