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About

Elena Ceseracciu is a leading researcher at the intersection of rehabilitation robotics, human motion analysis, and wearable technology. Her work focuses on creating intelligent, symbiotic human-robot interfaces that can anticipate and respond to a user’s movement intentions. In her highly cited 2010 paper (38 citations), she pioneered the use of Support Vector Machine (SVM) classifiers to decode locomotion modes from surface electromyography (EMG), enabling more responsive control of rehabilitation exoskeletons. Ceseracciu has also made foundational contributions to robotics middleware, co-authoring a seminal 2014 paper (32 citations) on YARP, a widely used software framework for humanoid robots. Her expertise extends to wearable sensing, where she validated a model-based inverse kinematics approach using inertial measurement units (IMUs) to estimate joint angles with high accuracy (29 citations). As a key contributor to the BioMot Project, she advanced the concept of symbiotic wearable exoskeletons that seamlessly integrate EMG-informed biomechanical models. With over 125 total citations across her most-cited works, Ceseracciu’s research is driving the next generation of adaptive, intuitive assistive devices for rehabilitation and daily living.