Navigation system using passive collaborative control adapted to user profile for a rollator device - Ballesteros-Gómez, Joaquín (UMA Editorial, 2017)
Rollators provide autonomy to persons with mobility impairments. These platforms can be used while people perform their Activities of Daily Living in order to provide support and/or balance. Also, they can be used during the rehabilitation process to strengthen the lower limbs or to provide balance before users can progress to canes or crutches. Rollators have a limited set of personalization options, but they are usually related to the users' body size. Hence, people who need extra typically have to choose a wheelchair instead. This transition to a wheelchair limits users' movements and it increases their disuse syndrome because they do not exercise their lower limbs. Hence, it is a priority to extent the use of rollator platforms as much as possible by adapting help to people who can not use a conventional rollator on their own. Technological enhancements can be added to rollator to expand their use to a larger population. For example, force sensors on handlebars provide information about users' weight bearing. This information can be used during rehabilitation to control their partial weight-bearing. Encoders on wheels may also provide useful information about the walking speed, which is a well know estimator of fall risk. In addition to monitorization, motors can be attached to the wheels for assistance, e.g. to reduce effort while ascending slopes. This thesis focuses on creating a navigation system for a robotized rollator, which includes weight bearing sensors, encoders and wheel motors. The navigation system relies on passive collaborative control to continuously combine user and system commands in a seamless way. The main contribution of this work is adaptation to the user's needs through continuous, transparent monitorization and profile estimation.
In order to achieve this goal, research in different areas has been necessary. First, a methodology to provide human-like platform motion in reactive navigation algorithms has been proposed to improve user acceptance of help. Then, work has focused on gait analysis and user's condition estimation using only onboard sensors. In addition, a new methodology to evaluate fall risk using only onboard sensors while users walk has been proposed to balance the contribution of user and robot to control. All proposed subsystems have been validated with a set of volunteers at two rehabilitation hospitals: Fondazione Santa Lucia (Rome) and Hospital Regional Universitario (Malaga). Volunteers presented a wide variety of physical and cognitive disabilities. Tests with healthy volunteers have been discarded from the beginning to avoid a sampling bias error. Obtained results have shown that the proposed system can be used for: i) reactively generating human-like trajectories that outperforms all other tested algorithms in terms of likeness to human paths and success rate; ii) monitoring gait and user's condition while users walk using only on-board sensors; and iii) evaluating fall risk without wearable sensors nor ambient sensors. This thesis open a number of open research lines: i) user condition estimation can be extended to another medical scales; ii) the method to reactively generate human-like-trajectories can be extended to add deliberative human-adapted-path-planning; and iii) the fall risk estimator can be extended to a fall risk predictor.
Keywords: Robótica - Tesis doctorales; Análisis de datos