Vision-based approach for long-term mobility monitoring: Single case study following total hip replacement

Dolatabadi, Elham; Taati, Babak; Mihailidis, Alex

doi:10.1682/jrrd.2013.12.0263

Cited by 21 publications

(12 citation statements)

References 31 publications

(33 reference statements)

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“…The tracked joint positions were expressed in the Kinect-centric coordinate system. Therefore, it was necessary to rotate and translate the coordinates to a participant-centric coordinate system, invariant to sensor placement and orientation [26] , [27] . To do so, from the beginning of each recorded segment, a frame was manually selected at the neutral sitting position as the baseline frame.…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy

Zhi

Lukasik

et al. 2018

IEEE J. Transl. Eng. Health Med.

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Robotic stroke rehabilitation therapy can greatly increase the efficiency of therapy delivery. However, when left unsupervised, users often compensate for limitations in affected muscles and joints by recruiting unaffected muscles and joints, leading to undesirable rehabilitation outcomes. This paper aims to develop a computer vision system that augments robotic stroke rehabilitation therapy by automatically detecting such compensatory motions. Nine stroke survivors and ten healthy adults participated in this study. All participants completed scripted motions using a table-top rehabilitation robot. The healthy participants also simulated three types of compensatory motions. The 3-D trajectories of upper body joint positions tracked over time were used for multiclass classification of postures. A support vector machine (SVM) classifier detected lean-forward compensation from healthy participants with excellent accuracy (AUC = 0.98, F1 = 0.82), followed by trunk-rotation compensation (AUC = 0.77, F1 = 0.57). Shoulder-elevation compensation was not well detected (AUC = 0.66, F1 = 0.07). A recurrent neural network (RNN) classifier, which encodes the temporal dependency of video frames, obtained similar results. In contrast, F1-scores in stroke survivors were low for all three compensations while using RNN: lean-forward compensation (AUC = 0.77, F1 = 0.17), trunk-rotation compensation (AUC = 0.81, F1 = 0.27), and shoulder-elevation compensation (AUC = 0.27, F1 = 0.07). The result was similar while using SVM. To improve detection accuracy for stroke survivors, future work should focus on predefining the range of motion, direct camera placement, delivering exercise intensity tantamount to that of real stroke therapies, adjusting seat height, and recording full therapy sessions.

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Section: Methodsmentioning

confidence: 99%

Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy

Zhi

Lukasik

et al. 2018

IEEE J. Transl. Eng. Health Med.

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“…Other studies have used specialized depth sensors such as the Microsoft Kinect v2 sensor and its skeleton tracking capabilities to measure spatiotemporal and kinematic variables of gait [33][34][35]. For example, one study explored the use of Kinect for ongoing mobility monitoring of older adults after hip replacement surgery and confirmed the sensitivity of measured gait parameters during the course of recovery [36].…”

Section: Human Pose Estimation and Gait Assessmentmentioning

confidence: 99%

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia

Mehdizadeh

Iaboni

et al. 2020

IEEE J. Transl. Eng. Health Med.

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Fall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait assessments based on gait data that is collected frequently and unobtrusively, and analysed using computer vision methods. Recent developments in computer vision have led to the availability of open source human pose estimation algorithms, which automatically estimate the joint locations of a person in an image. In this study, a preexisting pose estimation model was applied to 1066 walking videos collected of 31 older adults with dementia as they walked naturally in a corridor on a specialized dementia unit over a two week period. Using the tracked pose information, gait features were extracted from video recordings of gait bouts and their association with clinical mobility assessment scores and future falls data was examined. A significant association was found between extracted gait features and a clinical mobility assessment and the number of future falls, providing concurrent and predictive validation of this approach.

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“…One single case study aimed to monitor changes in gait before and after total hip replacement surgery ( Dolatabadi, Taati & Mihailidis, 2014 ) with the intention of understanding balance issues after surgery. Outcome measures assessing balance after surgery are typically in the form of self-report which does not always align with clinical assessment ( Gandhi et al, 2009 ).…”

Section: Resultsmentioning

confidence: 99%

Healthcare applications of single camera markerless motion capture: a scoping review

Scott

Seyres

Philp

et al. 2022

PeerJ

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Background Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Survey Methodology Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. Results A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Conclusions Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.

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Vision-based approach for long-term mobility monitoring: Single case study following total hip replacement

Cited by 21 publications

References 31 publications

Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy

Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy

Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia

Healthcare applications of single camera markerless motion capture: a scoping review

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