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- Motion sensors for Knee angle recognition in muscle rehabilitation solutionsPublication . Franco, Tiago; Oliveira, Leonardo Sestrem de; Henriques, Pedro Rangel; Alves, Paulo; Pereira, Maria João; Brandão, Diego; Leitão, Paulo; Silva, AlfredoThe progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity O(1) were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor.
- Big data analytics para a classificação do risco de abandono escolar em cursos do ensino superiorPublication . Franco, Tiago; Alves, Paulo; Koscianski, AndréO abandono escolar é uma constante preocupação das instituições de ensino superior, com divergentes e complexos fatores relatados por diversos autores. Conseguimos notar que este problema é bastante abrangente e explorado, já sendo possível encontrar nas instituições setores especializados ou programas de auxílio como psicólogos, auxílio moradia, programa de monitoria, entre outros, buscando minimizar a quantidade de alunos desistentes. Entretanto estas propostas dependem do próprio aluno a buscar ajuda necessária, abrindo uma lacuna para aqueles que não se sentem confortáveis a procurar ou não possuem total conhecimento do próprio caso. Este trabalho propõe um modelo para a identificação prévia dos alunos desistentes, com objetivo de tornar as instituições de ensino aptas a entender melhor os casos de abandono e se possível encaminhá-los a setores especializados. Para tal, utilizamos o Instituto Politécnico de Bragança como estudo de caso que nos forneceu mais 200 milhões de registros relacionados aos alunos matriculados entre 2008 a 2017. Analisamos e processamos a Big Data fornecida com a finalidade de moldá-la como parâmetros de entrada de algoritmos de machine learning. Inicialmente testamos três algoritmos e descobrimos que o random forest demonstra ser o mais eficiente neste contexto. A partir disso, aproveitamos do volume de dados para identificar qual seria melhor ciclo de treino e obtemos que o período de 4 anos consegue atingir melhores resultados. No aprimoramento do modelo adicionamos mais 2 atributos buscando realçar a trajetória escolar do aluno. Para implementação e visualização do modelo, desenvolvemos uma ferramenta de extração de dados e uma aplicação Web, que através de diferentes níveis de acesso, além de conseguir identificar os alunos em risco de abandono, também possibilita aos usuários efetuar análises comparativas entre escolas e cursos por meio de uma página personalizada com estatísticas transformadas em gráficos e tabelas. O estudo se apresenta como uma boa solução para identificação prévia dos alunos em risco de abandono, possibilitando análises e encaminhamentos. O modelo ainda pode ser ampliado a mais parâmetros e tende a obter melhores resultados ao longo dos anos aperfeiçoando através do reforço os atributos criados.
- Biofeedback-based method for real-time fatigue monitoring of kneePublication . Franco, Tiago; Henriques, Pedro; Alves, Paulo; Pereira, Maria João; Leitão, Paulo; Azevedo, NelsonThis paper introduces and implements a method to monitor muscle fatigue in real-time using a wearable biofeedback system to improve muscle rehabilitation treatments. The biofeedback system consists of an electromyography (EMG) sensor to capture muscle activity and two motion sensors to track knee angles. The proposed method for monitoring muscle fatigue involves three steps: (1) recognition of the movement phases during the knee extension exercise; (2) clipping of the EMG signal and calculation of fatigue-related metrics; and (3) normalization of metrics through a calibration process. An experimental session was performed with 10 healthy subjects performing 50 repetitions of the knee extension exercise. Processed data revealed changes in fatigue-related metrics, which align with existing literature. A comparison was also made between real-time and computer processing using raw data. While minor differences were noted between the two processing methods, the mobile app closely mirrored the trajectory of processed data in the cloud, ensuring reliability and consistency. This study advances remote muscle rehabilitation by quantifying muscle fatigue during treatment sessions. Thus, health professionals can tailor treatment plans based on individual patient characteristics, optimizing treatment duration, and reducing injury risk.
- Model for the identification of students at risk of dropout using big data analyticsPublication . Franco, Tiago; Alves, PauloIn the school context, one of the main metrics for institution performance is the student’s dropout rate. The decrease of the number of students in a university implies a reduction of the main resources necessary for its operation, but the difficulty of this problem is that we need to identify early as possible the students that are at risk of dropout, in order to adopt measures before they give up. This work proposes a model for the early identification of students at dropout risk, extracting weekly the academic data generated by the university and applying machine learning techniques with the aim of producing a classification of dropout. We use as a case study the Instituto Politécnico de Bragança from Portugal, which provided data of three different datasets refers to the years 2009 to 2017, resulting in 200 million records. The results indicate that the proposed model is a good option to early identify students at risk of dropout, based on the critical_rate attribute created it is possible to generate a ranking of necessity, allowing institutions to target their resources in a critical order, minimizing their expenses and the errors of the model itself.
- System architecture for home muscle rehabilitation treatmentPublication . Franco, Tiago; Henriques, Pedro Rangel; Alves, Paulo; Pereira, Maria João; Pedrosa, Tiago; Silva, F.; Leitão, Paulo; Oliveira, L.The constant loss of functional capacity due to aging is a serious problem that can severely worsen the quality of life. Among the possible treatments, muscles electrostimulation can be a viable option because it is cheap and easy to access. With the evolution of wearable devices and the Internet of Things, this paper proposes a system architecture that enables an electrostimulation treatment to be performed at the patient’s home. For that, a scenario is presented in which the physiotherapist sets the treatment parameters online and the patient performs the electrostimulation sessions using a wearable with biofeedback sensors. To test the proposed architecture, a prototype capable of simulating a muscle rehabilitation treatment was implemented. The prototype was successful in performing the proposed scenario, following the defined rules for electrostimulation and collecting biofeedback at the same time.
