Longitudinal assessment of deep brain stimulation effects in Parkinson's disease through instrumented gait analysis

Parkinson’s disease(PD) is a progressive neurodegenerative disorder characterized by a wide spectrum of motor and non-motor symptoms. Among the advanced therapeutic options for patients with motor complications not adequately controlled by pharmacological therapy, Deep Brain Stimulation(DBS) has become an established treatment. Although its clinical benefits are widely recognized, the long-term effects of DBS on motor performance, particularly gait, are still not fully understood when assessed through objective and quantitative tools. Clinical evaluation of PD is traditionally based on rating scales which, despite being standardized, partly rely on subjective clinical judgment. In recent years, wearable inertial sensors have been increasingly introduced as complementary tools to provide objective and reproducible measurements of motor performance. These technologies allow a detailed quantification of gait parameters and enable longitudinal monitoring beyond conventional clinical observation. Within this context, the aim of this thesis was to investigate the short- and long-term effects of DBS on gait performance using wearable Inertial Measurement Unit(IMU) sensors. Data were collected from patients undergoing DBS surgery at three time points: pre-surgery, six months and three years after surgery. Gait analysis was performed during structured motor tasks under different medication and stimulation conditions, considering both single-task and dual-task walking. Particular attention was given to differences between subjects with and without Freezing of Gait. Gait parameters were extracted through validated processing algorithms, and methodological adjustments were introduced to ensure reliable signal processing. Through longitudinal and condition-based comparisons, this work contributes to a better understanding of how DBS influences gait performance over time and highlights the potential of wearable IMU technology as an objective tool supporting clinical assessment.