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Author: search.filters.author.Rodríguez Muñoz, Arturo JafetSubject: search.filters.subject.Ingenierías - Tesis

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    Feedback based dynamic environment system for robot-assisted gait training
    (2023)
    Rodríguez Muñoz, Arturo Jafet
    ;
    Del Valle Soto, Carolina
    ;
    González Sánchez, Javier
    Computer-aided rehabilitation therapy has evolved at a staggering rate since the combination of mechanics, electronics and software was first introduced over two decades ago. Multiple procedures have been augmented by the use of machines to standardize the therapy and also to have metrics. Therefore, patients who cannot walk properly due to functional gait disorder have experienced an evolution from the traditional approaches of using a walking bar to robot- assisted gait training as shown by Ricklin, Meyer-Heim, and Van Hedel (2018). The main differences reside in having a more controlled environment where the patient can suffer fewer accidents, promote muscle memory through proper movements, and measuring the effort done among other essential data. The Instituto Nacional de Estadística y Geografía (INEGI) creates multiple census and surveys to map the current situation of Mexico. According to INEGI (2021) and INEGI (2017) in Mexico in 2020 the total population is approximately 126 million people where 51.21% are women and 48.78% are men, and 16.53% of the population has an impairment or limitation. In other words, 20.8 million people have at least one of the seven most reported impairments which include walking or climbing stairs using their legs. Unfortunately, robot-assisted gait therapy requires advanced equipment which is expensive. Consequently, it is not intended to be a personal device but instead a medical device available only in hospitals and rehabilitation centers. As a result, the amount of people who can benefit from this technology depends on their location and the availability of each center, so a patient might have to wait several months before starting therapy. Hence, the importance of having high efficiency in each session so that each center can help more people. In this dissertation, we first wondered if having multiple wireless devices on a subject was possible without suffering from data loss due to interference. The challenge comes from the possibility of scalability, in other words, introducing a more abundant amount of devices to collaborate and gather more significant data as time passes. Therefore, we proposed a new predictive tool and a scheme to address the concerns previously stated. The tool and the scheme allow the simulation of environments where multiple consumer devices gather informa- tion from the user regardless of the polling speed or the connection technology employed in a Wireless Sensor Network (WSN). From the tests we ran, we found that it is possible to have an environment where many different sensors can feed a system without losing data, which means that using this simulator, a WSN can be planned for its initial phase or for scaling a previous state. Afterward, we worked on validating that it was possible to use an electroencephalogram with a virtual reality headset on patients that have a neurological condition. We retrieved the affective state using a dedicated framework designed for that purpose. During the study, we found that some of the interpreted values, such as meditation, had a shorter range and variation for the patients with more neurological affectations. Furthermore, introducing virtual reality showed an increase of 16% in the efficiency of the session, which was accomplished with a static scenario. Therefore, we predicted that a dynamic feedback-based scenario could provide better efficiency than a static scenario. Finally, we worked on the final study, creating an adaptive feedback system based on adjusting the background music, visual aid brightness, and overall illumination of a virtual reality environment. The proposed active feedback system successfully increased the active physical effort done by the users.