SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve the quality of life for individuals suffering arm and hand impairments from stroke, through a medical device for in-home telerehabilitation that is easy to use, engaging, and affordable. Every year, ~800,000 people have a stroke in the United States, and about 65% suffer long-term upper extremity impairments. Sustained and extensive therapy is often needed to improve and regain the functions of the hand. Many individuals post-stroke have difficulty accessing rehabilitation due to transportation, health and mobility issues. This device can potentially reduce long-term disability in the United States and alleviate the financial burden on both individuals and society. By integrating the product into physicians’ and therapists’ workflow, it expands the patient pool that can access rehabilitation, making it more time- and labor-efficient. It will contribute to the field of telehealth and promote decentralized health care. This Small Business Technology Transfer Research (STTR) Phase I project will develop a telerehabilitation system that promotes neuroplasticity for hand and arm motor recovery, to be used in the homes of individuals with neurological disorders. The objective of this proposal is to create a beta prototype that provides personalized intense hand and arm rehabilitation via engaging motion based games. The proposed project includes: 1. enhancing the adaptive algorithms to dynamically adjust the games to an individual’s needs; 2. developing a Rehabilitation Assessment Protocol by optimizing the extensive motion tracking capabilities. This provides important information to clinicians for making rehabilitation plan adjustments, improving the interventions, and reaching goals faster; 3. developing a HIPAA compliant data platform for the recommendation system to facilitate encrypted data streaming and therapist-patient interaction in real-time or offline. This includes the development of a therapist portal that generates automated progress reports, allowing them to treat more patients efficiently; 4. conducting a usability study to evaluate the capabilities of the system from the perspective of the prescribing therapist. These capabilities of virtual rehabilitation, remote clinician supervision, and progress tracking offer a cost-effective way to improve patient outcomes as well as the fiscal viability and profitability for clinicians. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to potentially improve the quality of life for individuals suffering arm and hand impairments from stroke, through a medical device for telerehabilitation. Each year, ~800,000 people have a stroke in the United States, and about 65% of them suffer long-term upper extremity impairments. Due to many barriers such as cost, transportation, and time, many individuals do not obtain enough therapy for recovery. The telerehabilitation approach may reduce some of these barriers, allowing therapists and their patients to have meaningful remote sessions. For therapists, this may improve fiscal outcomes by automating the flow of reviewing patient progress, adjusting their rehabilitation treatments, and billing for services. This project will advance the development of a personalized telerehabilitation system, specifically for hand and arm motor recovery, for individuals suffering from a stroke. New exergames designed for rehabilitation of the fingers, hand, and arm will be developed and added to the current library of games. Machine learning will be added to the system to create a versatile, engaging, and customizable solution. This novel approach to rehabilitation will personalize treatments that may be more effective by addressing individual user needs with predictive analytics. Machine learning will drive the recommendation system to synchronize the rehabilitation plan with the patient recovery trajectory. This synchronization will help the therapist provide personalized therapeutic exercises and possibly increase their patients? recovery outcomes. The games and machine learning algorithms will be evaluated with clinicians and individuals with stroke. The final step will be to test the feasibility of the system in a comprehensive stroke center. These capabilities of personalized virtual rehabilitation, remote clinician supervision, and progress tracking may offer a cost-effective way to improve patient outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.