Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Zhao, Yixin; Zhou, Haiyang; Lu, Sichao; Liu, Yanzhong; An, Xiang; Liu, Qiang

doi:10.3390/app12147124

Cited by 11 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…11. Note that the classification accuracy of our simulationbased HAR system is similar to today's real or experimentalbased HAR systems [21], [53], [54]. However, the proposed simulation-based approach is quite unique in that it effortlessly generates a large amount of high-quality simulation data for training purposes.…”

Section: Testing Of the Simulation-based Har System Employing Modelmentioning

confidence: 68%

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Waqar,

Pätzold

2024

IEEE Internet Things J.

View full text Add to dashboard Cite

Modern human activity recognition (HAR) systems are designed using large amounts of experimental data. So far, real-data-driven or experimental-based HAR systems using Wi-Fi or radar systems have shown considerable results. However, the acquisition of large, clean, and labeled training datasets remains a crucial impediment to the progress of experimentalbased HAR systems. Therefore, in this paper, a paradigm shift from the experimental to a fully simulation-based design of HAR systems is proposed in the context of radar sensing. An endto-end simulation framework is proposed as a proof-of-concept that can simulate realistic millimeter-wave radar signatures for synthesized human motion. We designed a human motion synthesis tool that emulates different types of human activities and generates the spatial trajectories accordingly. These trajectories are processed by a geometric model with respect to user-defined antenna configurations. Considering the long-and short-time stationarity of wireless channels, we synthesize the raw in-phase and quadrature data and process the data to simulate the radar signatures for emulated human activities. Finally, a simulated and a real HAR dataset were used to train and test a simulation-based HAR system, respectively, which gave an average (maximum) classification accuracy of 94% (98.4%). The main advantage of the proposed simulation framework is that the training effort for radar-based classifiers, e.g., gesture recognition systems, can be minimized drastically.

show abstract

Section: Testing Of the Simulation-based Har System Employing Modelmentioning

confidence: 68%

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Waqar,

Pätzold

2024

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…A study on a carpet system displayed its ability to use surface force information for 3D human pose analysis but revealed limitations in detecting certain body positions and differentiating similar movements [ 39 ]. Recently, radar-based HAR has gained interest due to its ease of deployment in diverse environments, insensitivity to ambient lighting conditions, and maintaining user privacy [ 18 , 40 ].…”

Section: Human Activity Recognition Approaches and Related Workmentioning

confidence: 99%

Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine

Alhazmi,

Alanazi,

Alshehry

et al. 2024

Sensors

View full text Add to dashboard Cite

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients’ privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.

show abstract

“…6) Fusion of radar and camera shows better precision in comparison to radar and camera alone [119]. 7) Efficient algorithms can decrease need of having big dataset: Using FMCW radar, Zhao et al [103] reported that the efficient algorithms can decrease the required data samples for training purpose. With an improved PCA, dimensional reduction is performed and a modified version of VGG net is used on the dataset provided by [81].…”

Section: B Human Activity Recognitionmentioning

confidence: 99%

“…11) The performance of shallow learning models (SVM in particular), degrades when training and test is performed in separate environment whereas DL model shows consistent performance [194]. 12) Using FMCW radar, Zhao et al [103] reported that efficient algorithms can decrease the required data samples for training purposes. 13) It has also been observed that efficient features extraction scheme and classifier can reduce the need of having big dataset [103].…”

Section: B Human Activity Recognitionmentioning

confidence: 99%

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

Ahmed,

Cho

2024

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

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.

show abstract

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Cited by 11 publications

References 37 publications

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine

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

Contact Info

Product

Resources

About