Vision-based posture assessment to detect and categorize compensation during robotic rehabilitation therapy

Taati, Babak; Wang, Rosalie H.; Huq, Rajibul; Snoek, Jasper; Mihailidis, Alex

doi:10.1109/biorob.2012.6290668

Cited by 34 publications

(27 citation statements)

References 18 publications

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“…Our methods showed better classification performance, with an average F1-score of 0.989 for side-and-side reaching and 0.998 for up-and-down reaching. Babak Taati et al [12,16,23] applied a camera-based system to identify and categorize compensatory movements by a multiclass classifier. An acceptable accuracy (86% per frame) was achieved in healthy adult subjects who were asked to simulate a series of compensation movements.…”

Section: Discussionmentioning

confidence: 99%

“…The main drawback of the sensor-based systems is the possibility of inducing unnatural movements due to the attached sensors. It is difficult to find an unobtrusive and easy-to-use solution [12]. Moreover, the validity and reliability of the outcome estimates from these wearable sensors for rehabilitation of stroke survivors is a daunting challenge for researchers [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Cai

Zhang

et al. 2019

J NeuroEngineering Rehabil

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Background: Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms. Methods: Eight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-anddown reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements. Results: Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements. Conclusions: Good classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Cai

Zhang

et al. 2019

J NeuroEngineering Rehabil

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“…Automated real-time auditory feedback for compensation (Thielman, 2010) was considered as a safer alternative. In the context of motion-based therapeutic games, addressing compensation has been limited to detecting compensation (Taati, Wang, Huq, Snoek, & Mihailidis, 2012), and preventing users from succeeding with it (Da Gama et al, 2012), rather than actively encouraging users to reduce compensation. Therefore, current efforts for addressing compensation in unsupervised therapeutic exercises may not reduce compensation as well as therapist supervision.…”

Section: Introductionmentioning

confidence: 99%

Reducing Compensatory Motions in Motion-Based Video Games for Stroke Rehabilitation

Alankuş

Kelleher

2014

Human–Computer Interaction

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Stroke survivors' unsupervised therapeutic exercise motions are often accompanied by harmful compensatory motions that prevent proper motor recovery and introduce additional health issues. These compensatory motions are often performed unconsciously and are difficult to prevent. Motion-based games show promise for motivating patients to perform stroke rehabilitation exercises at home by themselves. Currently, exercises with these games are likely to contain undesired compensatory motions. In this article, we provide the design and empirical evaluation of a motion-based game system that addresses the issue of compensation in therapeutic games. We introduce a technique to identify and measure compensation, develop a game that meaningfully uses exercise and compensation as inputs, and use incentives and disincentives to reduce compensation. We show that this technique outperforms existing approaches by significantly reducing compensatory motions during therapeutic exercise. This has important implications for therapeutic games, which can use our findings to improve the quality of motions to be closer to therapist-supervised motions. Our techniques can increase the effectiveness of therapeutic games and reduce the possibility that they may cause harm in long-term use.Gazihan Alankus (gazihan.alankus@ieu.edu.tr, http://homes.ieu.edu.tr/∼galankus) is a computer scientist with an interest in human-computer interaction and using games for therapeutic purposes; he is an Assistant Professor in the

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“…Applying intelligent rehabilitation techniques, such as rehabilitation robots [1][2][3][4][5][6][7] or in-home rehabilitation systems based on wearable sensors [8][9][10][11][12], to help people suffering from limited mobile ability to perform physical rehabilitation is of significance. Such intelligent techniques alleviate the shortage of physical therapists and realize data monitoring for more accurate evaluations on patients [13][14][15][16], and thus have become a new trend in recent years. The traditional scenario for lower limb rehabilitation is level walking.…”

Section: Introductionmentioning

confidence: 99%

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

et al. 2020

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Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular–mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal–Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation.

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Vision-based posture assessment to detect and categorize compensation during robotic rehabilitation therapy

Cited by 34 publications

References 18 publications

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Reducing Compensatory Motions in Motion-Based Video Games for Stroke Rehabilitation

A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

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