Quantitative Assessment of Hand Function in Healthy Subjects and Post-Stroke Patients with the Action Research Arm Test

Padilla-Magaña, Jesus Fernando

doi:10.3390/s22103604

Cited by 8 publications

(8 citation statements)

References 34 publications

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“…Resistive bend sensors are employed in the commercially available “Cyberglove” device (CyberGlove Systems LLC, San Jose, CA) (Merians et al, 2009 ; Lee, 2014 ; Thielbar et al, 2014 ; Dimbwadyo-Terrer et al, 2016 ; Ranganathan, 2017 ; Jarque-Bou et al, 2020 ; Padilla-Magana et al, 2022 ). The CyberGlove monitors all individual finger joints.…”

Section: Resultsmentioning

confidence: 99%

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

Bates

Sunderam

2023

Front. Hum. Neurosci.

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IntroductionVarious neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer.MethodsHere, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement.ResultsA majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon.DiscussionWe conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.

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Section: Resultsmentioning

confidence: 99%

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

Bates

Sunderam

2023

Front. Hum. Neurosci.

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“…This section briefly describes the experimental protocol for data acquisition. The extension and flexion angles of eleven finger joints in healthy subjects [ 31 ] and post-stroke patients [ 32 ] were measured during the performance of sixteen activities of the Action Research Arm Test (ARAT) corresponding to the subtests (grasp, grip, and pinch) using an instrumented glove (Cyberglove Systems LLC; San Jose, CA, USA). The eleven finger joints angles recorded were: thumb carpometacarpal (CMC) joint, thumb, index, middle, ring, and little metacarpophalangeal (MCP) joints, thumb interphalangeal (IP) joint, and index, middle, ring, and little proximal interphalangeal (PIP) joints.…”

Section: Methodsmentioning

confidence: 99%

“…The eleven finger joints angles recorded were: thumb carpometacarpal (CMC) joint, thumb, index, middle, ring, and little metacarpophalangeal (MCP) joints, thumb interphalangeal (IP) joint, and index, middle, ring, and little proximal interphalangeal (PIP) joints. For more information about the study protocol, please refer to [ 31 , 32 ]. The information obtained allowed us to construct a dataset composed of the flexion and extension angles of 25 healthy subjects during the performance of 400 ARAT activities and 12 post-stroke patients during the execution of 144 ARAT activities.…”

Section: Methodsmentioning

confidence: 99%

Classification Models of Action Research Arm Test Activities in Post-Stroke Patients Based on Human Hand Motion

Padilla-Magaña

Pitarch

2022

Sensors

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The Action Research Arm Test (ARAT) presents a ceiling effect that prevents the detection of improvements produced with rehabilitation treatments in stroke patients with mild finger joint impairments. The aim of this study was to develop classification models to predict whether activities with similar ARAT scores were performed by a healthy subject or by a subject post-stroke using the extension and flexion angles of 11 finger joints as features. For this purpose, we used three algorithms: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The dataset presented class imbalance, and the classification models presented a low recall, especially in the stroke class. Therefore, we implemented class balance using Borderline-SMOTE. After data balancing the classification models showed significantly higher accuracy, recall, f1-score, and AUC. However, after data balancing, the SVM classifier showed a higher performance with a precision of 98%, a recall of 97.5%, and an AUC of 0.996. The results showed that classification models based on human hand motion features in combination with the oversampling algorithm Borderline-SMOTE achieve higher performance. Furthermore, our study suggests that there are differences in ARAT activities performed between healthy and post-stroke individuals that are not detected by the ARAT scoring process.

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“…Hand gripping or grasping is a basic movement that involves flexion and extension of multiple joints. Up to now, the studies on human hand grip mainly focuses on ROM, including active, passive and functional ROMs [10][11][12][13][14], which are measured statically. Many robotic hands claimed that their ROMs can reach or be close to that of normal hands [15,16].…”

Section: Ofmentioning

confidence: 99%

Finger Kinematics During Human Hand Grip and Release

Li¹,

Wen²,

Duanmu³

et al. 2023

Preprint

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Bionic robotic hand can perform many movements similar to human hand. But there is still a significant gap in manipulation between robot and human hand. It is necessary to understand the finger kinematics and motion patterns of human hand to improve the performance of robotic hand. This study aimed to comprehensively investigate normal hand motion patterns by evaluating the kinematics of hand grip and release in healthy individuals. The data corresponding to rapid grip and release were collected from the dominant hand of 22 healthy people by sensory glove. The kinematics of 14 finger joints were analyzed, including the dynamic range of motion (ROM), peak velocity, joint sequence and finger sequence. The results show that the proximal interphalangeal (PIP) joint had a largest dynamic ROM than metacarpophalangeal (MCP) and distal interphalangeal (DIP) joints. Besides, the PIP joint had the highest peak velocity, both in flexion and extension. For joint sequence, the PIP joint moved prior to the DIP or MCP joints during flexion, while extension started in DIP or MCP joints, followed by PIP joint. Regarding the finger sequence, the thumb started to move before four fingers, and stopped moving after the fingers during both grip and release. This study explored the normal motion patterns in hand grip and release, which provided kinematic reference for the design of robotic hand and thus contribute to its development.

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Quantitative Assessment of Hand Function in Healthy Subjects and Post-Stroke Patients with the Action Research Arm Test

Cited by 8 publications

References 34 publications

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

Classification Models of Action Research Arm Test Activities in Post-Stroke Patients Based on Human Hand Motion

Finger Kinematics During Human Hand Grip and Release

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