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Physical therapy is often essential for complete recovery after injury. However, a significant population of patients fail to adhere to prescribed exercise regimens. Lack of motivation and inconsistent in-person visits to physical therapy are major contributing factors to suboptimal exercise adherence, slowing the recovery process. With the advancement of virtual reality (VR), researchers have developed remote virtual rehabilitation systems with sensors such as inertial measurement units. A functional garment with an integrated wearable sensor can also be used for real-time sensory feedback in VR-based therapeutic exercise and offers affordable remote rehabilitation to patients. Sensors integrated into wearable garments offer the potential for a quantitative range of motion measurements during VR rehabilitation. In this research, we developed and validated a carbon nanocomposite-coated knit fabric-based sensor worn on a compression sleeve that can be integrated with upper-extremity virtual rehabilitation systems. The sensor was created by coating a commercially available weft knitted fabric consisting of polyester, nylon, and elastane fibers. A thin carbon nanotube composite coating applied to the fibers makes the fabric electrically conductive and functions as a piezoresistive sensor. The nanocomposite sensor, which is soft to the touch and breathable, demonstrated high sensitivity to stretching deformations, with an average gauge factor of ~35 in the warp direction of the fabric sensor. Multiple tests are performed with a Kinarm end point robot to validate the sensor for repeatable response with a change in elbow joint angle. A task was also created in a VR environment and replicated by the Kinarm. The wearable sensor can measure the change in elbow angle with more than 90% accuracy while performing these tasks, and the sensor shows a proportional resistance change with varying joint angles while performing different exercises. The potential use of wearable sensors in at-home virtual therapy/exercise was demonstrated using a Meta Quest 2 VR system with a virtual exercise program to show the potential for at-home measurements. 

Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants.