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The potential of iRest in measuring the hand function performance of stroke patients

Abdul Rahman H, Khor KX, Yeong CF, Su EL, Narayanan AL

Biomed Mater Eng, 2017;28(2):105-116.
PMID: 28372264 DOI: 10.3233/BME-171660

BACKGROUND: Clinical scales such as Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) are widely used to evaluate stroke patient's motor performance. However, there are several limitations with these assessment scales such as subjectivity, lack of repeatability, time-consuming and highly depend on the ability of the physiotherapy. In contrast, robot-based assessments are objective, repeatable, and could potentially reduce the assessment time. However, robot-based assessments are not as well established as conventional assessment scale and the correlation to conventional assessment scale is unclear.
OBJECTIVE: This study was carried out to identify important parameters in designing tasks that efficiently assess hand function of stroke patients and to quantify potential benefits of robotic assessment modules to predict the conventional assessment score with iRest.
METHODS: Twelve predictive variables were explored, relating to movement time, velocity, strategy, accuracy and smoothness from three robotic assessment modules which are Draw I, Draw Diamond and Draw Circle. Regression models using up to four predictors were developed to describe the MAS.
RESULTS: Results show that the time given should be not too long and it would affect the trajectory error. Besides, result also shows that it is possible to use iRest in predicting MAS score.
CONCLUSION: There is a potential of using iRest, a non-motorized device in predicting MAS score.
Fulltext Quantitative metrics for evaluating surgical dexterity using virtual reality simulations

Wu M, Kit CY, Su ELM, Yeong CF, Ahmmad SNZ, Holderbaum W, et al.

PLoS One, 2025;20(3):e0318660.
PMID: 40029914 DOI: 10.1371/journal.pone.0318660

This study develops and evaluates quantitative metrics to assess surgical dexterity within virtual reality (VR) simulations to enhance surgical training and performance. By employing advanced VR technology, this research systematically investigates the influence of controlled experimental factors-posture, handedness, and visual magnification-on surgical performance. The impact of human factors such as surgical specialty, experience, and lifestyle factors like sleep and caffeine consumption on surgical dexterity is also analyzed. The findings reveal that seated posture, dominant hand usage, and enhanced visual magnification significantly improve surgical precision and eﬃciency. Contrary to common beliefs, lifestyle factors such as sleep duration and coffee consumption showed minimal impact on performance metrics. The study highlights the potential of VR simulations to provide a controlled, replicable, and safe environment for surgical training, emphasizing the importance of personalized training protocols that cater to individual surgeon's needs. The insights from this research advocate for integrating quantitative, objective metrics in surgical training programs to refine and accelerate dexterity acquisition, ultimately aiming to improve patient outcomes and surgical care.

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