Soft Robotic Bilateral Hand Rehabilitation System for Fine Motor Learning

Haghshenas-Jaryani, Mahdi; Pande, Charu; Wijesundara, Muthu B. J.

doi:10.1109/icorr.2019.8779510

Cited by 14 publications

(15 citation statements)

References 15 publications

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“…This intervention method may be related to three potential principles of nerve remodeling. First, soft robotic gloves can not only activate the primary motor cortex of the affected side by driving the affected hand through the unaffected hand, but also trigger the proprioceptive input by moving the affected hand, so as to activate the corresponding primary sensory cortex (Haghshenas-Jaryani et al, 2019), and establish effective "peripheral" stimulation feedback to "central." Second, soft robotic gloves realize bilateral exercise training mode through the joint action of the unaffected hand, which is conducive to the normalization of inter-cortical inhibition between cerebral hemispheres.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Immediate Cortical Activation on Mirror Visual Feedback Combined With a Soft Robotic Bilateral Hand Rehabilitation System: A Functional Near Infrared Spectroscopy Study

et al. 2022

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BackgroundMirror visual feedback (MVF) has been widely used in neurological rehabilitation. Due to the potential gain effect of the MVF combination therapy, the related mechanisms still need be further analyzed.MethodsOur self-controlled study recruited 20 healthy subjects (age 22.150 ± 2.661 years) were asked to perform four different visual feedback tasks with simultaneous functional near infrared spectroscopy (fNIRS) monitoring. The right hand of the subjects was set as the active hand (performing active movement), and the left hand was set as the observation hand (static or performing passive movement under soft robotic bilateral hand rehabilitation system). The four VF tasks were designed as RVF Task (real visual feedback task), MVF task (mirror visual feedback task), BRM task (bilateral robotic movement task), and MVF + BRM task (Mirror visual feedback combined with bilateral robotic movement task).ResultsThe beta value of the right pre-motor cortex (PMC) of MVF task was significantly higher than the RVF task (RVF task: -0.015 ± 0.029, MVF task: 0.011 ± 0.033, P = 0.033). The beta value right primary sensorimotor cortex (SM1) in MVF + BRM task was significantly higher than MVF task (MVF task: 0.006 ± 0.040, MVF + BRM task: 0.037 ± 0.036, P = 0.016).ConclusionOur study used the synchronous fNIRS to compare the immediate hemodynamics cortical activation of four visual feedback tasks in healthy subjects. The results showed the synergistic gain effect on cortical activation from MVF combined with a soft robotic bilateral hand rehabilitation system for the first time, which could be used to guide the clinical application and the future studies.

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Section: Introductionmentioning

confidence: 99%

Synergistic Immediate Cortical Activation on Mirror Visual Feedback Combined With a Soft Robotic Bilateral Hand Rehabilitation System: A Functional Near Infrared Spectroscopy Study

et al. 2022

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“…A pneumatically actuated soft robotic digit is presented in ref. [18] for hand rehabilitation in post-stroke patients based on the bilateral therapy system. In comparison with previous similar systems, the structure of the soft actuator has not provided continuous bending.…”

Section: Introductionmentioning

confidence: 99%

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

2021

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Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

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“…Consequently, it is proposed for stroke rehabilitation. While, the clinical efficacy of robot-assisted therapy for stroke rehabilitation was controversial [ 22 – 24 ].…”

Section: Discussionmentioning

confidence: 99%

Predictive nomogram for soft robotic hand rehabilitation of patients with intracerebral hemorrhage

et al. 2022

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Background Few studies focused on the risk factors for hand rehabilitation of intracerebral hemorrhage (ICH) using of soft robotic hand therapy (SRHT). The aim of this study was to establish a predictive nomogram for soft robotic hand rehabilitation in patients with ICH. Methods According to the Brunnstrom motor recovery (BMR) stage, the patients were grouped into poor and good motor function groups. The data of patient demographic information and serum level of C-terminal Agrin Fragment (CAF), S100B and neurofilament light (NfL) were collected. The logistic regression was used to analyze the risk factors for poor hand function. Results Finally, we enrolled 102 and 103 patients in the control and SRHT groups. For the SRHT group, there were 17 and 86 cases with poor and good motor function at 6-months follow-up respectively. In the good motor function group, the Fugl-Meyer Assessment-Wrist and Hand (FMA-WH score) and BMR score at admission were all better than that in the poor motor function group respectively (p < 0.001). The mean serum level of CAF, S100B and NfL in the good motor function group were 2.5 ± 0.82 ng/mL, 286.6 ± 236.4 ng/L and 12.1 ± 10.4 pg/mL respectively, which were lower than that in the poor motor function group (p < 0.001, Table 3). The multivariate logistic regression showed that hematoma volume (OR = 1.47, p = 0.007), FMA-WH score admission (OR = 0.78, p = 0.02), S100B (OR = 1.32, p = 0.04), and NfL (OR = 1.24, p = 0.003) were all significant predictors of poor motor function. Conclusions We found that Soft robotic hands therapy benefited in hand function in patients with ICH and hematoma volume, FMA-WH score admission, S100B, and NfL were all significant predictors for poor motor function of patients with ICH.

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Soft Robotic Bilateral Hand Rehabilitation System for Fine Motor Learning

Cited by 14 publications

References 15 publications

Synergistic Immediate Cortical Activation on Mirror Visual Feedback Combined With a Soft Robotic Bilateral Hand Rehabilitation System: A Functional Near Infrared Spectroscopy Study

Synergistic Immediate Cortical Activation on Mirror Visual Feedback Combined With a Soft Robotic Bilateral Hand Rehabilitation System: A Functional Near Infrared Spectroscopy Study

Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

Predictive nomogram for soft robotic hand rehabilitation of patients with intracerebral hemorrhage

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