Posts Tagged personalized rehabilitation
SofBoost, an artificial intelligence (AI) body recognition technology company, launches RehabBoost in partnership with healthcare entrepreneur Scott Verner.
Together, Miami-based SofBoost and Verner will work to develop a comprehensive library of rehabilitation tools for medical application, using machine learning and artificial intelligence technology, according to a media release.
“We are thrilled to be able to have the opportunity to disrupt the healthcare and rehabilitation space, as an extension of our broader mission to transform the way movements are learned and practiced,” says SofBoost CEO, Paul Jaure, in the release.
“I’ve closely followed SofBoost’s demonstrated success in the machine learning space, and believe our collaboration will make an important contribution to the healthcare industry,” states Verner, also currently the President and CEO of TRIVIDIA Health, as well as the Chairman of The Job Creators Network Foundation.
“This project aligns with my personal mission to create new business models and strategies that transform the way patient needs are fulfilled, and I’m excited to see our vision come to life.”
SofBoost’s patent-pending technology compares user body positions with predetermined methodologies, drawing on proprietary algorithms to produce personalized corrective analysis in real time. This first-of-its-kind product will offer instant and personalized rehabilitation support from anywhere, through an easy-to-use app interface, the release continues.
RehabBoost marks the company’s first expansion to other categories, and is in line with its growth strategy. SofBoost is actively seeking strategic partnerships to expand into various swing sports, fitness and other line extensions, according to the company.
[Source(s): SofBoost, Business Wire]
Abstract: Successful rehabilitation of stroke patients is strongly dependent on the engagement of patients. During a whole rehabilitation program, mundane rehabilitation exercises can easily become routine for patients, leading to boredom and as a result to ineffective functional recovery. This has been taken into consideration just by few rehabilitation systems. Engagement in rehabilitation can be decomposed into long term engagement (LTE) and short term engagement (STE) for the reason that their indicators and stimulation strategies are different.
This paper proposes various engaging strategies to optimize both LTE and STE of patients. Actually, proposes to combine the strategies concerning the whole rehabilitation program and the strategies to be applied during single rehabilitation exercises. Based on the proposed reasoning model, a cyber-physical computing based solution for a personalized rehabilitation system is proposed. Various cyberphysical characteristics, such as function augmentation, adaptive and learning control, are being implemented in the system in order to realize the various strategies of engagement