Human Pose Detection for Robotic-Assisted and Rehabilitation Environments

Hernández, Óscar G.; Morell, Vicente; Ramón, José Luis García; Jara, Carlos A.

doi:10.3390/app11094183

Cited by 28 publications

(13 citation statements)

References 29 publications

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“…Studies that evaluate the accuracy of motion detection typically study only one specific type of exercise motion. Hernández [17] concludes that OpenPose is an adequate library for evaluating patient performance in rehabilitation programs that involve the following exercises: left elbow side flexion, shoulder abduction, and shoulder extension based on comparison with Kinect. Ota [8] verifies the reliability and validity of OpenPose-based motion analysis during the bilateral squat based on comparison with Vicon MoCap system.…”

Section: Related Workmentioning

confidence: 99%

Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset

et al. 2022

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In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerehabilitation.

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Section: Related Workmentioning

confidence: 99%

Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset

et al. 2022

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“…Attempts to incorporate this human pose estimation technology into the medical field continues [ 31 , 32 , 33 ]. Usually, since it is a technology that mainly checks movement, research is mainly conducted to track joints that do not work properly and check movements such as joint movement range.…”

Section: Related Workmentioning

confidence: 99%

Design of a Diagnostic System for Patient Recovery Based on Deep Learning Image Processing: For the Prevention of Bedsores and Leg Rehabilitation

Choi

Jang

2022

Diagnostics

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Worldwide COVID-19 infections have caused various problems throughout different countries. In the case of Korea, problems related to the demand for medical care concerning wards and doctors are serious, which were already slowly worsening problems in Korea before the COVID-19 pandemic. In this paper, we propose the direction of developing a system by combining artificial intelligence technology with limited areas that do not require high expertise in the rehabilitation medical field that should be improved in Korea through the prevention of bedsores and leg rehabilitation methods. Regarding the introduction of artificial intelligence technology, medical and related laws and regulations were quite limited, so the actual needs of domestic rehabilitation doctors and advice on the hospital environment were obtained. Satisfaction with the test content was high, the degree of provision of important medical data was 95%, and the angular error was within 5 degrees and suitable for recovery confirmation.

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“…In this field, evaluation of low-cost markerless human pose estimators has received much attention for their diversity of applications, especially in rehabilitation robotic environments. In [3], Hernández et al present an evaluation of the angles in the elbow and shoulder joints, estimated by OpenPose and Detectron 2, during four typical upper-limb rehabilitation exercises: elbow side flexion, elbow flexion, shoulder extension, and shoulder abduction. A low-cost setup of two Kinect 2 RGB-D cameras was used to obtain the ground truth of the joint and skeleton estimations during the different exercises.…”

Section: Contributionsmentioning

confidence: 99%