Andy Harland

Papers

Total Citations

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About

Andy Harland’s research sits at the intersection of biomechanics, sports engineering, and human-machine interaction, with a focus on enhancing both athletic performance and assistive technology. His most cited work, “Gesture Recognition from Bio-signals Using Hybrid Deep Neural Networks” (2020, 9 citations), advances non-invasive prosthesis control by leveraging surface electromyogram (sEMG) signals, offering transradial amputees a promising path toward improved hand function and quality of life. In parallel, Harland investigates the nuanced relationship between product design and human perception, as seen in his study on the physical properties of footballs (2016, 9 citations), which challenges assumptions about consistency in sports equipment and informs better design standards. His earlier validation of the RoboGuide system (2012, 3 citations) demonstrates a commitment to bridging the gap between mechanical testing and real-world sporting movements, enabling more accurate emulation of human motion for athletic footwear development. Across these contributions, Harland’s work is characterized by a practical, interdisciplinary approach—combining deep learning, robotics, and perceptual psychology—to solve tangible problems in rehabilitation and sport. His research not only advances technical knowledge but also directly impacts user experience and quality of life, making him a notable figure in applied biomechanics and sports engineering.