SBIR Phase I: A Novel Human Machine Interface for Assistive Robots

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will benefit the 30 million disabled survivors of stroke, spinal cord injury, and traumatic brain injury around the globe. Making assistive robots more affordable and easy to use is important for this market, projected to grow to $1.2 billion in 2024. The proposed wearable technology will enable effective communication and control of robots designed to assist patients with activities of daily living, which will help patients improve quality of life, lower costs for long-term care, and preserve physical and mental health with increased activities. The data may be used to monitor rehabilitation and deterioration progress, which could encourage patients to exercise more frequently and enable more personalized treatment.This Small Business Innovation Research (SBIR) Phase I project will explore the feasibility of commercializing a lightweight and compact optical sensor band for detecting commands from disabled users and controlling assistive robots. Most commercially available human-robot interface options are not practical or reliable for chronic hemiparetic patients due to the diverse and evolving patient conditions. The research objective involves designing an optical sensor array to capture muscle activities and developing an adaptive muscle synergy-based classification algorithm to convert optical data to user command. The project will optimize the configuration for near-infrared emitters and phototransistors to efficiently monitor muscle activities on the forearm. Advanced signal processing and machine learning techniques will be used to filter the data collected from multimodal sensor inputs to ensure robust performance regardless of user conditions.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.