Motion Capture System for Finger Movement Measurement in Parkinson Disease

Parkinson’s disease (PD) is a chronic neurodegenerative disorder that affects almost 1% of the population in the age group above 60 years. The key symptom in PD is the restriction of mobility. The progress of PD is typically documented using the Unified Parkinson’s Disease Rating Scale (UPDRS), which includes a finger-tapping test. We created a measurement tool and a methodology for the objective measurement of the finger-tapping test. We built a contactless three-dimensional (3D) capture system using two cameras and light-passive (wireless) reflexive markers. We proposed and implemented an algorithm for extracting, matching, and tracing markers. The system provides the 3D position of spherical or hemispherical markers in real time. The system’s functionality was verified with the commercial motion capture system OptiTrack. Our motion capture system is easy to use, saves space, is transportable, and needs only a personal computer for data processing—the ideal solution for an outpatient clinic. Its features were successfully tested on 22 patients with PD and 22 healthy control subjects

Prediction of Lower Extremity Movement by Cyclograms

Human gait is nowadays undergoing extensive analysis. Predictions of leg movements can be used for orthosis and prosthesis programming, and also for rehabilitation. Our work focuses on predicting human gait with the use of angle-angle diagrams, also called cyclograms. In conjunction with artificial intelligence, cyclograms offer a wide area of medical applications. We have identified cyclogram characteristics such as the slope and the area of the cyclogram for a neural network learning algorithm. Neural networks learned by cyclograms offer wide applications in prosthesis control systems

Feedback-error learning control for powered assistive devices

Active orthoses (AOs) are becoming relevant for user-oriented training in gait rehabilitation. This implies efficient responses of AO's low-level controllers with short time modeling for medical applications. This thesis investigates, in an innovative way, the performance of Feedback-Error Learning (FEL) control to time-effectively adapt the AOs' responses to user-oriented trajectories and changes in the dynamics due to the interaction with the user. FEL control comprises a feedback PID controller and a neural network feedforward controller to promptly learn the inverse dynamics of two AOs. It was carried out experiments with able-bodied subjects walking on a treadmill and considering external disturbances to user-AO interaction. Results showed that the FEL control effectively tracked the user-oriented trajectory with position errors between 5% to 7%, and with a mean delay lower than 25 ms. Compared to a single PID control, the FEL control decreased by 16.5% and 90.7% the position error and delay, respectively. Moreover, the feedforward controller was able to learn the inverse dynamics of the two AOs and adapt to variations in the user-oriented trajectories, such as speed and angular range, while the feedback controller compensated for random disturbances. FEL demonstrated to be an efficient low-level controller for controlling AOs during gait rehabilitation.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, and part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs - Controlo Inteligente de um Sistema Ortótico Ativo e Autónomo - under Grant NORTE-01-0145-FEDER-030386, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Prediction of Lower Extremity Movement by Cyclograms

Human gait is nowadays undergoing extensive analysis. Predictions of leg movements can be used for orthosis and prosthesis programming, and also for rehabilitation. Our work focuses on predicting human gait with the use of angle-angle diagrams, also called cyclograms. In conjunction with artificial intelligence, cyclograms offer a wide area of medical applications. We have identified cyclogram characteristics such as the slope and the area of the cyclogram for a neural network learning algorithm. Neural networks learned by cyclograms offer wide applications in prosthesis control systems

HiBSO Hip Exoskeleton: Toward a Wearable and Autonomous Design

HiBSO is an active orthosis designed to assist the hip flexion-extension of the elderly. A fully autonomous system with untethered power electronics and energy supply is now available. Going beyond the restricted walking conditions of a treadmill unveils many opportunities for the understanding of human-robot interaction. Previous works have presented the mechanical design optimized for high transparency and light weight, while dedicated kinematics allow high torque for sit-to-stand transition and high speed for level walking. The control strategies are currently in the evaluation process. In this document, the recent improvements to the device will be described, from the mechanical design to the control electronics. Some specific aspects such as the remote communication for the controller are emphasized. The assessment of the power autonomy is addressed with two sessions of walking in different conditions, and revealed a maximum operating time of more than 80 min. In this context, the controller is based on adaptive oscillators for the gait detection and is combined with a 40% torque assistance based on biomechanics from the literature