[pt] Dores no pé e joelho estão relacionadas com patologias ortopédicas e lesões nos membros inferiores. Desde a corrida de rua até o treinamento funcional CrossFit, estas dores e lesões estão correlacionadas com a distribuição iregular da pressão plantar e o posicionamento inadequado do joelho durante a prática física de longo prazo, e podem levar a lesões ortopédicas graves se o padrão de movimento não for corrigido. Portanto, o monitoramento da distribuição da pressão plantar do pé e das características espaciais e temporais das irregularidades no posicionamento dos pés e joelhos são de extrema importância para a prevenção de lesões. Este trabalho propõe uma plataforma, composta de uma rede de sensores vestíveis e um classificador de Reconhecimento de Atividade Humana (HAR), para fornecer feedback em tempo real de exercícios funcionais, visando auxiliar educadores físicos a reduzir a probabilidade de lesões durante o treinamento. Realizamos um experimento com 12 voluntários diversos para construir um classificador HAR com aproximadamente de 87 porcento de precisão geral na classificação, e um segundo experimento para validar nosso modelo de avaliação física. Por fim, realizamos uma entrevista semi estruturada para avaliar questões de usabilidade e experiência do usuário da plataforma proposta.Visando uma pesquisa replicável, fornecemos informações completas sobre o hardware e o código fonte do sistema, e disponibilizamos o conjunto de dados do experimento. / [en] Foot and knee pain fave been associated with numerous orthopedic pathologies and injuries of the lower limbs. From street running to CrossFitTM functional training, these common pains and injuries correlate highly with unevenly distributed plantar pressure and knee positioning during long-term physical practice and can lead to severe orthopedic injuries if the movement pattern is not amended. Therefore, the monitoring of foot plantar pressure distribution and the spatial and temporal characteristics of foot and knee positioning abnomalities is of utmost importance for injury prevention. This work proposes a platform, composed af an lot wearable body sensor network and a Human Activity Recognition (HAR), to provide realtime feedback of functional exercises, aiming to enhace physical educators capability to mitigate the probability of injuries during training. We conducted an experiment with 12 diverse volunteers to build a HAR classifier that achieved about 87 percent overall classification accuracy, and a second experiment to validate our physical evaluation model. Finally, we performed a semi-structured interview to evaluate usability and user experience issues regarding the proposed platform. Aiming at a replicable research, we provide full hardware information, system source code and a public domain dataset.

<p dir="ltr">With the ability to connect to networks and send and receive data, Internet of Things (IoT) devices involve associated security risks and threats, for a given environment. These threats are even more of a concern in a Smart Home network, where there is a lack of a dedicated security IT team, unlike a corporate environment. While efficient user interface(UI) and ease of use is at the front and center of IoT devices within Smart Home which enables its wider adoption, often security and privacy have been an afterthought and haven’t kept pace when needed. Therefore, a unsafe possibility exists where malicious actors could exploit vulnerable devices in a domestic home environment.</p><p dir="ltr">This thesis involves a detailed study of the cybersecurity for a Smart Home and also examines the various types of cyberthreats encountered, such as DDoS, Man-In-Middle, Ransomware, etc. that IoT devices face. Given, IoT devices are commonplace in most home automation scenarios, its crucially important to detect intrusions and unauthorized access. Privacy issues are also involved making this an even more pertinent topic. Towards this, various state of the art industry standard tools, such as Nmap, Nessus, Metasploit, etc. were used to gather data on a Smart Home environment to analyze their impacts to detect security vulnerabilities and risks to a Smart Home. Results from the research indicated various vulnerabilities, such as open ports, password vulnerabilities, SSL certificate anomalies and others that exist in many cases, and how precautions when taken in timely manner can help alleviate and bring down those risks.</p><p dir="ltr">Also, an IoT monitoring dashboard was developed based on open-source tools, which helps visualize threats and emphasize the importance of monitoring. The IoT dashboard showed how to raise alerts and alarms based on specific threat conditions or events. In addition, currently available cybersecurity regulations, standards, and guidelines were also examined that can help safeguard against threats to commonly used IoT devices in a Smart Home. It is hoped that the research carried out in this dissertation can help maintain safe and secure Smart Homes and provide direction for future work in the area of Smart Home Cybersecurity.</p>