An automated system for motor function assessment in stroke patients using motion sensing technology: A pilot study

Sheng, Bo; Wang, Xiang-Bin; Hou, Meijin; Huang, Jia; Xiong, Shuping; Zhang, Yanxin

doi:10.1016/j.measurement.2020.107896

Cited by 13 publications

(6 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the accuracy was the average accuracy of all exercises tested on the patients. The study in [18] developed an automated system that assesses patients using a feedforward neural network [54]. The Kinect V2 was employed due to its markerless 3D vision system, as well as its reliability in clinical settings [55].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Sarwat

Maged

et al. 2021

Sensors

View full text Add to dashboard Cite

The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Another example of an automated assessment system for upper limb rehabilitation was presented in [18], which utilizes motion capture data using the Microsoft Kinect VS [19]. The motion of 25 joints of the patient is tracked and then placed into a feedforward-neuralnetwork (FFNN)-based assessment model to calculate clinical scores.…”

Section: Introductionmentioning

confidence: 99%

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Sarwat

Maged

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…With the extracted features, we compared 10 commonly used classifiers: 1) Support Vector Machine (SVM) [57], 2) K-Nearest Neighbor (KNN) [14], 3) Decision Tree (DT) [58], 4) Random Forest (RF) [59], 5) Gaussian Naive Bayes (GNB) [60], 6) Feedforward Neural Network, (FFNN) [61], 7) AdaBoost [7], 8) Logistic Regression (LR) [62] and 9) Linear Discriminant Analysis (LDA) [63]. Based on the cited studies of classifiers, we set parameters (if any) for the involved techniques, as shown in Table III.…”

Section: Techniques In Classification Stagementioning

confidence: 99%

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

Meng

Jiang

Qin

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upperlimb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS

show abstract

“…Previous works on people with stroke show that, with the Kinect v2, hand and trunk range of motion are valid and reliable [ 26 ] and a combination of hand efficiency, hand smoothness and shoulder adduction can distinguish the reaching performance between healthy control, the less-affected side and the more-affected side of patients with stroke [ 27 , 28 , 29 ]. Yet, measures of temporal and spatial efficiency, though widely used in virtual reality rehabilitation [ 30 ], have not yet been validated.…”

Section: Introductionmentioning

confidence: 99%

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Faity¹,

Mottet²,

Froger

2022

Sensors

View full text Add to dashboard Cite

Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but it remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the analysis of upper limb reaching kinematics. Twenty-six healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce behaviour similar to that of stroke survivors. With the Kinect v2 and with the VICON, 3D upper limb and trunk motions were simultaneously recorded. The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess the number of velocity peaks and the peak hand velocity. Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the tracking limitations of the Kinect.

show abstract

An automated system for motor function assessment in stroke patients using motion sensing technology: A pilot study

Cited by 13 publications

References 44 publications

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching

Contact Info

Product

Resources

About