David Saucier
Papers
9
Total Citations
208
H-Index
7
About
David Saucier is a prominent researcher specializing in wearable sensor technology, human movement analysis, and fall detection. His work sits at the intersection of biomechanics, ergonomics, and soft robotics, with a particular focus on developing and validating stretch sensor systems for real-world clinical and occupational applications. Saucier is perhaps best known for leading the extensive "Closing the Wearable Gap" research series, a multi-part investigation that systematically validated soft robotic sensors (SRS) for capturing foot, ankle, and knee joint kinematics. This body of work — spanning sensor placement optimization, gait recognition using deep learning, slip and trip perturbation detection, and comparisons against gold-standard 3D motion capture systems — has established a rigorous methodological foundation for next-generation wearables. His 2020 review on wearable stretch sensors for health monitoring and fall detection has garnered 98 citations, reflecting broad community recognition of its impact. Beyond validation studies, Saucier has advanced practical wearable design, exploring smart knee braces, ankle motion capture devices, and the durability of textile versus electronic components over time. Collectively, his research addresses a critical gap between laboratory-grade motion analysis and accessible, everyday wearable solutions — work with meaningful implications for rehabilitation, elderly care, and workplace safety.
Research Focus
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Top Papers
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