Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar

In this proposed approach to unobtrusive human activity classification, a two-stage machine learning-based algorithm was applied to backscattered ultrawideband radar signals. First, a preprocessing step was applied for noise and clutter suppression. Then, feature extraction and a combination of time-frequency (TF) and time-range (TR) domains were used to extract the features of human activities. Then, feature analysis was performed to determine robust features relative to this kind of classification and reduce the dimensionality of the feature vector. Subsequently, different recognition algorithms were applied to group activities as fall or non-fall and categorise their types. Finally, a performance study was used to choose the higher accuracy algorithm. The ensemble bagged tree and fine K-nearest neighbour methods showed the best performance. The results show that the two-stage classification was more accurate than the one-stage. Finally, it was observed that the proposed approach using a combination of TR and TF domains with two-stage recognition outperformed reference approaches mentioned in the literature, with average accuracies of 95.8% for eight-activities classification and 96.9% in distinguishing between fall and non-fall activities with efficient computational complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Our proposed approach shows superiority even when a 2D representation has been used. � By 5% more than the achieved accuracy in Du et al [40].…”

Section: Discussionmentioning

confidence: 66%

“…Segmented features of spectrograms were extracted using a convolution operation. The weakness in Du et al [39,40] was the inability to capture the local structure of the micromotion signature. Therefore, the use of multiple-domain and multistage classification improves accuracy for motion recognition.…”

Section: Discussionmentioning

confidence: 99%

Unobtrusive human activity classification based on combined time‐range and time‐frequency domain signatures using ultrawideband radar

Mostafa

Chamaani

2021

IET Signal Processing

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“…The artificial intelligence algorithm based on human-computer interaction is adopted to recognise the actions of basketball players in the game. In combination with the basketball movement rules and the relevant theories of psychology and physiology, analysis and investigation have been conducted for the fake actions of basketball players to obtain qualitative and quantitative results (Du et al, 2020). Chen et al (2018) proposed two deep convolutional neural networks (DCNNs) based on multi-static radar micro-Doppler signals for human recognition and gait classification.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis

Liu

2021

Front. Psychol.

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In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.

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“…For traditional line-of-sight scenario, micro-Doppler features are employed for radar based human motion recognition [28][29][30][31]. In the presence of solid brick wall, low frequency (usually lower than 3 GHz) is usually adopted for ultra wide band (UWB) through-the-wall radar (TWR).…”

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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Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar

Cited by 43 publications

References 21 publications

Unobtrusive human activity classification based on combined time‐range and time‐frequency domain signatures using ultrawideband radar

Unobtrusive human activity classification based on combined time‐range and time‐frequency domain signatures using ultrawideband radar

Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis

Human Motion Serialization Recognition With Through-the-Wall Radar

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