Content validity of the WorkHab functional capacity evaluation

Functional Capacity Evaluation Research: Report from the Fourth International Functional Capacity Evaluation Research Meeting

Purpose To summarize progress of functional capacity evaluation (FCE) research based on the proceedings of the Fourth International FCE Research Conference held in Switzerland on September 21 and 22, 2018. Methods A scientific committee identified key issues in FCE research and developed the program including key note presentations, a call for abstracts, and round table discussions over 2 days. Highlights of the presentations and discussions are summarized in this article. Results Seventy-nine participants from 11 countries attended the conference where 10 keynote lectures and 21 abstracts were presented. There was also an open discussion regarding the need for an International FCE clinical practice guideline (CPG), methods for developing such a guideline, and practical next steps. Full program details and abstracts from this Fourth International FCE Research Conference are available from https://www.sar-reha.ch/interessengemeinschaften/ig-ergonomie.html . Conclusions Researchers and clinicians continue to increase the body of knowledge in the FCE field. A major finding of this conference is the diversity across the different FCE protocols and research groups as well as of the different uses of FCE across cultural and social economic systems. Next steps will include exploring the development of an international, interdisciplinary, evidence-based FCE clinical practice guideline by a committee formed at the conference.

Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation

Introduction Occupational rehabilitation often involves functional capacity evaluations (FCE) that use simulated work tasks to assess work ability. Currently, there exists no single, streamlined solution to simulate all or a large number of standard work tasks. Such a system would improve FCE and functional rehabilitation through simulating reaching maneuvers and more dexterous functional tasks that are typical of workplace activities. This paper reviews efforts to develop robotic FCE solutions that incorporate machine learning algorithms. Methods We reviewed the literature regarding rehabilitation robotics, with an emphasis on novel techniques incorporating robotics and machine learning into FCE. Results Rehabilitation robotics aims to improve the assessment and rehabilitation of injured workers by providing methods for easily simulating workplace tasks using intelligent robotic systems. Machine learning-based approaches combine the benefits of robotic systems with the expertise and experience of human therapists. These innovations have the potential to improve the quantification of function as well as learn the haptic interactions provided by therapists to assist patients during assessment and rehabilitation. This is done by allowing a robot to learn based on a therapist's motions ("demonstrations") what the desired workplace activity ("task") is and how to recreate it for a worker with an injury ("patient"). Through Telerehabilitation and internet connectivity, these robotic assessment techniques can be used over a distance to reach rural and remote locations. Conclusions While the research is in the early stages, robotics with integrated machine learning algorithms have great potential for improving traditional FCE practice.