PIKL (Paddle Integrated Kemper Logic); Assistance or Obstacle? Rethinking Effectiveness and Access in Gait Rehabilitation Technologies

The general problem connecting both my technical and STS research is the limited accessibility and effectiveness of smart training technologies designed to improve user performance. Devices like smart pickleball paddles or gait training systems often claim to deliver actionable feedback using embedded technologies such as piezoelectric sensors, or FSR sensors. However, many of these tools lack accuracy or usability, especially for nonelite users, which leads to doubt about their value. This matters because whether the goal is to train for competitive play, improve recreational performance, or recover physical ability as in the case of gait rehabilitation, users depend on reliable feedback to make meaningful progress. In response, the technical project developed a smart pickleball paddle that delivers accurate real time data on swing speed, impact force, and stroke classification with a user friendly interface to support self-improvement. My STS research analyzed robotic assisted gait training devices through Actor Network Theory, showing how these tools could be more inclusive by recognizing the different needs and challenges of individual users. Together, both projects address the broader challenge of making smart performance enhancing technologies not only technically effective but also socially inclusive and useful in real life contexts.
In the technical project, we developed a smart pickleball paddle that integrates piezoelectric sensors, an IMU sensor, a microcontroller, and a custom 3D printed handle. My primary focus was detecting the impact location on the paddle surface. I investigated both FSR and piezoelectric sensors by conducting experiments under varying force conditions and analyzing the output data. The results showed that while FSR sensors respond well to sustained pressure, piezoelectric sensors offer faster response times, making them more suitable for detecting quick impacts during play. This informed our decision to use piezoelectric sensors for accurate and real time shot detection.
The STS research examined current robotic assisted gait training devices, highlighting both their technical limitations and the social barriers that restrict access for many patients. Case studies and statistical data revealed how effectiveness is often evaluated without considering patient diversity or real world constraints. The study emphasized that sociotechnical problems extend beyond engineering challenges, involving policy, affordability, and overlooked stakeholders. By analyzing a government supported rehabilitation program as an example, the research showed how multiple human and nonhuman actors are interconnected in the healthcare network, influencing the accessibility and impact of rehabilitation technologies.
Both the technical and STS projects contributed meaningfully to addressing the broader problem of improving the accessibility and effectiveness of smart performance enhancing technologies. The technical project successfully implemented features like impact location detection using piezoelectric sensors and a custom amplification circuit. Despite time constraints, alternative approaches were developed to maintain key functionality. Future work should explore more precise sensors, improved calibration techniques, and a more efficient embedded system to enhance accuracy and reliability. The STS research contributed by identifying systemic barriers to robotic assisted gait training accessibility in Virginia, including facility shortages and high costs. It emphasized the need for policy level solutions, such as increased public funding and insurance coverage, to support broader access. It also addressed how current evaluations of effectiveness often overlook the therapist's role and the patient's unique needs. Future researchers should conduct long term studies that measure the effectiveness of these devices when combined with human input and explore how design improvements can better support diverse patient populations. Together, both projects underscore the need for inclusive design and policy strategies to ensure smart technologies truly serve the users they aim to support.
I would like to sincerely thank my capstone teammates—Kemper Siever, Oscar Lauth, and Wilmot Westreicher—for their dedication and collaboration throughout the technical project. Oscar and Wilmot led the software development and created a user friendly interface that brought our design to life, while Kemper and I worked closely on the hardware implementation to realize the paddle’s key features. For the STS project, I am especially grateful to my STS professors, Rider Foley and Caitlin Wylie, for their guidance and support. Their thoughtful feedback and encouragement were instrumental in shaping my research.

School of Engineering and Applied Science

Bachelor of Science in Electrical Engineering

Technical Advisor: Adam Barnes

STS Advisor: Caitlin Wylie

Technical Team Members: Jiseoung Kim, Kemper Siever, Oscar Lauth, Wilmot Westreicher