Lars Holmstrom

APDM (United States), Portland State University

Papers

Total Citations

H-Index

About

Lars Holmstrom’s research lies at the intersection of wearable sensing, biomechanics, and autonomous robotics, with a focus on translating human and robotic movement into actionable data. His most impactful work, “Upper Limb Joint Angle Tracking with Inertial Sensors” (2011), has garnered 54 citations and established a foundational method for using wearable inertial systems to monitor human motion outside laboratory settings. This contribution is critical for continuous assessment of physical activity and functional ability, enabling real-world rehabilitation and performance analysis. In earlier work, “Experience Based Surface Discernment by a Quadruped Robot” (2007), Holmstrom explored autonomous terrain classification using an AIBO robotic dog, employing genetic algorithms to design gaits that adapt to different surfaces and inclines. Though less cited, this study demonstrates his versatility in applying machine learning to robotic locomotion. Holmstrom’s combined achievements highlight a career dedicated to bridging sensor technology with practical applications in human health and robotics, offering tools that empower both clinicians and engineers to capture and interpret movement in natural environments.