Stroke survivors often compensate for the loss of motor function in their distal joints by altered use of more proximal joints and body segments. This can be detrimental to the rehabilitation process in the long-term as it hinders regaining normal function of impaired joints in the training. It is important to detect such motor patterns and correct them as soon as possible, either via visual or verbal feedback or through assistive control mechanisms.

Challenges 
Detecting these unusual motor patterns can be challenging due to the following issues:

• Context dependency
  These behaviors vary significantly across patients and across tasks. A motor behavior labeled compensatory in one context may be healthy in another context! 
• Sparse data
  Only limited amounts of motor behavior data can be obtained from patients!
• Label Uncertainty
  Experts may not concur on labeling motor behaviors as compensatory or non-compensatory which can lead to high output noise!
   

Our Goal: Detection and Correction of unhealthy motion patterns
For this we require sensors, that can observe patient motions, actuators, that can influence patient motions and a control algorithm, that can derive the correction actuation given the observations from the sensors. An example of a system of sensor and actuators is the ReHyb Hybrid Exoskeleton that has encoders that can observe patient joint kinematics and can influence patient motions via Torque and FES control.

The aim is then to develop a safe and stable corrective control design for the detection and correction of unhealthy motor behavior.