Christopher Caulcrick

Imperial College London, UK Dementia Research Institute

Papers

Total Citations

H-Index

About

Christopher Caulcrick is a leading researcher at the intersection of biomechatronics and human-robot interaction, with a primary focus on advancing lower limb exoskeleton (LLE) technology. His work centers on solving the critical challenge of seamless human-robot synergy, particularly through the fusion of biosignals and sensor data. Caulcrick’s most impactful contribution is his pioneering work on human joint torque modelling, where he demonstrated how combining electromyography (EMG) and mechanomyography (MMG) can decode user intent during exoskeleton interaction—a paper that has garnered 58 citations and is foundational for assist-as-needed control strategies. He has also innovated in gait analysis, introducing a method using feedforward neural networks to estimate gait cycle progress from unilateral inertial and muscle activity sensors, enabling more natural, unilateral monitoring. Additionally, his application of Model Predictive Control for human-centred robotic assistance addresses the critical public health issue of mobility loss from neural trauma. Through these contributions, Caulcrick is driving the development of intelligent, adaptive exoskeletons that can fluidly cooperate with human effort, promising transformative advances in rehabilitation and assistive mobility.