Soft Robotic Hand for Stroke Rehabilitation

Stroke Rehabilitation

Current Methods

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 Robot
Soft Robots Applications

3D Printed Soft Robots

  • 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

Soft Robotic Hand for Stroke Rehabilitation

  • 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

  • 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

MDP Representation

Q learning

Simulation

Execution

Closed-Loop System

Sensor Characterization

Systems Characterization

Future Work

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I’m a developer & innovator who enjoys building products and researching ways we can use AI, Blockchain & robotics to solve problems in healthcare and energy!

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Alishba Imran

Alishba Imran

I’m a developer & innovator who enjoys building products and researching ways we can use AI, Blockchain & robotics to solve problems in healthcare and energy!

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