A personalized limb rehabilitation training system for stroke patients

Wu, Weibin; Wang, Deli; Wang, Tianyunyang; Liu, Ming

doi:10.1109/robio.2016.7866610

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…However, most systems do not tackle current barriers to adopt robotic assessment systems for therapists. Factors such as complexity of the data analysis and the metrics used to gauge process are rarely discussed [11]. A popular method to approach complexity reduction is the use of machine learning [12].…”

Section: A Data Utilization In Rehabilitation and Recoverymentioning

confidence: 99%

Heuristic Detection of Recovery Progress using Robotic Data

Lai

Sutjipto

Carmichael

et al. 2019

2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation And

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Assessment methods for rehabilitation and recovery have recently been the focal point of research for medical professionals and engineers alike. Current assessment protocols rely on historical ordinal metrics which have been disputed despite their inter-rater reliability. Contemporary kinematic measures have allowed for new approaches to assess recovery progress. However, the abundance of data has deterred medical professionals from adopting these new protocols. This paper presents a method, based on the RMSE-LWSS (Longest Warping Subsequence) score, to distinguish outliers from systemic change for updating the personalized exercise path for users. By treating change detection as a classification problem, the incorporation of a compromised path based on the user's current capability is possible. Experiments were conducted to verify the efficacy of the method, comparing against statistical techniques for change detection and classification of pre-determined paths. The paper highlights how readily available data, rather than complex sensor systems, can be utilized to improve the robustness of personalization capabilities for robotic rehabilitation systems.

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Section: A Data Utilization In Rehabilitation and Recoverymentioning

confidence: 99%

Heuristic Detection of Recovery Progress using Robotic Data

Lai

Sutjipto

Carmichael

et al. 2019

2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation And

View full text Add to dashboard Cite

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“…in order to adjust the proposed tasks accordingly. Kinematic performance measures, such as movement accuracy, smoothness, velocity, inter-joint coordination, range of motion and stiffness [18][19][20][21][22][23][24], game-related statistics [13,25], measures of muscle activity [18], or the combination of kinematic and psychophysiological measurements [26][27][28] have been among the measures used for the design of patient-tailored training protocols. However, those approaches either focused on a single performance measure describing a specific aspect of rehabilitation or used multiple measures, but lacked the ability to meaningfully synthesize the information from these variables.…”

Section: Introductionmentioning

confidence: 99%

Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study

et al. 2020

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Background: In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. Methods: Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. Results: The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. Conclusions: The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings.

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