Human robot interaction is a new and rapidly growing field and its application in the realm of rehabilitation and physical care is a major focus area of research worldwide. This paper discusses the development and implementation of a wireless motion capture system for the human arm which can be used for physical therapy or real-time control of a robotic arm, among many other potential applications. The system is comprised of a mechanical brace with rotary potentiometers inserted at the different joints to capture position data. It also contains surface electrodes which acquire electromyographic signals through the CleveMed BioRadio device. The brace interfaces with a software subsystem which displays real time data signals. The software includes a 3D arm model which imitates the actual movement of a subject's arm under testing. This project began as part of the Rochester Institute of Technology's Undergraduate Multidisciplinary Senior Design curriculum and has been integrated into the overall research objectives of the Biomechatronic Learning Laboratory.

In recent years, robot-assisted rehabilitation has gained momentum as a viable means for improving outcomes for therapeutic interventions. Such therapy experiences allow controlled and repeatable trials and quantitative evaluation of mobility metrics. Typically though these robotic devices have been focused on rehabilitation within a clinical setting. In these traditional robot-assisted rehabilitation studies, participants are required to perform goal-directed movements with the robot during a therapy session. This requires physical contact between the participant and the robot to enable precise control of the task, as well as a means to collect relevant performance data. On the other hand, non-contact means of robot interaction can provide a safe methodology for extracting the control data needed for in-home rehabilitation. As such, in this paper we discuss a contact and non-contact based method for upper-arm rehabilitation exercises that enables quantification of upper-arm movements. We evaluate our methodology on upper-arm abduction/adduction movements and discuss the advantages and limitations of each approach as applied to an in-home rehabilitation scenario.

A Hidden Markov Model (HMM) is used to predict and characterize a stochastic process that is not easily identifiable; i.e., it is hidden from the observer. This process can only be identified through an additional set of stochastic events that is not only observable, but is also responsible for producing the original hidden stochastic process mentioned above. The goal of this project is to use a HMM to track upperextremity arm motions performed in the sagittal plane (representing the hidden states) by means of the surface electromyographic (sEMG) activity associated with these arm motions (representing the observed states). After which, we intend to use the characterized sEMG signals to teleoperate a robotic manipulator. We wish to create a rehabilitative robotic platform for people who have suffered from progressive muscular degenerative disorders and neurological deficits. This platform will take advantage of any residual physiological information that is still available (non-invasively) within these individuals. The ultimate goal is to create more intelligent orthotics and wearable robotic systems for people having these types of disabilities. It is hoped that this kind of device could assist a disabled user in performing daily living tasks that require reaching for an object. The HMM algorithm presented here is implemented and tested offline in Matlab with five healthy participants. It was successful in tracking two degrees of freedom on the human arm (representing the elbow and shoulder joints) with less than 15° of error.