Contribución al análisis del movimiento humano aplicado a la identificación de posturas y bloqueos de la marcha en pacientes con Parkinson

Resum

The following dissertation presents the contributions of the author in the field of human movement analysis and, specifically, in Parkinson's disease. Recent technologies have allowed developing reduced inertial sensors capable of monitoring human movement. This, along with the reduced prices of these inertial sensors, the so-called inertial measurement units, which consists in small devices capable to measure movement by means of inertial sensors, have widely spread. Inertial measurement units have been employed among others, in fields such as medicine, sports, automotive and gaming. In the first part of the present thesis, a wearable long-term monitoring inertial measurement unit is presented as the first main contribution in human movement analysis. The unit is capable of acquiring data and provides the possibility of implementing artificial intelligence-based classifiers in real time. A specific hardware and firmware has been developed in order to implement both operations. This tool has been validated in different European projects and studies carried out in the Technical Research Centre for Dependency Care and Autonomous Living of the Universitat Politècnica de Catalunya (CETpD-UPC). The second part of the thesis addresses the analysis of human posture based on accelerometry measurements. To this end, data acquired from the inertial system described at the first part of the thesis have been used. Two methodologies are presented that have been validated on healthy people and patients with Parkinson's disease. The algorithms developed are focused on the detection of positions with a single inertial system located at the waist thereby achieving an enhanced comfort and acceptance by the users. A key contribution is the methodology provided to detect postural transitions, which consist in the movement performed to achieve a position from another one. The algorithm is based on support vector machines applied to the inertial data coming from a single measurement unit. Basic activity recognition is performed recognizing static postures such as sitting, standing, or lying with a hierarchical classification system. Moreover, dynamic postures such as walking and different postural transitions are also recognized. Finally, the posture detection methodologies are employed to enhance the identification of one of the most annoying symptoms of Parkinson's disease, the so-called Freezing of Gait. This contribution relies on the posture algorithm which has been validated in Parkinson's disease patients. Furthermore, it is shown how the introduction of the posture detection improves the evaluation values of the FOG algorithms