A Radio Frequency Sensor System for Prognostics and Health Management

Cheng, Shihua; Thomas, Larry; Cook, Jason L.; Pecht, Michael

doi:10.1115/detc2009-87723

Cited by 1 publication

(1 citation statement)

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“…Because this sort of sensor must be worn continually, it might be unpleasant and not always viable [8]. The second block contains devices with pressure, acoustic, infrared, and radio-frequency sensors that are positioned around the monitored individual [9], [10]. However, the expense of installing these systems is very costly, and they are only appropriate for specialist patient care rooms, making them difficult to employ in everyday living at home.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Patient Activity Recognition using LSTM Network and High-Fidelity Body Pose Tracking

Doan¹

2022

IJACSA

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The need for healthcare services is growing, particularly in light of the COVID-19 epidemic's convoluted trajectory. This causes overcrowding in medical facilities, making it difficult to manage, treat, and monitor patients' health. Therefore, a method to remotely observe the patient's behavior is required, to aid in early warning and treatment, and to reduce the need for hospitalization for patients with minor diseases. This paper proposes a new real-time smart camera system to monitor, recognize and warn the patient's abnormal actions remotely with reasonable cost and easy to deploy in practice. The key benefit of the proposed methods is that patient actions may be detected without the usage of ambient sensors by employing pictures from a regular video camera. It carries out the detection using highfidelity human body pose tracking with MediaPipe Pose. Then, the Raspberry Pi 4 device and the LSTM network are used for remote monitoring and real-time classification of patient actions. The test dataset is built from reality and reuses the existing datasets. Our system has been evaluated and tested in practice with over 96.84% accuracy, runs at over 30 frames per second, suitable for real-time execution on mobile devices with limited hardware configuration.

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Section: Introductionmentioning

confidence: 99%

An Efficient Patient Activity Recognition using LSTM Network and High-Fidelity Body Pose Tracking

Doan¹

2022

IJACSA

View full text Add to dashboard Cite

show abstract

A Radio Frequency Sensor System for Prognostics and Health Management

Cited by 1 publication

References 2 publications

An Efficient Patient Activity Recognition using LSTM Network and High-Fidelity Body Pose Tracking

An Efficient Patient Activity Recognition using LSTM Network and High-Fidelity Body Pose Tracking

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