Home-Based Physical Therapy with an Interactive Computer Vision System

Gu, Yiwen; Pandit, Shreya; Saraee, Elham; Nordahl, Timothy; Ellis, Terry; Betke, Margrit

doi:10.1109/iccvw.2019.00320

Cited by 23 publications

(29 citation statements)

References 46 publications

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“…Lu et al [286] designed a pose-based estimation system for assessing Parkinson's disease motor severity. Gu et al [287] developed a pose-based physical therapy system that patients can be evaluated and advised at home. Furthermore, such a system can be established to detect abnormal action and to predict the following actions ahead of time.…”

Section: Applicationsmentioning

confidence: 99%

Deep Learning-Based Human Pose Estimation: A Survey

Zheng¹,

Wu²,

Yang³

et al. 2020

Preprint

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Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion. The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 240 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. We also provide a regularly updated project page: https://github.com/zczcwh/DL-HPE

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Section: Applicationsmentioning

confidence: 99%

Deep Learning-Based Human Pose Estimation: A Survey

Zheng¹,

Wu²,

Yang³

et al. 2020

Preprint

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“…Some studies confirmed a good reliability of devices such as Kinect and Kinect V2 in recording the kinematics of the upper limb when compared to marker-based systems [15,16]. Applications to the field of rehabilitation and health of low-cost RGB-D systems such as Kinect include a variety of studies, including biomechanical assessments [17][18][19][20][21][22][23][24][25][26][27], translation of quantitative clinical scales [28][29][30], post-stroke patients' assessments [31][32][33][34], validation for their usage in home environments [35][36][37], and assisted living [38].…”

Section: Introductionmentioning

confidence: 97%

Analysis of Upper-Limb and Trunk Kinematic Variability: Accuracy and Reliability of an RGB-D Sensor

Scano¹,

Mira²,

Cerveri³

et al. 2020

MTI

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In the field of motion analysis, the gold standard devices are marker-based tracking systems. Despite being very accurate, their cost, stringent working environments, and long preparation time make them unsuitable for small clinics as well as for other scenarios such as industrial application. Since human-centered approaches have been promoted even outside clinical environments, the need for easy-to-use solutions to track human motion is topical. In this context, cost-effective devices, such as RGB-Depth (RBG-D) cameras have been proposed, aiming at a user-centered evaluation in rehabilitation or of workers in industry environment. In this paper, we aimed at comparing marker-based systems and RGB-D cameras for tracking human motion. We used a Vicon system (Vicon Motion Systems, Oxford, UK) as a gold standard for the analysis of accuracy and reliability of the Kinect V2 (Microsoft, Redmond, WA, USA) in a variety of gestures in the upper limb workspace—targeting rehabilitation and working applications. The comparison was performed on a group of 15 adult healthy subjects. Each subject had to perform two types of upper-limb movements (point-to-point and exploration) in three workspace sectors (central, right, and left) that might be explored in rehabilitation and industrial working scenarios. The protocol was conceived to test a wide range of the field of view of the RGB-D device. Our results, detailed in the paper, suggest that RGB-D sensors are adequate to track the upper limb for biomechanical assessments, even though relevant limitations can be found in the assessment and reliability of some specific degrees of freedom and gestures with respect to marker-based systems.

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“…Accurate tracking of human anatomy is important in several areas, including activity recognition in gaming 1 , gesture identification in consumer electronics 2 , behavioural analysis in medical monitoring 3,4 , as well as form and functional analysis in professional sports 5 . Three-dimensional pose estimation from depth images or depth videos has been performed across many different domains: fall detection of elderly [6][7][8] , medical diagnosis 9 , assistance in physical therapy 10,11 , monitoring of patient sleep 12 , sport coaching 13 , interaction with robots 14 , and general action recognition [15][16][17] . As the application areas for pose estimation span a wide range, so too does the technology used for it.…”

Section: Introductionmentioning

confidence: 99%

Real-time, low-cost multi-person 3D pose estimation

Ruget,

Tyler,

Martín

et al. 2021

Preprint

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The process of tracking human anatomy in computer vision is referred to pose estimation, and it is used in fields ranging from gaming to surveillance. Three-dimensional pose estimation traditionally requires advanced equipment, such as multiple linked intensity cameras or high-resolution time-of-flight cameras to produce depth images. However, there are applications, e.g. consumer electronics, where significant constraints are placed on the size, power consumption, weight and cost of the usable technology. Here, we demonstrate that computational imaging methods can achieve accurate pose estimation and overcome the apparent limitations of time-of-flight sensors designed for much simpler tasks. The sensor we use is already widely integrated in consumer-grade mobile devices, and despite its low spatial resolution, only 4×4 pixels, our proposed Pixels2Pose system transforms its data into accurate depth maps and 3D pose data of multiple people up to a distance of 3 m from the sensor. We are able to generate depth maps at a resolution of 32×32 and 3D localization of a body parts with an error of only ≈10 cm at a frame rate of 7 fps. This work opens up promising real-life applications in scenarios that were previously restricted by the advanced hardware requirements and cost of time-of-flight technology.

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Home-Based Physical Therapy with an Interactive Computer Vision System

Cited by 23 publications

References 46 publications

Deep Learning-Based Human Pose Estimation: A Survey

Deep Learning-Based Human Pose Estimation: A Survey

Analysis of Upper-Limb and Trunk Kinematic Variability: Accuracy and Reliability of an RGB-D Sensor

Real-time, low-cost multi-person 3D pose estimation

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