How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Remote Gait type classification system using markerless 2D video

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating 5 types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating 5 types of gait, at 2 severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.info:eu-repo/semantics/publishedVersio

Human Gait Analysis in Neurodegenerative Diseases: a Review

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined

Evaluation of Long-term Effects of Mild Traumatic Brain Injury on Visual Motor Control of NCAA Division I Football Athletes

Context: Current concussion evaluation assessments rely largely on static measures that may not detect subtle changes in behavior. Dynamic evaluation, such as visual-motor tracking tasks, may reveal subtle and meaningful changes in motor behavior post-concussion. Objective: This study compared measurements of performance regularity over a time series (approximate entropy; ApEn), which was derived from a visual-motor tracking task performed before concussion, post concussion, and at one month, three months, and six months post concussion. ApEn values were compared for number of previous concussions and playing position groups. Design: ApEn values were collected from the visual-motor tracking task, and history of concussion and playing position were obtained from an intake questionnaire. Post-test ApEn values were collected from participants who sustained a concussion during the study along with control subjects (matched by age and date of pre-test). Setting: Testing occurred in an office in the athletic training room, which matches the typical setting for pre and post-concussion testing. Participants: Ninety-nine Division I football athletes were baseline tested. Six concussed subjects were pre and post-tested along with 11 control subjects. One of those six was post-tested at one, three, and six months, while two were post-tested at injury, one month, and three months along with one control subject. Task and Procedure: The subject was seated at arm’s length from a laptop, pressed the distal joint of his index finger against a force plate and traced a line presented on the computer screen. Visual feedback was not displayed on the screen for the no-vision condition. An algorithm calculated ApEn from the output. Results: Post-test ApEn values were not significantly lower when compared to pre-test values and values of control participants. There was a significant effect of position group on ApEn scores, but not RMSE. There was not a significant main effect of previous number of concussions on ApEn, but ApEn was significantly lower in those with two or more previous concussions compared to those with one