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Pedro Miguel Ferreira Mouta Instrumentation of a Cane to Detect

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Pedro Miguel Ferreira Mouta Instrumentation of a Cane to Detect Mês de Ano Pedro Miguel Ferreira Mouta Instrumentation of a cane to detect and prevent falls Dissertação de Mestrado Mestrado Integrado em Engenharia Biomédica Ramo de Eletrónica Médica Trabalho efetuado sob a orientação de: Doutora Cristina P. Santos, Universidade do Minho Janeiro de 2020 DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Licença concedida aos utilizadores deste trabalho https://creativecommons.org/licenses/by-nc-nd/4.0/ ii ACKNOWLEDGMENTS I want to thank my mentor, professor Cristina Santos for leading the work in the right direction and proving valuable knowledge and experience each step of the way. Moreover, I would like to mention immense gratitude to the doctoral student Nuno Ferrete Ribeiro, who was outstanding, not only at the level of scientific guidance but also at supporting, advising, and being always present throughout this year. Thank you to my laboratory colleagues for keeping the excellent energy present and the camaraderie during this year. Besides, I want to thank the suggestions and availability provided. I also want to thank Inês Carvalho for being present in all the important moments of my life and giving the strength to never give up when the obstacles appear. I am genuinely proud of you. I want to thank and acknowledge my friends for always giving the motivation to keep going forward and recommendations in the various projects of my life. Finally, I want to thank my parents and siblings, in particular to my mother and father over the unconditional support over these last few years. Besides to provide me with the best conditions for my education, they were also an emotional pillar, always indicating the best way forward. Thank you for all your strength and encouragement. I have exceptional pride in both of you. Thank you very much everyone Pedro Miguel Ferreira Mouta iii STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. iv RESUMO O número de quedas tornou-se uma das principais causas de lesões e mortes na comunidade geriátrica. Como resultado, o custo do tratamento das lesões também aumenta. Portanto, é necessário o desenvolvimento de estratégias relacionadas com quedas e que exibam capacidade de monitorização em tempo real sem colocar restrições ao usuário. Devido às suas vantagens, os acessórios do dia-a-dia podem ser uma solução para incorporar sistemas relacionados com quedas, sendo que as bengalas não são exceção. Além disso, a avaliação da marcha pode ser capaz de aprimorar a capacidade de uso de uma bengala para usuários mais idosos. Desta forma, é crucial o desenvolvimento de estratégias que reconheçam estados de queda, do passo anterior a uma queda e dos diferentes eventos da marcha de uma bengala. Esta dissertação tem como objetivo desenvolver estratégias capazes de identificar as situações anteriormente descritas com base num sistema incorporado numa bengala que coleta informações inerciais e de força, a Assistive Smart Cane (ASCane). A estratégia referente à deteção de quedas consistiu em testar os dados adquiridos através da ASCane com três algoritmos de deteção de quedas (baseados em thresholds fixos), com um algoritmo de thresholds dinâmicos e diferentes classificadores de machine learning encontrados na literatura. Estes métodos foram testados e modificados para dar conta do uso de informação adquirida através de uma bengala. O melhor desempenho alcançado em termos de sensibilidade e especificidade foi de 96,90% e 98,98%, respetivamente. Relativamente à deteção dos diferentes eventos da ASCane em situações controladas e da vida real, um detetor de eventos da marcha foi e comparado com um sistema de ground truth. Além disso, foi também realizado um estudo de machine learning envolvendo oito métodos de seleção de features e nove classificadores diferentes de machine learning. Os resultados mostraram que a precisão dos classificadores foi bastante aceitável e apresentou, como melhores resultados, 98,32% de precisão para situações controladas e 94.82% para situações do dia-a-dia. No que concerne à deteção de passos pré-queda, a mesma abordagem de machine learning foi realizada. Os modelos foram precisos (precisão = 98,15%) e com a implementação de um filtro de pós-processamento, todas as deteções de falsos positivos foram eliminadas e uma queda foi passível de ser detetada 1,019s antes do final do respetivo passo de pré-queda e 2.009s antes do impacto. PALAVRAS-CHAVE: QUEDAS, PASSOS PRÉ-QUEDA, EVENTOS DE UMA BENGALA, ASSISTIVE SMART CANE v ABSTRACT The number of falls is growing as the main cause of injuries and deaths in the geriatric community. As a result, the cost of treating the injuries associated with falls is also increasing. Thus, the development of fall-related strategies with the capability of real-time monitoring without user restriction is imperative. Due to their advantages, daily life accessories can be a solution to embed fall-related systems, and canes are no exception. Moreover, gait assessment might be capable of enhancing the capability of cane usage for older cane users. Therefore, reducing, even more, the possibility of possible falls amongst them. Summing up, it is crucial the development of strategies that recognize states of fall, the step before a fall (pre-fall step) and the different cane events continuously throughout a stride. This thesis aims to develop strategies capable of identifying these situations based on a cane system that collects both inertial and force information, the Assistive Smart Cane (ASCane). The strategy regarding the detection of falls consisted of testing the data acquired with the ASCane with three different fixed multi-threshold fall detection algorithms, one dynamic multi-threshold and machine learning methods from the literature. They were tested and modified to account the use of a cane. The best performance resulted in a sensitivity and specificity of 96.90% and 98.98%, respectively. For the detection of the different cane events in controlled and real-life situations, a state-of-the-art finite-state-machine gait event detector was modified to account the use of a cane and benchmarked against a ground truth system. Moreover, a machine learning study was completed involving eight feature selection methods and nine different machine learning classifiers. Results have shown that the accuracy of the classifiers was quite acceptable and presented the best results with 98.32% of overall accuracy for controlled situations and 94.82% in daily-life situations. Regarding pre-fall step detection, the same machine learning approach was accomplished. The models were very accurate (Accuracy = 98.15%) and with the implementation of an online post-processing filter, all the false positive detections were eliminated, and a fall was able to be detected 1.019s before the end of the corresponding pre-fall step and 2.009s before impact. KEYWORDS: FALLS, PRE-FALL STEPS, CANE EVENTS, ASSISTIVE SMART CANE vi CONTENTS 1. Introduction ................................................................................................................................ 1 1.1 Motivation ........................................................................................................................... 1 1.2 Problem statement and scope ............................................................................................. 2 1.3 Goals and research questions .............................................................................................. 3 1.4 Contribution to knowledge ................................................................................................... 4 1.5 Publications ........................................................................................................................ 5 1.6 Thesis outline ...................................................................................................................... 5 2. Falls and related technological Approaches: State-of-the-art .......................................................... 6 2.1 Introduction......................................................................................................................... 6 2.2 Different Stages of a Fall ...................................................................................................... 7 2.3 Classification and type of falls .............................................................................................. 8 2.4 Risk factors ......................................................................................................................... 9 2.4.1 Gender, Ethnicity and Age .......................................................................................... 10 2.4.2 Psychological Status .................................................................................................
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