Remote Physical Therapy: Requirements for a Single RGB Camera Motion Sensing

Adolf, Jindřich; Doležal, Jaromír; Macaš, Martin; Lhotská, Lenka

doi:10.23919/ae51540.2021.9542912

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…In our previous work we have demonstrated where to use a more efficient solution without the need for a GPU to process the videos in real-time [22]. In our next study [23], we proved that OpenPose is robust in terms of quality, video resolution, and lighting changes. Thus, its use is in principle not limited by the environment where the measurement is performed.…”

Section: Introductionmentioning

confidence: 66%

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Evaluation of functional tests performance using a camera-based and machine learning approach

Adolf,

Segal,

Turna

et al. 2023

PLoS ONE

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The objective of this study is to evaluate the performance of functional tests using a camera-based system and machine learning techniques. Specifically, we investigate whether OpenPose and any standard camera can be used to assess the quality of the Single Leg Squat Test and Step Down Test functional tests. We recorded these exercises performed by forty-six healthy subjects, extract motion data, and classify them to expert assessments by three independent physiotherapists using 15 binary parameters. We calculated ranges of movement in Keypoint-pair orientations, joint angles, and relative distances of the monitored segments and used machine learning algorithms to predict the physiotherapists’ assessments. Our results show that the AdaBoost classifier achieved a specificity of 0.8, a sensitivity of 0.68, and an accuracy of 0.7. Our findings suggest that a camera-based system combined with machine learning algorithms can be a simple and inexpensive tool to assess the performance quality of functional tests.

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Section: Introductionmentioning

confidence: 66%

“…In their study, the 2D analysis shows significant bias for the peak values of the ranges of motion. This study records motion in the sagittal plane, which performs worse in detection compared to the frontal plane [23]. In our study, we measure only the frontal plane.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of functional tests performance using a camera-based and machine learning approach

Adolf,

Segal,

Turna

et al. 2023

PLoS ONE

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“…In the modern age, it is now possible to utilize a camera video stream to collect, analyze, and interpret human emotions in a remotely located 3D environment by using artificial neural networks [ 5 ]. Our objective is to characterize human motion using neural network architectures such as auto-encoder [ 6 ] and Siamese twin [ 7 ], in conjunction with human pose estimation (HPE) techniques such as real-time multi-person key point detection algorithms such as OpenPose [ 8 ] and MediaPipe [ 9 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins’ Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images

Segal

Hadar

Lhotská

2023

JPM

Self Cite

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In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers.

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“…For example, The COVID-19 pandemic demonstrated the importance of remote diagnosis and treatment. In the modern age, it is now possible to utilize a camera video stream to collect, analyze, and interpret human emotions in a remotely located 3D environment by using artificial neural networks (3). Vocabulary Lexicon's purpose with this Research work is to present an indexing system for our predefine body movements that will enable us to recognize and describe physical actions using machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator

Hadar

Lhotska

2022

Studies in Health Technology and Informatics

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This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.

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Remote Physical Therapy: Requirements for a Single RGB Camera Motion Sensing

Cited by 3 publications

References 10 publications

Evaluation of functional tests performance using a camera-based and machine learning approach

Evaluation of functional tests performance using a camera-based and machine learning approach

Using EfficientNet-B7 (CNN), Variational Auto Encoder (VAE) and Siamese Twins’ Networks to Evaluate Human Exercises as Super Objects in a TSSCI Images

Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator

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