Wearable Robotic Systems Laboratory


The mission of the Wearable Robotic Systems Laboratory is to develop robotic technology that aims to improve recovery of lost motor functions in neurological populations through robot-assisted training. To achieve this goal, we apply principles of motor learning and use mechanical and bio-mechanical models to design robotic devices and human-in-the-loop control strategies that better support those principles, eventually validating prototypes and controllers through human experiments. The ultimate goal of this research is to increase frequency and intensity of rehabilitation exercises while reducing the physical burden on therapists.

Our research also seeks to quantify human movements through unobtrusive, custom-designed wearable sensors that can be used outside the constrained laboratory environment to accurately measure real-life tasks, such as walking down a hallway, climbing and descending stairs or running in the track. By applying statistical inference tools and machine learning, we aim to find associations between subtle changes in kinematic and kinetic patterns and specific medical conditions that affect the motor function, as well as precursors of orthopedic injuries. Identifying such correlations is critical for developing more accessible and reliable tools for early diagnosis, injury prevention, and human performance monitoring.

Close collaborations with clinical researchers play an important role in our research – from the definition of the unmet clinical need to human testing on clinical populations.

In addition, our group is also passionate about analysis, design, and control of dynamic systems. In particular, we study cable-driven parallel robots (CDPR), a special class of parallel mechanisms where rigid links are replaced by cables or ropes.

Research Areas

  • Lower-extremity powered exoskeletons for assistance and rehabilitation

  • Wearable sensors for motion analysis, quantitative assessment of movement disorders, and injury prevention

  • Cable-driven parallel robots


Damiano Zanotto