Control Strategies and Artificial Intelligence in Rehabilitation Robotics

By Novak, Domen; Riener, Robert | AI Magazine, Winter 2015 | Go to article overview

Control Strategies and Artificial Intelligence in Rehabilitation Robotics


Novak, Domen, Riener, Robert, AI Magazine


Rehabilitation robots physically support and guide a patient's limb during motor therapy, but require sophisticated control algorithms and artificial intelligence to do so. This article provides an overview of the state of the art in this area. It begins with the dominant paradigm of assistive control, from impedance-based cooperative controller through electromyography and intention estimation. It then covers challenge-based algorithms, which provide more difficult and complex tasks for the patient to perform through resistive control and error augmentation. Furthermore, it describes exercise adaptation algorithms that change the overall exercise intensity based on the patient's performance or physiological responses, as well as socially assistive robots that provide only verbal and visual guidance. The article concludes with a discussion of the current challenges in rehabilitation robot software: evaluating existing control strategies in a clinical setting as well as increasing the robot's autonomy using entirely new artificial intelligence techniques.

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As robotics moved from industrial to service applications, engineers began looking for new tasks that could be automated with robots. Industrial tasks had been a perfect candidate for automation since they are physically exhausting and require high precision. Motor rehabilitation seemed like a similarly appropriate robotics application. In the course of rehabilitation, the patient must exercise by performing limb motions thousands of times, and the therapist must physically support and guide the patient's limb during these motions. Since therapists inevitably get exhausted, a rehabilitation robot could support and guide the limb instead.

Numerous rehabilitation robots have been designed for both the upper (figure 1) and lower limbs (figure 2). The two most famous arm rehabilitation robots are the MIT-MANUS, now sold as the InMotion ARM (Interactive Motion Technologies, USA) and the ARMin, now sold as the ArmeoPower (Hocoma AG, Switzerland). The most famous leg rehabilitation robot is the commercially available Lokomat (Hocoma AG, Switzerland), with another notable example being the Gait Trainer (Reha-Stim, Germany). All of these, and many other robots, were developed in order to support and guide the patient's limbs. However, appropriate hardware is not enough; both therapists and robots need to intelligently adapt their support to ensure proper exercise. Mistakes should be corrected, but the patient should exercise actively and intensely, so the support should not be excessive.

The first rehabilitation robot controllers did not adapt their support to the patient at all. They were very stiff, and essentially guided the patient's limbs along a predefined trajectory with little care for what the patient was doing or wanted to do. Clinical tests found that patients put significantly less effort into robot-aided exercise with such controllers than into therapist-aided exercise, and frequently just let the robot move their passive limbs without actively participating in the motion (Israel et al. 2006, Ziherl et al. 2010). This "slacking" process leads to slower neuromotor recovery (Casadio and Sanguined 2012). To avoid it, the robot needs to adopt a control strategy that assists the patient only as needed: a cooperative control strategy.

Help Me Help You: Cooperative Assistive Control

Assistive controllers are the dominant control paradigm in rehabilitation robotics, and are used in the majority of commercial systems. They operate on the level of the individual motion, helping the patient complete a motion within a desired time while correcting any major errors (such as large deviations from an optimal trajectory). The main characteristic of modern assistive controllers is that they only help as much as it is necessary for the patient to complete a motion, an approach called patient-cooperative control (Riener et al. …

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