Privacy-aware Remote Monitoring System by Skeleton Recognition

Nitta, Yoshihisa; Murayama, Yuko

doi:10.24251/hicss.2019.072

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…the movement of both elbows and knees of patients were detected and tracked using The skeletontracking algorithm [24]. Previously, marker-based systems was widely used for telerehabilitation to detect the movement of patients by placing markers on their bodies [25].…”

Section: Fig 2 Posenet Key-pointmentioning

confidence: 99%

Falling Estimation Based on PoseNet Using Camera with Difference Absolute Standard Deviation Value and Average Amplitude Change on Key-Joint

Siddiq

Rizal

Erfianto

et al. 2023

AMM

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Elderly people need special attention and some of them need to be monitored continuously and in real-time. Fall detection is one of the systems used to monitor the daily life of the elderly. Various devices and methods were developed to monitor the condition of the elderly on daily activity. The system that has been proposed in previous studies uses a number of sensors that are placed on the body. However, this system tends to be high cost, complex installation, and inconvenient to use. Therefore, an alternative system is needed to overcome this problem. The purpose of this study is to developed a fall detection method using PoseNet with pose calculations based on key joins. Testing on larger data sets is needed to verify the proposed method's performance further. The use of cameras can be a solution to monitoring the activities of the elderly. With the image processing method, it is possible to estimate the activities of the elderly. The purpose of this study is to developed a fall detection method using PoseNet with pose calculations based on key joins. This study developed a fall detection method using PoseNet with pose calculations based on key joins. The key-join used is Left and Right Shoulder and is only measured at the y-coordinate. We calculated the difference absolute standard deviation value (DASDV) and average amplitude change (AAC) on the Y-coordinate. From 10 falling and non-falling conditions trials, we obtained 85% and 80% accuracy for AAC and DASDV. The result of this research can be used as a source or comparison for future research.

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Section: Fig 2 Posenet Key-pointmentioning

confidence: 99%

Falling Estimation Based on PoseNet Using Camera with Difference Absolute Standard Deviation Value and Average Amplitude Change on Key-Joint

Siddiq

Rizal

Erfianto

et al. 2023

AMM

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Telehealth Using PoseNet-Based System for In-Home Rehabilitation

2021

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The increasing cost of healthcare services is accelerating the development of the telehealth system to fulfill the necessity of delivering an efficient and cost-effective remote healthcare services. Moreover, the ageing of the global population and the disruption of the COVID-19 pandemic are creating a rapid rise of demand for healthcare services. This includes those who are in need of remote monitoring for chronic conditions through rehabilitation exercises. Therefore, this paper presents a telehealth system using PoseNet for in-home rehabilitation, with built-in statistical computation for doctors to analyze the patient’s recovery status. This system enables patients to perform rehabilitation exercises at home using an ordinary webcam. The PoseNet skeleton-tracking method is applied to detect and track the patients’ angular movements for both elbows and knees. By using this system, the measurement of the elbow and knee joint angles can be calculated and recorded while patients are performing rehabilitation exercises in front of the laptop webcam. After the patients complete their rehabilitation exercises, the skeleton results of four body parts will be generated. Based on the same actions performed by patients on selected days, the doctors can examine and evaluate the deviation rate of patients’ angular movements between different days to determine the recovery rate.

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IoT based Pose detection of patients in Rehabilitation Centre by PoseNet Estimation Control

Asokan¹,

Vijayakumar²

2022

JIIP

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Recently, Virtual rehabilitation has recently emerged as a contemporary option to treating chronic, handicapped, or mobility-impaired patients using virtual reality, augmented reality, and motion capture technology. Using a virtual environment, patients are able to work out in accordance with their treatment plan. This study provides a PoseNet-based in-home rehabilitation telemedicine system with integrated statistical computation allowing clinicians to assess a patient's recovery progress. Using a smartphone camera, patients may undertake rehabilitation activities at home. The angular motions of the patients' elbows and knees are detected and tracked using the PoseNet skeleton-tracking technology. The estimated elbow and other feature poses are recorded during the completion process of rehabilitation activities in front of the mobile camera. Finally, additional performance measurements are gathered and analysed in order to better understand how well the system works.

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Privacy-aware Remote Monitoring System by Skeleton Recognition

Cited by 3 publications

References 7 publications

Falling Estimation Based on PoseNet Using Camera with Difference Absolute Standard Deviation Value and Average Amplitude Change on Key-Joint

Falling Estimation Based on PoseNet Using Camera with Difference Absolute Standard Deviation Value and Average Amplitude Change on Key-Joint

Telehealth Using PoseNet-Based System for In-Home Rehabilitation

IoT based Pose detection of patients in Rehabilitation Centre by PoseNet Estimation Control

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