Human motion classification using Impulse Radio Ultra Wide Band through-wall RADAR model

Pardhu, Thottempudi; Kumar, Vijay

doi:10.1007/s11042-023-14496-w

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…Identifying and classifying human motion through the wall using IR-UWB RADAR is the focus of the current research. Thottempudi et al [41] proposed that this approach will involve a new human motion classification through wall problem formulation as a hybrid optimization algorithm development using training on a Random Multimodal Deep Learning Classifier. This method combines the features of UWB Signals and novel AI Algorithms to achieve better accuracy and lower computational time in classifying human motion through the wall.…”

Section: A Key Features Of the Human Signalmentioning

confidence: 99%

“…This research has diverse applications in smart home development, defense applications, and disaster management [47]. Figure 3 illustrates the real-time experiment conducted by Thottempudi et al [41] to classify human movements through a wall .…”

Section: A Key Features Of the Human Signalmentioning

confidence: 99%

“…Thottempudi Pardhu et al [41] have made efforts in the direction of improving human motion detection by proposing the use of the Spotted Grey Wolf Optimizer (SGWO) as a novel hybrid algorithm that synergistically combines both the Spotted Hyena and the Grey Wolf Optimizer. In this work, the SGWO has been used to improve the performance of the Random Multimodal Deep Learning (RMDL) Classifier in human motion classification, such as walking and standing, with great accuracy, especially in complex background conditions such as presence detection behind walls.…”

Section: Hybrid Optimization Algorithms Based Human Motion Classifica...mentioning

confidence: 99%

“…In contrast, D represents the calculated distance vector between the agent and the prey or target. This distance vector D is usually computed as D = |C • x prey − x old |, where x prey represents the prey position, and again, C is another coefficient matrix acting to define better the encircling behavior the agents adopt [41]. The application of SGWO in enhancing the effectiveness of the RMDL Classifier leverages the intense penetration and high range-resolution capabilities of UWB radar [6], [13], [32], [40].…”

Section: Hybrid Optimization Algorithms Based Human Motion Classifica...mentioning

confidence: 99%

“…UWB radar signal processing expertise of advanced methods such as the fast Fourier transform (FFT) [20], [37] for analyzing signals, and the back projection techniques [35], [37] for reconstructing an image from the radar data. Hence, a fusion of these advanced signal processing techniques into the SGWO-based RMDL Classifier avails accurate classification of the human movement required for various applications in security surveillance systems, search and rescue operations, and the development of intelligent home technologies [41].…”

Section: Hybrid Optimization Algorithms Based Human Motion Classifica...mentioning

confidence: 99%

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Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis

Pardhu,

Kumar,

Kumar

et al. 2024

IEEE Access

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Ultra-wide Band (UWB) technology has emerged as a pivotal tool for human motion detection, finding applications in diverse areas ranging from smart homes to automotive safety. This paper presents a comprehensive survey of methodologies employed in UWB-based motion detection, elucidating their strengths, challenges, and performance metrics. While several methods, including Convolutional Neural Network (CNN) approaches, have been explored, challenges such as motion state overlaps, the necessity for enhanced spatial resolution, and background noise interference persist. Among the various methods analyzed, the SGWO-based RMDL technique emerges as a frontrunner, offering superior accuracy, reduced mean squared error, and impressive true negative and positive rates. Moreover, its computational efficiency sets a precedent in human motion detection. This paper provides insights into the state-of-the-art Through the wall imaging and human vital signs observation for future research and realtime applications.

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Section: A Key Features Of the Human Signalmentioning

confidence: 99%

Section: A Key Features Of the Human Signalmentioning

confidence: 99%

Section: Hybrid Optimization Algorithms Based Human Motion Classifica...mentioning

confidence: 99%

Section: Hybrid Optimization Algorithms Based Human Motion Classifica...mentioning

confidence: 99%

Section: Hybrid Optimization Algorithms Based Human Motion Classifica...mentioning

confidence: 99%

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Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis

Pardhu,

Kumar,

Kumar

et al. 2024

IEEE Access

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Enhanced Classification of Human Fall and Sit Motions Using Ultra-Wideband Radar and Hidden Markov Models

Pardhu,

Kumar,

Kanavos

et al. 2024

Mathematics

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In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems.

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Therapeutic Exercise Recognition Using a Single UWB Radar with AI-Driven Feature Fusion and ML Techniques in a Real Environment

Hussain,

Siddiqui,

Saleem

et al. 2024

Sensors

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Physiotherapy plays a crucial role in the rehabilitation of damaged or defective organs due to injuries or illnesses, often requiring long-term supervision by a physiotherapist in clinical settings or at home. AI-based support systems have been developed to enhance the precision and effectiveness of physiotherapy, particularly during the COVID-19 pandemic. These systems, which include game-based or tele-rehabilitation monitoring using camera-based optical systems like Vicon and Microsoft Kinect, face challenges such as privacy concerns, occlusion, and sensitivity to environmental light. Non-optical sensor alternatives, such as Inertial Movement Units (IMUs), Wi-Fi, ultrasound sensors, and ultrawide band (UWB) radar, have emerged to address these issues. Although IMUs are portable and cost-effective, they suffer from disadvantages like drift over time, limited range, and susceptibility to magnetic interference. In this study, a single UWB radar was utilized to recognize five therapeutic exercises related to the upper limb, performed by 34 male volunteers in a real environment. A novel feature fusion approach was developed to extract distinguishing features for these exercises. Various machine learning methods were applied, with the EnsembleRRGraBoost ensemble method achieving the highest recognition accuracy of 99.45%. The performance of the EnsembleRRGraBoost model was further validated using five-fold cross-validation, maintaining its high accuracy.

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Human motion classification using Impulse Radio Ultra Wide Band through-wall RADAR model

Cited by 4 publications

References 38 publications

Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis

Advancements in UWB-Based Human Motion Detection Through Wall: A Comprehensive Analysis

Enhanced Classification of Human Fall and Sit Motions Using Ultra-Wideband Radar and Hidden Markov Models

Therapeutic Exercise Recognition Using a Single UWB Radar with AI-Driven Feature Fusion and ML Techniques in a Real Environment

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