Publications by authors named "Andrea Pergolini"

Introduction: People with Parkinson's Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects' awareness of their gait disturbances.

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Individuals with Parkinson's disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects' activities.

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Accurate gait phase estimation algorithms can be used to synchronize the action of wearable robots to the volitional user movements in real time. Current-day gait phase estimation methods are designed mostly for rhythmic tasks and evaluated in highly controlled walking environments (namely, steady-state walking). Here, we implemented adaptive Dynamic Movement Primitives (aDMP) for continuous real-time phase estimation in the most common locomotion activities of daily living, which are level-ground walking, stair negotiation, and ramp negotiation.

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