Human Motion Classification Based on Range Information with Deep Convolutional Neural Network

Shao, Yuming; Guo, Sai; Sun, Ling Hui; Chen, Weidong

doi:10.1109/icisce.2017.317

Cited by 30 publications

(25 citation statements)

References 20 publications

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“…When a person is moving, different components of the human body have different relative distances from the radar at time t. Therefore, various time-range maps produced by different activities can be used to recognize the corresponding activities, although they neglect the Doppler information. In [25], Yuming Shao et al have employed the time-range maps with a deep CNN to classify human motions, and a good performance was achieved.…”

Section: Human Target Analysis With Hrrp and Micro-doppler Profilesmentioning

confidence: 99%

Toward Deep Learning-Based Human Target Analysis

He¹,

Li²,

Jing³

2019

UWB Technology and Its Applications

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In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration.

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Section: Human Target Analysis With Hrrp and Micro-doppler Profilesmentioning

confidence: 99%

Toward Deep Learning-Based Human Target Analysis

He¹,

Li²,

Jing³

2019

UWB Technology and Its Applications

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“…A general belief is that deep learning requires "Big Data" to be effective; but small datasets also produce good results [41][42] via data augmentation and transfer learning. Figure 1 summarizes the research on activity classification using deep learning for enhanced accuracy [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] yielding precisions from 80 to almost 100%. It is hard to assess the different performances since all the deep learning algorithms are ad hoc and the size and the nature of datasets vary.…”

Section: Machine Learning Perspectivementioning

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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“…Deep learning realizes the integration of feature extraction and recognition; it can directly recognise the micro-Doppler or range spectrum of human actions. Using the high-resolution range image of gait, a deep convolutional neural network proposed in Shao et al [7] uses the data of seven types of human motion collected with UWB radar, obtaining a recognition rate of up to 95.24%. However, we argue that a single feature is insufficient for real motion recognition.…”

Section: Introductionmentioning

confidence: 99%

Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar

2021

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Human motion recognition for biological radar has made astonishing progress. However, in some applications with high real-time requirements, it is difficult for existing approaches to achieve high accuracy. A multidimensional features long short-term memory (LSTM) neural network model is presented using multibranch network structure and high-dimensional radar feature fusion, which can recognise motions of human in real time, even in the presence of occlusions. The features selected for motion recognition including slow time range-map and slow time Doppler map. A single feature-based representation is not enough to capture the variations and attributes of individuals (range, velocity, etc.); thus, the fusion of multiple features is significant for recognising motions. Furthermore, because action reflects the behaviour of a human within a period, and the start and end are unavailable, intercepting fixed-length data in the time domain for recognition is not feasible. Thus, we introduce an approach based on an LSTM network that extracts features along the time dimension. Experiments show that the proposed approach is effective. A recognition accuracy of above 93.38% is achieved.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Human Motion Classification Based on Range Information with Deep Convolutional Neural Network

Cited by 30 publications

References 20 publications

Toward Deep Learning-Based Human Target Analysis

Toward Deep Learning-Based Human Target Analysis

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

Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar

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