Application of DNN for radar micro-doppler signature-based human suspicious activity recognition

Chakraborty, Mainak; Kumawat, Harish Chandra; Dhavale, Sunita Vikrant; A, Arockia Bazil Raj

doi:10.1016/j.patrec.2022.08.005

Cited by 26 publications

(5 citation statements)

References 14 publications

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“…Precision (%) Recall (%) F1-Score (%) Accuracy (%) DCNN [10] 83.0 83.0 83.0 83.0 LSTM [10] 96.9 93.3 93.1 93.0 CNN [11] 89.7 88.0 87.9 88.0 VGG16 [12] 96.8 96.7 96.7 97.0 1D-CNN+LSTM [13] 98…”

Section: Methodsmentioning

confidence: 99%

“…The CNN model outperformed the others, achieving a superior performance rate of 88%. Chakraborty et al [ 12 ] utilized an X-band CW radar to compile the diverse DIAT-RadHAR dataset that captures activities such as army marching, stone pelting/grenade throwing, jumping while holding a gun, army jogging, army crawling, and boxing. This research also incorporated six pre-trained CNN models to support the deep learning (DL) architectures in analyzing the data, and the accuracy approached 98%.…”

Section: Introductionmentioning

confidence: 99%

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Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data

Tan,

Tian,

Sharma

et al. 2024

Sensors

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Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user’s quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model’s potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data

Tan,

Tian,

Sharma

et al. 2024

Sensors

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“…In addition to that, recognition of suspicious activities such as boxing, crawling, jogging (in army style), jumping with gun and throwing grenades etc. has also been considered in literature [151]. Another categorization of the activities is the activities performed at the same place and activities which involve leaving the original position [139].…”

Section: B Nature Of Activities Being Classifiedmentioning

confidence: 99%

Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

Ahmed,

Cho

2024

IEEE Commun. Surv. Tutorials

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The unprecedented non-contact, non-invasive, and privacy-preserving nature of radar sensors has enabled various healthcare applications, including vital sign monitoring, fall detection, gait analysis, activity recognition, fitness evaluation, and sleep monitoring. Machine learning (ML) is revolutionizing every domain, with radar-based healthcare being no exception. Progress in the field of healthcare radars and ML is complementing the existing radar-based healthcare industry. This article provides an overview of ML usage for two major healthcare applications: vital sign monitoring and activity recognition. Vital sign monitoring is the most promising healthcare application of radar, as it can predict several chronic cardiac and respiratory diseases. Activity recognition is also a prominent application since the inability to perform activities may result in critical suffering. The article presents an overview of commercial radars, radar hardware, and historical progress of healthcare radars, followed by the usage of ML for healthcare radars. Subsequently, the paper discusses how ML can overcome the limitations of conventional radar data processing chains for healthcare radars. The article also touches upon recent generative ML concepts used in healthcare radars. Among several interesting findings, it was discovered that ML does not completely replace existing vital sign monitoring algorithms; rather, ML is deployed to overcome the limitations of traditional algorithms. On the other hand, activity recognition always relies on ML approaches. The most widely used algorithms for both applications are Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM). Generative AI has the capability to augment data and is expected to have a significant impact soon. Recent trends, lessons learned from these trends, and future directions for both healthcare applications are presented in detail. Finally, the future work section discusses a wide range of healthcare topics for humans, ranging from neonates to elderly individuals.

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“…Chakraborty et al [65] used an open source pretrained DCNN, i.e., MobileNetV2, VGG19, ResNet-50, InceptionV3, DenseNet-201, and VGG16, to train with their own provided dataset (DIAT-µRadHAR) consisting of 3780 micro-Doppler images comprising different coarse-grained military-related activities, e.g., boxing, crawling, jogging, jumping with a gun, marching, and grenade throwing. An overall accuracy of 98% proved the suitability of transfer learning for HAR.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

Papadopoulos,

Jelali

2023

Applied Sciences

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The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability.

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Application of DNN for radar micro-doppler signature-based human suspicious activity recognition

Cited by 26 publications

References 14 publications

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data

Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

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