Gesture therapy: A vision-based system for upper extremity stroke rehabilitation

Abstract

Stroke is the main cause of motor and cognitive disabilities requiring therapy in the world. Therefor it is important to develop rehabilitation technology that allows individuals who had suffered a stroke to practice intensive movement training without the expense of an always-present therapist. We have developed a low-cost vision-based system that allows stroke survivors to practice arm movement exercises at home or at the clinic, with periodic interactions with a therapist. The system integrates a virtual environment for facilitating repetitive movement training, with computer vision algorithms that track the hand of a patient, using an inexpensive camera and a personal computer. This system, called Gesture Therapy, includes a gripper with a pressure sensor to include hand and finger rehabilitation; and it tracks the head of the patient to detect and avoid trunk compensation. It has been evaluated in a controlled clinical trial at the National Institute for Neurology and Neurosurgery in Mexico City, comparing it with conventional occupational therapy. In this paper we describe the latest version of the Gesture Therapy System and summarize the results of the clinical trail.

New Developments in the Gesture Therapy Platform - Past, Present and Future of our Research

“…Patients treated with GT demonstrated greater interest and claimed higher importance and utility. Full details can be found in (Sucar et al, 2010).…”

“…From engineering to clinical applications, and from traslational research to neuroergonomics; we have contributed with controllers for arm and finger exercising , different artificial intelligence based adaptation algorithms to make the therapy adjusts to the changing needs of the patient as well as learning instructions from the therapist (Ávila-Sansores et al, 2013), a compensation detection algorithm that can ensure trunk compensation is avoided (Sucar et al, 2009), an initial set of necessary design criteria for serious games development in VR (Oropeza Salas, 2012), a set of serious games formulated capitalizing on these design principles (Oropeza Salas, 2012;Sucar et al, 2013), clinical evidence of the validity of the technique as compared to occupational therapy (Sucar et al, 2010), and an initial picture of how the brain responds to VR in terms of functional reorganization strategies . In parallel to this research efforts, we have been developing the platform Gesture Therapy (GT) (Sucar et al, 2010;Sucar et al, 2013), the practical realization of all the knowledge we have been gathering. Of course, the field of neurorehabilitation is still in its infancy with rehabilitation treatments having a disappointingly modest effect on impairment (Krakauer et al, 2012) and thus the journey has just begun.…”

Video-based Feedback for Assisting Physical Activity

“…Existing works usually focus on detection, recognition and posterior analysis of performed actions [Kato, 2010,Burke et al, 2009,Sucar et al, 2010,Ofli et al, 2016. Recent works have explored approaches for measuring how well an action is performed [Pirsiavash et al, 2014, Tao et al, 2016, Wang et al, 2013, Ofli et al, 2016, which can be used as a home based rehabilitation application.…”

“…Usually, the supervised therapy session is done within a short period of time mainly due to economical reasons. In order to support and maintain the rehabilitation of stroke survivors, continuous home based therapy systems are being investigated [Langhorne et al, 2005, Zhou and Hu, 2008, Sucar et al, 2010, Hondori et al, 2013, Mousavi Hondori and Khademi, 2014, Chaaraoui et al, 2012, Ofli et al, 2016. Having these systems at home and easily accessible, the patients keep a good level of motivation to do more exercise.…”

“…An affordable technology to support these home based systems are RGB-D sensors, more specifically, the Microsoft Kinect 1 sensor. Generally, these systems combine exercises with video games [Kato, 2010, Burke et al, 2009 or emulate a physical therapy session [Ofli et al, 2016, Sucar et al, 2010.…”