Soft Robotic Hand for Stroke Rehabilitation

Stroke Rehabilitation

A stroke occurs when blood supply to a part of the brain is blocked or reduced. This can be due to a blockage in a blood vessel, or a ruptured blood vessel.

Current Methods

There is a “Golden period” with optimal recovery of hand function which occurs typically within the first 3 months after a stroke. We want to be able to improve hand motoric functions within this period through identical and repetitive movements by the impaired hands, as the brain reorganizes (neuroplasticity) after stroke, to recover motoric function.

Existing end effectors and exoskeletons.
  • Motor Function: assess gross motor movements and a series of general impairment measures when using the upper extremities.
  • Global Stroke Severity: assess the severity of stroke through global assessment of deficits post stroke.
  • Muscle Strength: assess muscle power and strength during movement and tasks.
  • Dexterity: assess fine motor and manual skills through a variety of tasks, particularly with the use of the hand.
  • Range of Motion: assess ability to freely move upper extremity at joints both passively and actively.
  • Proprioception: assess bodily sensory awareness and location of limbs.
  • Activities of Daily Living: assess performance and level of independence in various everyday tasks.
  • Spasticity: assess the tone of muscles controlled by signals from the brain. If the part of your brain that sends these control signals is damaged by a stroke, then the muscle may become too active.

Existing Barriers With Exoskeleton Devices

Exoskeleton Hand
  • Powered lower and upper extremities exoskeletons sell for $70,000 — $120,000 each on average and can weight upwards of 51 lbs.
The ReWalk Exoskeleton

Soft Robots

Soft robots can be used to solve a lot of the challenges faced with traditional exoskeletons. They have the advantages of higher flexibility, safer operations, lightweight and simplified production; resulting in lower manufacturing cost.

Soft Robot
Soft Robots Applications

3D Printed Soft Robots

Early 3D printing technologies were limited to rigid materials, typically made from hard plastics. Today, additive manufacturing enables the rapid design and fabrication of soft robotics that can further reduce the cost of manufacutring/developement of hand exoskeletons.

  • In fused deposition modelling (FDM) — a) and b): a thermoplastic filament is heated (ΔT) by an extrusion head and pushed through an extrusion nozzle to generate pneumatic actuators capable of lifting a 3.2kg chair.
  • Direct ink writing (DIW) — c) and d): of composite hydrogel inks using pressure.
  • Selective laser sintering (SLS) — e) and f): of thermoplastic polyurethane (TPU) powders to create a monolithic pneumatically actuated hand capable of safely interfacing with humans.
Extrusion-based and powder-based 3D printing

Drawbacks of Soft Robotics

Most soft robotic gloves today mainly provide flexional movement to grasp objects, but can not produce sufficient extensional force for stroke patients who are unable to extend their fingers because of spasticity. This leads to a situation in which a stroke survivor is able to grasp an object but can not release it.

Soft Robotic Hand for Stroke Rehabilitation

We need a new soft robotic glove that is designed to assist both flexional and extensional movement while retaining a small size (15 mm in thickness or less) by incorporating an elastic torque-compensating layer into the soft actuator.

  • more degrees of freedom and a larger range of motion.
  • low component cost due to inexpensive materials (e.g. fabrics, elastomers, etc.).
  • safe human-robotic interaction due to the soft and compliant materials used for their fabrication.
  • portability.

Machine Learning

Control for Manipulation

Soft robots have a limited output force whereas soft robotic hands need to be able to supply a strong force when grasping various objects in their role. The development of control algorithms remains a challenge for soft robots because of the nonlinearity of soft actuators and their interaction with the environment.

  • Model-free controllers: usually based on machine learning techniques or empirical methods. Such controllers have great advantages in highly nonlinear, non-uniform, and unstructured environment situations where modeling is almost impossible.
  • Model-based controllers: usually need analytical models to derive the controller. These controllers have more accurate and reliable performance than model-free controllers for uniform soft manipulators in known environments. However, they usually require well-defined dynamical models for the soft robots, which may not be easy to construct based on rigid-body assumptions.
  • Hybrid controllers: combine model-free and model-based controllers, and are usually based on an analytical model to capture the main part of the system’s intrinsic properties and a data learning model to compensate dynamic uncertainties.

Control Algorithm

These soft-bodied agents can be controlled with the aid of Reinforcement Learning (RL) by making machines that are able to execute and identify optimal behavior in terms of a certain reward (or loss) function. This approach is highly motivated by what is presented in this paper by the University of Science and Technology of China.

MDP Representation


Q learning

I use a function approximation Q-Learning to train the control policy. Each episode, the soft robot executes a sequence of actions from the initial state. On the basis of the above state, action, reward representation, a “final action” is introduced. This action does not affect the shape of the robot, but indicating this episode will be stopped. So, the entire learning process consists of repeating episodes from the initial state to the “final action”.


I simulated the control of a soft robot gripper using a function approximation Q-learning to train the control policy for a simple manipulation task. This was done with SOFA using the Soft Robotics Toolkit.


For closed-loop control from each time you obtain the current state of the soft robot from the actual sensor. You then input this state into the Q function to get and execute the optimal action of the policy.

Closed-Loop System

Sensor Characterization

A soft robot may interact with a wide variety of objects in the environment, where some of the information of these objects and the robot itself cannot be effectively perceived. For example, hardness or weight of an object that the robot tries to pick up can potentially interfere with the interaction.

Systems Characterization

In contrast, systems characterization collects sensor data in a less controlled environment that mimics their use witin the field or real-time (less controlled) environment. In this case, ground truth measurements such as force are more difficult to obtain. Therefore, users often circumvent this by mapping to higher-level classifications, such as grasp success and texture recognition.

Future Work

3D printed soft robotics and Q-learning approaches for control will allow for us to develop hand exoskeletons that have lower cost, are portable, and have intuitive control compared to existing exoskeletons.



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Alishba Imran

Alishba Imran

Machine learning developer working on accelerating automation/hardware and energy storage!