- Data acquisition, conditioning and processing system for a wearable-based biostimulationPublication . Oliveira, Leonardo Sestrem de; Kaizer, Raul; Gonçalves, João; Leitão, Paulo; Teixeira, João Paulo; Lima, José; Franco, Tiago; Carvalho, José AugustoData acquisition by electromyography, as well as the muscle stimulation, has become more accessible with the new developments in the wearable technology and medicine. In fact, for treatments, games or sports, it is possible to find examples of the use of muscle signals to analyse specific aspects related, e.g., to disease, injuries or movement impulses. However, these systems are usually expensive, does not integrate data acquisition with the muscle stimulation and does not exhibit an adaptive control behaviour that consider the pathology and the patient response. This paper presents a wearable system that integrates the signal acquisition and the electrostimulation using dry thin-film titanium-based electrodes. The acquired data is transmitted to a mobile application running on a smartphone by using Bluetooth Low Energy (BLE) technology, where it is analysed by employing artificial intelligence algorithms to provide customised treatments for each patient profile and type of pathology, and taking into consideration the feedback of the acquired electromyography signal. The acquired patient’s data is also stored in a secure cloud database to support the physician to analyse and follow-up the clinical results from the rehabilitation process.
- Implementation of big data analytics tool in a higher education institutionPublication . Franco, Tiago; Alves, Paulo; Pedrosa, Tiago; Pereira, Maria João; Canão, JoséIn search of intelligent solutions that could help improve teaching in higher education, we discovered a set of analyzes that had already been discussed and just needed to be implemented. We believe that this reality can be found in several educational institutions, with paper or mini-projects that deal with educational data and can have positive impacts on teaching. Because of this, we designed an architecture that could extract from multiple sources of educational data and support the implementation of some of these projects found. The results show an important tool that can contribute positively to the teaching institution. Effectively, we can highlight that the implementation of a predictive model of students at risk of dropping out will bring a new analytical vision. Also, the system’s practicality will save managers a lot of time in creating analyzes of the state of the institutions, respecting privacy concerns of the manipulated data, supported by a secure development methodology.
- Approaches to classify knee osteoarthritis using biomechanical dataPublication . Franco, Tiago; Henriques, Pedro Rangel; Alves, Paulo; Pereira, Maria JoãoKnee osteoarthritis (KOA) is a degenerative disease that mainly affects the elderly. The development of this disease is associated with a complex set of factors that cause abnormalities in motor functions. The purpose of this review is to understand the composition of works that combine biomechanical data and machine learning techniques to classify KOA progress. This study was based on research articles found in the search engines Scopus and PubMed between January 2010 and April 2021. The results were divided into data acquisition, feature engineering, and algorithms to synthesize the discovered content. Several approaches have been found for KOA classification with significant accuracy, with an average of 86% overall and three papers reaching 100%; that is, they did not fail once in their tests. The acquisition of data proved to be the divergent task between the works, the most considerable correlation in this stage was the use of the ground reaction force (GRF) sensor. Although three studies reached 100% in the classification, two did not use a gradual evaluation scale, classifying between KOA or healthy individuals. Thus, we can get out of this work that machine learning techniques are promising for identifying KOA using biomechanical data. However, the classification of pathological stages is a complex problem to discuss, mainly due to the difficult access and lack of standardization in data acquisition.
- Decision support system for NMES treatments : a solution designPublication . Franco, TiagoThe preservation of functional capacity in old age is associated with a more active and dignified life. Maintaining this capacity is not a trivial task; several diseases can make it difficult to practice regular physical exercises or make it unfeasible, such as knee osteoarthritis. Neuromuscular electrical stimulation (NMES) is a treatment for muscle rehabilitation positioned as an alternative. However, it is still unclear which electrostimulation configurations can produce the most effective treatment. The literature indicates that the difference in treatment depends on the intrinsic characteristics of the patients. In this scenario, a decision support system is designed to assist physiotherapists in data analyses to create a personalized treatment. The proposed treatment relies on a wearable system with an NMES actuator and biofeedback sensors. Thus, it is expected to adapt the NMES in real-time based on unique patient characteristics, such as muscle fatigue. In addition, the system architecture is designed for the treatment session to be carried out at the patient's home, reducing costs and providing more comfort.
- Titanium based dry electrodes for biostimulation and data acquisitionPublication . Conselheiro, Raul; Lopes, Claudia; Azevedo, Nelson; Leitao, Paulo; Agulhari, Cristiano; Veloso, H.; Vaz, F.; Gonçalves, João Lucas; Franco, Tiago; Oliveira, Leonardo Sestrem deWet electrodes rely on conductive electrolyte gel for proper perfor- mance, usually presenting high setup time and being disposable or requiring time-consuming cleaning methods. Skin irritation and signal quality deterioration are some of the problems that may occur with these electrodes. Therefore, to overcome these drawbacks, 3D printed bases using Fused Deposition Modelling (FDM) with Polylactic acid (PLA), Polyurethane (PU) and Cellulose filaments were functionalized with titanium (Ti) and titanium-nitride (TiN) thin films and dopped with copper (Cu). The electrodes, implemented using different diameters, were used to record electromyography (EMG) signals proceeded by biostimula- tion sessions to access their electrical and mechanical characteristics and com- pare them to that of commercial AgCl/Ag electrodes. The results show that TiN and TiNCu0.45 dry electrodes present results similar to wet AgCl/Ag electrodes for data acquisition. To be comparable to usual carbon electrodes for muscle stimulation, they need some conductivity improvements to lower the necessary voltage. Besides, the endurance of the thin films must be enhanced as well as the adhesion to the polymer.