Probabilistic Model-based Learning Control of a Soft Pneumatic Glove for Hand Rehabilitation

OBJECTIVE

Stroke survivors are usually unable to perform activities of daily living (ADL) independently due to loss of hand functions. Soft pneumatic gloves provide a promising assistance approach for stroke survivors to conduct ADL tasks. However, few studies have explored effective control strategies for the 'human-soft robot' integrated system due to challenges in the nonlinearities of soft robots and uncertainties of human intentions. Therefore, this work aims to develop control approaches for the system to improve stroke survivors hand functions.

METHODS

Firstly, a soft pneumatic glove was utilized to aid with stroke-impaired hands. Secondly, a probabilistic model-based learning control approach was proposed to overcome the challenges. Then a task-oriented intention-driven training modality was designed. Finally, the control performance was evaluated on three able-bodied subjects and three stroke survivors who attended 20-session rehabilitation training.

RESULTS

The proposed approach could enable the soft pneumatic glove to provide adaptive assistance for all participants to accomplish different tasks. The tracking error and muscle co-contraction index showed decreasing trends while the hand gesture index showed an increasing tendency over training sessions. All stroke survivors showed improved hand functions and better muscle coordination after training.

CONCLUSION

This work developed a learning-based soft robotic glove training system and demonstrated its potential in post-stroke hand rehabilitation.

SIGNIFICANCE

This work promotes the application of soft robotic training systems in stroke rehabilitation.

Verification of Finger Joint Stiffness Estimation Method With Soft Robotic Actuator

Stroke has been the leading cause of disability due to the induced spasticity in the upper extremity. The constant flexion of spastic fingers following stroke has not been well described. Accurate measurements for joint stiffness help clinicians have a better access to the level of impairment after stroke. Previously, we conducted a method for quantifying the passive finger joint stiffness based on the pressure-angle relationship between the spastic fingers and the soft-elastic composite actuator (SECA). However, it lacks a ground-truth to demonstrate the compatibility between the SECA-facilitated stiffness estimation and standard joint stiffness quantification procedure. In this study, we compare the passive metacarpophalangeal (MCP) joint stiffness measured using the SECA with the results from our designed standalone mechatronics device, which measures the passive metacarpophalangeal joint torque and angle during passive finger rotation. Results obtained from the fitting model that concludes the stiffness characteristic are further compared with the results obtained from SECA-Finger model, as well as the clinical score of Modified Ashworth Scale (MAS) for grading spasticity. These findings suggest the possibility of passive MCP joint stiffness quantification using the soft robotic actuator during the performance of different tasks in hand rehabilitation.

Soft Rehabilitation Actuator With Integrated Post-stroke Finger Spasticity Evaluation

Strokes cause severe impairment of hand function because of the spasticity in the affected upper extremities. Proper spasticity evaluation is critical to facilitate neural plasticity for rehabilitation after stroke. However, existing methods for measuring spasticity, e.g. Modified Ashworth Scale (MAS), highly depends on clinicians' experiences, which are subjective and lacks quantitative details. Here, we introduce the first rehabilitation actuator that objectively reflects the condition of post-stroke finger spasticity. The actuator is 3D printed with soft materials. By considering the finger and the actuator together, the spasticity, i.e. stiffness, in finger is obtained from the pressure-angle relationship. The method is validated by simulations using finite element analysis (FEA) and experiments on mannequin fingers. Furthermore, it is examined on four stroke subjects and four healthy subjects. Results show the finger stiffness increases significantly from healthy subjects to stroke subjects, particularly those with high MAS score. For patients with the same MAS score, stiffness variation can be a few times. With this soft actuator, a hand rehabilitation robot that may tell the therapeutic progress during the rehabilitation training is readily available.

Model-based online learning and adaptive control for a “human-wearable soft robot” integrated system

Soft robots are considered intrinsically safe with regard to human–robot interaction. This has motivated the development and investigation of soft medical robots, such as soft robotic gloves for stroke rehabilitation. However, the output force of conventional purely soft actuators is usually limited. This restricts their application in stroke rehabilitation, which requires a large force and bidirectional movement. In addition, accurate control of soft actuators is difficult owing to the nonlinearity of purely soft actuators. In this study, a soft robotic glove is designed based on a soft-elastic composite actuator (SECA) that integrates an elastic torque compensating layer to increase the output force as well as achieving bidirectional movement. Such a hybrid design also significantly reduces the degree of nonlinearity compared with a purely soft actuator. A model-based online learning and adaptive control algorithm is proposed for the wearable soft robotic glove, taking its interaction environment into account, namely, the human hand/finger. The designed hybrid controller enables the soft robotic glove to adapt to different hand conditions for reference tracking. Experimental results show that satisfactory tracking performance can be achieved on both healthy subjects and stroke subjects (with the tracking root mean square error (RMSE) < 0.05 rad). Meanwhile, the controller can output an actuator–finger model for each individual subject (with the learning error RMSE < 0.06 rad), which provides information on the condition of the finger and, thus, has further potential clinical application.