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

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>In the past years, robotic systems have become increasingly popular in both upper and lower 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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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 and comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper limb exoskeleton and tested it with seventeen 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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The experiments illustrated the model’s capability to differentiate distinct motor improvement progressions among subjects and subtasks. The model suggested personalized training schedules based on motor improvement estimations for each movement in different directions. Patients’ motor performances were retained when training movements were reintroduced at a later stage.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our results 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 auspicious results for the applicability in clinical settings.</jats:p></jats:sec><jats:sec><jats:title>Trial registration</jats:title><jats:p>This study is registered in ClinicalTrials.gov (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="clintrialgov" xlink:href="NCT02770300">NCT02770300</jats:ext-link>, registered 30 March 2016, <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://clinicaltrials.gov/ct2/show/NCT02770300">https://clinicaltrials.gov/ct2/show/NCT02770300</jats:ext-link>).</jats:p></jats:sec>

The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

“…Unob-trusive and wearable technologies coupled with algorithms that recognize performed activities will make the remote evaluation of patients' adherence to prescribed exercise affordable, which until now was based on self-reported measures by subjects [17]. Assessment-driven therapy adaptation algorithms integrated into exergame solutions should ensure that the physical therapy remains at an optimal level of challenge to maintain motivation for long-term therapy programs necessary in chronic conditions [88]. Moreover, movement classification algorithms integrated into digital coach systems and socially assistive robots could support real-time evaluations and personalized feedback.…”