An automated mechanism to characterize wheelchair user performance

This paper proposes a mechanism to derive quantitative descriptions of wheelchair usage as a tool to aid Occupational Therapist with their performance assesment of mobility platform users. This is accomplished by analysing data computed from a standalone sensor package fitted on an wheelchair platform. This work builds upon previous propositions where parameters that could assist in the assessment were recommended to the authors by a qualified occupational therapist (OT). In the current scheme however the task-specific parameters that may provide the most relevant user information for the assessment are automatically revealed through a machine learning approach. Data mining techniques are used to reveal the most informative parameters, and results from three typical classifiers are presented based on learnings from manual labelling of the training data. Trials conducted by healthy volunteers gave classifications with an 81% success rate using a Random Forest classifier, a promising outcome that sets the scene for a potential clinical trial with a larger user pool

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

A passive estimator of functional degradation in power mobility device users

© 2015 IEEE. This paper documents the development of a passive technique for assessing a power mobility device user's driving proficiency during everyday driving activities outside formal assessment conditions by therapists. This is approached by first building a model by means of an Artificial Neural Network to infer longer-Term destinations for discretized bouts of travel, and subsequently drawing cues indicative of decline in driving proficiency for the duration of point-To-point navigation rather than relying on instantaneously calculated metrics. This resultant quantity, which we refer to as 'functional degradation', can then provide therapists with additional information concerning user health or serve as a leveraging parameter in combinatory shared-control mobility frameworks. Experiments conducted by able-bodied users subject to simulated noise scaled to varying degrees of functional degradation reveal a quantitative correlation between these longer-Term proficiency metrics and the magnitude of degradation experienced; a promising outcome that sets the scene for a larger-scale clinical trial

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Advances of Italian Machine Design

This 2028 Special Issue presents recent developments and achievements in the field of Mechanism and Machine Science coming from the Italian community with international collaborations and ranging from theoretical contributions to experimental and practical applications. It contains selected contributions that were accepted for presentation at the Second International Conference of IFToMM Italy, IFIT2018, that has been held in Cassino on 29 and 30 November 2018. This IFIT conference is the second event of a series that was established in 2016 by IFToMM Italy in Vicenza. IFIT was established to bring together researchers, industry professionals and students, from the Italian and the international community in an intimate, collegial and stimulating environment

Robotic and Sensor Technologies for Mobility in Older People

Maintaining independent mobility is fundamental to independent living and to the quality of life of older people. Robotic and sensor technologies may offer a lot of potential and can make a significant difference in the lives of older people and to their primary caregivers. The aim of this study was to provide a presentation of the methods that are used up till now for analysis and evaluation of human mobility utilizing sensor technologies and to give the state of the art in robotic platforms for supporting older people with mobility limitations. The literature was reviewed and systematic reviews of cohort studies and other authoritative reports were identified. The selection criteria included (1) patients with age â\u89¥60 years; (2) patients with unstable gait, with or without recurrent falls; (3) patients with slow movements, short strides, and little trunk movement; (4) sensor technologies that are currently used for mobility evaluation; and (5) robotic technologies that can serve as a supporting companion for older people with mobility limitations. One hundred eighty-one studies published up until February 2017 were identified, of which 36 were included. Two categories of research were identified from the review regarding the robot and sensor technologies: (1) sensor technologies for mobility analysis and (2) robots for supporting older people with mobility limitations. Potential for robotic and sensor technologies can be taken advantage of for evaluation and support at home for elder persons with mobility limitations in an automated way without the need of the physical presence of any medical personnel, reducing the stress of caregivers

Markerless Kinematics of Pediatric Manual Wheelchair Mobility

Pediatric manual wheelchair users face substantial risk of orthopaedic injury to the upper extremities, particularly the shoulders, during transition to wheelchair use and during growth and development. Propulsion strategy can influence mobility efficiency, activity participation, and quality of life. The current forefront of wheelchair biomechanics research includes translating findings from adult to pediatric populations, improving the quality and efficiency of care under constrained clinical funding, and understanding injury mechanisms and risk factors. Typically, clinicians evaluate wheelchair mobility using marker-based motion capture and instrumentation systems that are precise and accurate but also time-consuming, inconvenient, and expensive for repeated assessments. There is a substantial need for technology that evaluates and improves wheelchair mobility outside of the laboratory to provide better outcomes for wheelchair users, enhancing clinical data. Advancement in this area gives physical therapists better tools and the supporting research necessary to improve treatment efficacy, mobility, and quality of life in pediatric wheelchair users. This dissertation reports on research studies that evaluate the effect of physiotherapeutic training on manual wheelchair mobility. In particular, these studies (1) develop and characterize a novel markerless motion capture-musculoskeletal model systems interface for kinematic assessment of manual wheelchair propulsion biomechanics, (2) conduct a longitudinal investigation of pediatric manual wheelchair users undergoing intensive community-based therapy to determine predictors of kinematic response, and (3) evaluate propulsion pattern-dependent training efficacy and musculoskeletal behavior using visual biofeedback.Results of the research studies show that taking a systems approach to the kinematic interface produces an effective and reliable system for kinematic assessment and training of manual wheelchair propulsion. The studies also show that the therapeutic outcomes and orthopaedic injury risk of pediatric manual wheelchair users are significantly related to the propulsion pattern employed. Further, these subjects can change their propulsion pattern in response to therapy even in the absence of wheelchair-based training, and have pattern-dependent differences in joint kinematics, musculotendon excursion, and training response. Further clinical research in this area is suggested, with a focus on refining physiotherapeutic training strategies for pediatric manual wheelchair users to develop safer and more effective propulsion patterns