A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use

Gaze tracking, where the point of regard of a subject is mapped onto the image of the scene the subject sees, can be employed to study the visual attention of the users of prosthetic hands. It can show whether the user is pays greater attention to the actions of their prosthetic hand as they use it to perform manipulation tasks, compared with the general population. Conventional analysis of the video data requires a human operator to identify the key areas of interest in every frame of the video data. Computer vision techniques can assist with this process, but a fully automatic systems requires large training sets. Prosthetic investigations tend to be limited in numbers. However, if the assessment task is well controlled, it is possible to make a much simpler system that uses initial input from an operator to identify the areas of interest and then the computer tracks the objects throughout the task. The tool described here, employs colour separation and edge detection on images of the visual field to identify the objects to be tracked. To simplify the computer's task further, this test uses the Southampton Hand Assessment Procedure (SHAP), to define the activity spatially and temporarily, reducing the search space for the computer. The work reported here is the development a software tool capable of identifying and tracking the Points of Regard and Areas of Interest, throughout an activity with minimum human operator input. Gaze was successfully tracked for fourteen unimpaired subjects, which was compared with the gaze of four users of myoelectric hands. The SHAP cutting task is described and the differences in attention observed with a greater number of shorter fixations by the prosthesis users compared to unimpaired subjects. There was less looking ahead to the next phase of the task by the prosthesis users

On Understanding and Measuring the Cognitive Load of Amputees for Rehabilitation and Prosthetic Development

Objective: To derive a definition of cognitive load that is applicable for amputation as well as analyze suitable research models for measuring cognitive load during prosthetic use. Defining cognitive load for amputation will improve rehabilitation methods and enable better prosthetic design. Data Sources: Elsevier, Springer, PLoS, IEEE Xplore, PubMed. Study Selection: Studies on upper-limb myoelectric prosthetics and neuroprosthetics were prioritized. For understanding measurement, lower-limb amputations and studies with healthy individuals were included. Data Extraction: Queries including ‘cognitive load’, ‘neural fatigue’, ‘brain plasticity’, ‘neuroprosthetics’, ‘upper-limb prosthetics’, and ‘amputation’ were used with peerreviewed journals or articles. Papers published within the last 6 years were prioritized. Articles on foundational principles were included regardless of date. A total of 69 articles were found: 12-amputation, 15-cognitive load, 8-phantom limb, 22-sensory feedback, 12- measurement methods. Data Synthesis: The emotional, physiological, and neurological aspects of amputation, prosthetic use, and rehabilitation aspects of cognitive load were analyzed in conjunction with measurement methods, including resolution, invasiveness, and sensitivity to user movement and environmental noise. Conclusions: Usage of ‘cognitive load’ remains consistent with its original definition. For amputation, two additional elements are needed: ‘emotional fatigue’, defined as an amputee’s emotional response, including mental concentration and emotions, and ‘neural fatigue’, the physiological and neurological effects of amputation on brain plasticity. Cognitive load is estimated via neuroimaging techniques, including EEG, fMRI, and fNIRS. Because fNIRS measures cognitive load directly, has good temporal and spatial resolution, and is not as restricted by user movement, fNIRS is recommended for most cognitive load studies

Functionally Adaptive Myosite Selection using conformable HD sEMG electrodes for movement-pattern classification

Myoelectric prosthesis systems currently use advanced control schemes such as pattern recognition to classify muscle activation signals as intended movement classes. For this classification, generally, untargeted, equally spaced electrodes placed circumferentially around the muscle belly of the forearm, are used for acquisition of surface electromyogram (sEMG) for tran-radial amputee subjects. We propose a novel system, consisting of a hardware and software component. We built the hardware component in the form of a flexible and conformable high-density sEMG array. We tested the signal quality and electrode-skin contact characteristics to demonstrate the quality and conformability of the electrode array. We built the software component of the system based on separability criteria. This proposed system is called functionally adaptive myoelectrode site (myosite) selection (FAMS) and is to identify optimal myosites for pattern recognition. Our study investigates the effects of optimal myosite selection with increase in the number of movement classes and inclusion of fine motor movements. We also used myosite selection from current clinical and research procedures and compared the performances of FAMS to existing systems. Results of our study indicate that using optimal myosites selected using FAMS for movement pattern classification improves performance and this becomes more evident with increase in the number of selected myosites. The significance of using optimal myosites increases when more movement classes are included. This work also shows that the optimal myosites change spatially with the type and number of movement classes included for classification. We then explored other future applications of 1) FAMS in temporal adaptations to help prosthetic users begin early use of pattern recognition based prosthesis system and 2) extending FAMS to site selection for direct control so as to make FAMS a universal electrode interface for myoelectric prosthesis. Preliminary study results in these areas are presented in this work. The electrode design was further improved to fit inside of a prosthesis. This system has the capabilities to become an off-the-shelf universal system that can be prescribed for any myoelectric prosthesis user irrespective of their level of amputation and experience with using a myoelectric prosthesis. This system can reduce pre-prosthetic training time and facilitate early fitting. This system also removes the need for refitting every time the user changes the movement classes controlled by the prosthesis