An Automatic Rehabilitation Assessment System for Hand Function Based on Leap Motion and Ensemble Learning

Chenguang, Li; Long, Cheng; Yang, Hongjun; Zou, Yongxiang; Huang, Fubiao

doi:10.1080/01969722.2020.1827798

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…In [94], the authors developed an ensemble learning model composed of 18 classifiers, each trained on a random subspace. Using six categories, they found 92% accuracy for Brunnstrom and 82 percent for FMA scoring systems.…”

Section: B Work In Automated Exercise Assessmentmentioning

confidence: 99%

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapistlike assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

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Section: B Work In Automated Exercise Assessmentmentioning

confidence: 99%

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Three subsets are: • Adjacency matrix group: the adjacency matrix A removes the centripetal group and the centrifugal group. • Centripetal group: the neighboring nodes far from the gravity center (node 11), such as (8, 7), (11,8), (8,9), (11,12), (12,13), (12,15). • Centrifugal group: the neighboring nodes close to the gravity center (node 11), Such as (7, 8), (8,11), (9, 8), (12,11), (13,12), (15,12).…”

Section: B Motion Analysis Based On Hagcnmentioning

confidence: 99%

“…• Centripetal group: the neighboring nodes far from the gravity center (node 11), such as (8, 7), (11,8), (8,9), (11,12), (12,13), (12,15). • Centrifugal group: the neighboring nodes close to the gravity center (node 11), Such as (7, 8), (8,11), (9, 8), (12,11), (13,12), (15,12). Matrix A n is obtained according to this strategy.…”

Section: B Motion Analysis Based On Hagcnmentioning

confidence: 99%

“…Hamaguchi et al used LM to record the finger angle changes of 24 stroke patients' hands during group flexion and extension within seven seconds, calculated the peak angle and normalized peak velocity, and then used a support vector machine method to classify the patients into six categories [10]. Li et al designed a set of combined movements of the wrist and fingers and then utilized LM to measure the angle ranges of the patient's fingers when performing the movements to evaluate the patients by ensemble learning [11]. Song et al designed a mobile phone-based automated Fugl-Meyer assessment system for stroke patients.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion

Yang

Cheng

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Quantitative assessment of hand function can assist therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. To quantitatively assess the hand motor function of patients with post-stroke hemiplegia, this study proposes a novel multi-modality fusion assessment framework. This framework includes three components: the kinematic feature extraction based on a graph convolutional network (HAGCN), the surface electromyography (sEMG) signal processing based on a multi-layer long short term memory (LSTM) network, and the quantitative assessment based on the multi-modality fusion. To the best of the authors' knowledge, this is the first study of applying a graph convolution network to assess the hand motor function. We also collect the kinematic data and sEMG data from 70 subjects who completed 28 types of hand movements. Therapists first graded patients using traditional motor assessment scales (Brunnstrom Scale and Fugl-Meyer Assessment Scale) and further refined the patient's motor assessment result by their experience. Then, we trained the HAGCN and LSTM networks and quantitatively assessed each patient based on the proposed assessment framework. Finally, the Spearman correlation coefficient (SC) between the assessment result of this study and the traditional scale are 0.908 and 0.967, demonstrating a significant correlation between the proposed assessment and the traditional scale scores. In addition, the SC value between the score of this study and the refined hand motor function is 0.997, indicating the "ceiling effect" of some traditional scales can be avoided.

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“…Further, Lee et al [ 20 ] combined Kinect v2 and FSRs and achieved the automated evaluation of 26 FMA-UE items. It is worth noting that most of these systems merely consider part of the scale: some aim to evaluate the shoulder and elbow joints [ 17 , 18 ], and others focus on assessing hand function [ 19 , 21 ]. The incompleteness diminishes their practical value.…”

Section: Introductionmentioning

confidence: 99%

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study

Shu

et al. 2022

Brain Sciences

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Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients . By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.

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An Automatic Rehabilitation Assessment System for Hand Function Based on Leap Motion and Ensemble Learning

Cited by 20 publications

References 22 publications

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study

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