The broader impact/commercial potential of this PFI project is to benefit millions of post-stroke patients and other disabled people who need effective body rehabilitation, by developing an intelligent rehabilitation robot with adaptive upper trunk support and hologram-based virtual reality training scenes. The existing commercial rehabilitation products cannot efficiently support the upper torso of the patients with serious trunk disability, and do not have intelligent, fine-resolution robot control to train two legs with different impairment levels. An online rehabilitation training without frequent in-person visits to the office of the physical therapist (PT) would reduce the medical cost. This commercialization-oriented project will address those market needs. It will also develop and implement a new customer channel model called "8C + 5F", consisting of 8 communication channels with different types of customers and offering 5 technical facets of this platform. To enhance the academia-industry partnership, the team proposes an effective model called nexus organization (NEO), to achieve transdisciplinary, conflict-free collaborations between the university and the industry. To train future leaders in innovation and entrepreneurship, the team proposes a new education model called Innovation and Marketing-oriented Renaissance Foundry (IMRF) to prepare PhD students and postdoctoral researchers with strong entrepreneurship and innovation skills.
The proposed project aims to build a Holographic intelligent Rehabilitation robotic technology to train post-stroke patients. It has 3 marketing-oriented new designs: i) (Upper trunk exoskeleton with 3D linkage and elastic impedance control): A compact, light-weight and economic upper trunk exoskeleton will be developed for a robot system to improve the upper trunk stability control during rehabilitation. ii) (Intelligent platform control in each phase of intra-gait cycle to handle two-leg impairment asymmetry): Current treadmill control simply changes the speed/force of the treadmill in each gait cycle based on the user's speed or measured center of pressure. However, many patients have different impairment levels between his/her two legs. This PFI will use low-cost thermal/acoustic sensors with deep-learning-based 3D leg trajectory analysis to achieve fine-granularity 4-phase rehabilitation robot control. iii) (Self-engaging mixed reality rehabilitation environment based on holographic telemedicine): The team will extend their previously developed virtual reality (VR)-based design to a hologram-based mixed reality (MR) platform with the advanced telemedicine functions for virtual patient-to-PT or patient-to-patient co-rehab training.
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.
