Gait is a crucial skill for efficient interactions with the external environment, thus being central in human behavior. Balance is another important skill, allowing successful gait and avoidance of falling during everyday actions. At CATR, we aim at characterizing both processes via electromiography (EMG), electroencephalography (EEG), kinematics, and autonomic responses such as heart rate, all collected simultaneously, both in real and virtual environments. In addition, we also use these measures to characterize impaired gait and balance mechanisms in pathological populations such as people with Parkinson or with multiple sclerosis as well as in the normal aging population. Our aim is both to identify neural markers defining the presence of pathological conditions and the development of targeted rehabilitative procedures.

Our works with patients with Parkinson, for instance, suggest that right before a freezing of gait (FoG) episode, namely one of the most debilitating symptoms of Parkinson's disease when the gait suddenly stops and patients cannot move their feet forward for several seconds, there might be an enhanced synchronization across the signals coming from different systems (e.g., between EEG and EMG signals). We are further characterizing this phenomenon to unravel the extent to which across-networks synchronization might be a neural marker of FoG episodes as well as to develop rehabilitation tools which use this information to improve/avoid FoG's occurrences.

Finally, we also explore the interactions between gait/balance processes and sensory systems (e.g., vision) to investigate the properties of sensory weighting during different walking tasks. We showed for instance that vision can trick our body into perceiving uphill or downhill walking paths and modify gait properties accordingly.

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