iRest: Interactive rehabilitation and assessment tool

Rahman, Hamidah Abdul; Narayanan, Aqilah Leela T.; Xiang, Khor Kang; Ming, Eileen Su Lee; Fai, Yeong Che; Khan, Qamer Iqbal

doi:10.1109/ascc.2015.7244656

Cited by 10 publications

(18 citation statements)

References 12 publications

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“…"Peaks metric" used as (-ve) number of peaks to makes increment of the peaks metric in lin e wit h increment of the smoothness [6,23,15,20,21,28]. The distance travelled and the number o f data has normalized the number of peaks in the velocity profile [25,26,32] T he metric of movement smoothness can be evaluated by dividing the mean speed wit h th e p ea k speed [7,14,15,21,23,24,28]. T ime to Peak Velocity (T PV) T he T PV defined as the percentage of the time to reach the peak velocity o f t he p atients' h and movement [26].…”

Section: Number Of Peaks Speed (Nps)mentioning

confidence: 99%

“…The distance travelled and the number o f data has normalized the number of peaks in the velocity profile [25,26,32] T he metric of movement smoothness can be evaluated by dividing the mean speed wit h th e p ea k speed [7,14,15,21,23,24,28]. T ime to Peak Velocity (T PV) T he T PV defined as the percentage of the time to reach the peak velocity o f t he p atients' h and movement [26]. T he difference between the time where peak velocity has been reached and the time where velocity firstly exceeds five percent of peak velocity can determine the TPV [34].…”

Section: Number Of Peaks Speed (Nps)mentioning

confidence: 99%

See 1 more Smart Citation

Kinematic variables for upper limb rehabilitation robot and correlations with clinical scales: A review

Mazlan¹,

Rahman²,

Fai³

et al. 2020

Bulletin EEI

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The paper presents a comprehensive review carried out to identify the kinematic variables used in upper body rehabilitation assisted by robotic devices to assess the motor impairment of stroke patients and investigates the correlation between the kinematic variables and the clinical scales. Twenty-nine kinematic variables have been studied from twenty-eight articles involving 738 subacute or chronic stroke patients. The movement of speed, distance, accuracy, peak speed, peak speed ratio and number of peak speed were found to be the most frequently used kinematic variables in the aforementioned studies. Seven out of twenty-eight included articles examined the correlations between the kinematic variables used with the clinical scales. Some kinematic variables seem to have a strong correlation with the clinical scales but most of the kinematic variables have a moderate or weak correlation value. The important kinematic variables for evaluating the motor performance during rehabilitation assisted by robotic devices have been discussed. A suitable selected set of kinematic variables and clinical scale can potentially enhance the correlation value, at the same time can predict the clinical score evaluated by physiotherapist during the rehabilitation program with a high degree of accuracy.

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Section: Number Of Peaks Speed (Nps)mentioning

confidence: 99%

Section: Number Of Peaks Speed (Nps)mentioning

confidence: 99%

Kinematic variables for upper limb rehabilitation robot and correlations with clinical scales: A review

Mazlan¹,

Rahman²,

Fai³

et al. 2020

Bulletin EEI

Self Cite

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show abstract

“…These games were developed as a training strategy to train the subject's upper limb function while playing an interactive game. The shapes of the games were selected based on the isolated and combined movement of shoulder and elbow movements [16,23]. These modules were reported in our previous study [24].…”

Section: Training Module Descriptionmentioning

confidence: 99%

“…For example, MIT-Manus [12] and a two degree of freedom (DOF) elbow-shoulder robot [13], which were developed for unrestricted unilateral shoulder and elbow movements in the horizontal plane, showed the potential of additional therapy aided by robotic technology to improve motor function. Other robotic technology include the ARM Guide [14] robot which trains reaching movements in a straight line trajectory, the ReachMAN [15] and iRest [16,17] which involve training and assessment respectively using combination of reaching movements together with hand manipulation. In addition, CR2 robot was developed to train hand and wrist rotation movements by reconfigure the setting of the device [18,19].…”

Section: Introductionmentioning

confidence: 99%

A Simple Position Sensing Device for Upper Limb Rehabilitation

Rahman

Samuthram

Yeong³

et al. 2018

IJIE

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“…The diameter of the green dot increases when the moving fixture of the handle moves away from the static fixture. Our previous studies discussed details development of these robotic assessment modules [37][38][39]. There are evidences which showed that elbow extension angle affects the ROM of hand rotation which are forearm pronation and supination [40].…”

Section: Softwarementioning

confidence: 99%