An Intelligent Motor Assessment Method Utilizing a Bi-Lateral Virtual-Reality Task for Stroke Rehabilitation on Upper Extremity

Chung, Chia-Ru; Su, Mu-Chun; Lee, Si-Huei; Wu, Eric Hsiao-Kuang; Tang, Li-Hsien; Yeh, Shih-Ching

doi:10.1109/jtehm.2022.3213348

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…Regarding evaluation methods, the researchers suggest utilizing machine learning techniques for data processing ( Chung et al, 2022 ). Eichler et al (2018) developed a multi-camera tracking system with SVM and Random Forest to evaluate the motion of stroke patients.…”

Section: Introductionmentioning

confidence: 99%

Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force

Bai,

Li,

et al. 2024

Front. Neurosci.

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The clinical rehabilitation assessment methods for hemiplegic upper limb motor function are often subjective, time-consuming, and non-uniform. This study proposes an automatic rehabilitation assessment method for upper limb motor function based on posture and distributed force measurements. Azure Kinect combined with MediaPipe was used to detect upper limb and hand movements, and the array distributed flexible thin film pressure sensor was employed to measure the distributed force of hand. This allowed for the automated measurement of 30 items within the Fugl-Meyer scale. Feature information was extracted separately from the affected and healthy sides, the feature ratios or deviation were then fed into a single/multiple fuzzy logic assessment model to determine the assessment score of each item. Finally, the total score of the hemiplegic upper limb motor function assessment was derived. Experiments were performed to evaluate the motor function of the subjects’ upper extremities. Bland-Altman plots of physician and system scores showed good agreement. The results of the automated assessment system were highly correlated with the clinical Fugl-Meyer total score (r = 0.99, p < 0.001). The experimental results state that this system can automatically assess the motor function of the affected upper limb by measuring the posture and force distribution.

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Section: Introductionmentioning

confidence: 99%

Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force

Bai,

Li,

et al. 2024

Front. Neurosci.

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The cognitive basis for virtual reality rehabilitation of upper-extremity motor function after neurotraumas

Dewil,

Kuptchik,

Liu

et al. 2023

J Multimodal User Interfaces

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This paper aims to present previous works in augmented sensory guidance for motor learning and psychophysiological factors and contextualize how these approaches may facilitate greater optimization of motor rehabilitation after neurotraumas with virtual reality. Through library resources at Stevens Institute of Technology, we searched for related works using multiple electronic databases and search engines with a medical focus (detailed in the paper). Searches were for articles published between 1980 and 2023 examining upper extremity rehabilitation, virtual reality, cognition, and modes and features of sensory feedback (specific search terms detailed in the paper). Strategic activation of sensory modalities for augmented guidance using virtual reality may improve motor training to develop further skill retention in persons suffering from impulsive neurological damage. Features with unique motor learning characteristics to consider with augmented feedback signals include representation, timing, complexity, and intermittency. Furthermore, monitoring psychophysiological factors (e.g., sense of agency, cognitive loading, attention) that represent mental and psychological processes may assist in critically evaluating novel designs in computerized rehabilitation. Virtual reality approaches should better incorporate augmented sensory feedback and leverage psychophysiological factors to advance motor rehabilitation after neurotraumas.

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Extended reality to assess post-stroke manual dexterity: contrasts between the classic box and block test, immersive virtual reality with controllers, with hand-tracking, and mixed-reality tests

Everard,

Burton,

Van de Sype

et al. 2024

J NeuroEngineering Rehabil

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Background Recent technological advancements present promising opportunities to enhance the frequency and objectivity of functional assessments, aligning with recent stroke rehabilitation guidelines. Within this framework, we designed and adapted different manual dexterity tests in extended reality (XR), using immersive virtual reality (VR) with controllers (BBT-VR-C), immersive VR with hand-tracking (BBT-VR-HT), and mixed-reality (MD-MR). Objective This study primarily aimed to assess and compare the validity of the BBT-VR-C, BBT-VR-HT and MD-MR to assess post-stroke manual dexterity. Secondary objectives were to evaluate reliability, usability and to define arm kinematics measures. Methods A sample of 21 healthy control participants (HCP) and 21 stroke individuals with hemiparesis (IHP) completed three trials of the traditional BBT, the BBT-VR-C, BBT-VR-HT and MD-MR. Content validity of the different tests were evaluated by asking five healthcare professionals to rate the difficulty of performing each test in comparison to the traditional BBT. Convergent validity was evaluated through correlations between the scores of the traditional BBT and the XR tests. Test-retest reliability was assessed through correlations between the second and third trial and usability was assessed using the System Usability Scale (SUS). Lastly, upper limb movement smoothness (SPARC) was compared between IHP and HCP for both BBT-VR test versions. Results For content validity, healthcare professionals rated the BBT-VR-HT (0[0–1]) and BBT-MR (0[0–1]) as equally difficult to the traditional BBT, whereas they rated BBT-VR-C as more difficult than the traditional BBT (1[0–2]). For IHP convergent validity, the Pearson tests demonstrated larger correlations between the scores of BBT and BBT-VR-HT (r = 0.94;p < 0.001), and BBT and MD-MR (r = 0.95;p < 0.001) than BBT and BBT-VR-C (r = 0.65;p = 0.001). BBT-VR-HT and MD-MR usability were both rated as excellent, with median SUS scores of 83[57.5–91.3] and 83[53.8–92.5] respectively. Excellent reliability was found for the BBT-VR-C (ICC = 0.96;p < 0.001), BBT-VR-HT (ICC = 0.96;p < 0.001) and BBT-MR (ICC = 0.99;p < 0.001). The usability of the BBT-VR-C was rated as good with a median SUS of 70[43.8–83.8]. Upper limb movements of HCP were significantly smoother than for IHP when completing either the BBT-VR-C (t = 2.05;p = 0.043) and the BBT-VR-HT (t = 5.21;p < 0.001). Conclusion The different XR manual tests are valid, short-term reliable and usable tools to assess post-stroke manual dexterity. Trial registration https://clinicaltrials.gov/ct2/show/NCT04694833; Unique identifier: NCT04694833, Date of registration: 11/24/2020.

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An Intelligent Motor Assessment Method Utilizing a Bi-Lateral Virtual-Reality Task for Stroke Rehabilitation on Upper Extremity

Abstract: This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Institutional Review Board (IRB) of Taipei Veterans General Hospital under Application No. VGH2020-10-006C.

Cited by 5 publications

References 46 publications

Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force

Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force

The cognitive basis for virtual reality rehabilitation of upper-extremity motor function after neurotraumas

Extended reality to assess post-stroke manual dexterity: contrasts between the classic box and block test, immersive virtual reality with controllers, with hand-tracking, and mixed-reality tests

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