Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods
In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/7/1/10 |
| _version_ | 1797298990219264000 |
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| author | Jih-Ching Chiu Guan-Yi Lee Chih-Yang Hsieh Qing-You Lin |
| author_facet | Jih-Ching Chiu Guan-Yi Lee Chih-Yang Hsieh Qing-You Lin |
| author_sort | Jih-Ching Chiu |
| collection | DOAJ |
| description | In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model’s speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQTT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents. |
| first_indexed | 2024-03-07T22:43:03Z |
| format | Article |
| id | doaj.art-e2039f1328d64e9daca820844567606d |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-07T22:43:03Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-e2039f1328d64e9daca820844567606d2024-02-23T15:06:57ZengMDPI AGApplied System Innovation2571-55772024-01-01711010.3390/asi7010010Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing MethodsJih-Ching Chiu0Guan-Yi Lee1Chih-Yang Hsieh2Qing-You Lin3Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, TaiwanDepartment of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, TaiwanDepartment of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, TaiwanDepartment of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, TaiwanIn computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model’s speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQTT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents.https://www.mdpi.com/2571-5577/7/1/10mmWave radarintegrated sensing and communicationconvolutional neural networkartificial intelligence of thingsgesture recognition |
| spellingShingle | Jih-Ching Chiu Guan-Yi Lee Chih-Yang Hsieh Qing-You Lin Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods Applied System Innovation mmWave radar integrated sensing and communication convolutional neural network artificial intelligence of things gesture recognition |
| title | Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods |
| title_full | Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods |
| title_fullStr | Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods |
| title_full_unstemmed | Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods |
| title_short | Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods |
| title_sort | design and implementation of nursing secure care system with mmwave radar by yolo v4 computing methods |
| topic | mmWave radar integrated sensing and communication convolutional neural network artificial intelligence of things gesture recognition |
| url | https://www.mdpi.com/2571-5577/7/1/10 |
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