Browsing by Author "Silva, Alfredo"
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- Anomaly detection using smart shirt and machine learning: a systematic reviewPublication . Nunes, Eduardo; Barbosa, José; Alves, Paulo; Franco, Tiago; Silva, AlfredoIn recent years, the popularity and use of Artificial Intelligence (AI) and significant investments in the Internet of Medical Things (IoMT) will be common to use products such as smart socks, smart pants, and smart shirts. These products are known as Smart Textile or E-textile, which can monitor and collect signals that our body emits. These signals allow it to extract anomalous components using Machine Learning (ML) techniques that play an essential role in this area. This study presents a Systematic Literature Review (SLR) on Anomaly Detection using ML techniques in Smart Shirt. The objectives of the SLR are: (i) to identify what type of anomaly the smart shirt can detect; (ii) what ML techniques are in use; (iii) which datasets are in use; (iv) identify smart shirt or signal acquisition devices worn in the chest region; (v) list the performance metrics used to evaluate the ML model; (vi) the results of the techniques in general; (vii) types of ML algorithms are being applied. The SLR selected eleven primary studies published between January/2017-May/2022. The results showed that six anomalies were identified, with the Fall anomaly being the most cited. The Support Vector Machines (SVM) algorithm is the most used. Most of the primary studies used public or private datasets. The Hexoskin smart shirt was most cited. The most used metric performance was accuracy. Almost all primary studies presented a result above 90%, and all primary studies used the Supervisioned type of ML.
- 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.
- myHealth: a mobile App for home muscle rehabilitationPublication . Franco, Tiago; Henriques, Pedro Rangel; Alves, Paulo; Pereira, Maria João; Oliveira, Leonardo Sestrem de; Leitão, Paulo; Silva, AlfredoThe constant loss of functional capacity due to aging can lead to a less active and dignified life, especially if the necessary care is not taken. One of the treatments for muscle rehabilitation is electrostimulation, but it may require two or three visits to the clinic per week. In this paper is proposed a mobile application that serves as a key for minimizing the number of visits to the clinic. The proposed treatment for muscle rehabilitation is through a wearable system that can provide electrostimulation at the patient’s home. The developed application called myHealth will serve as the interface between the patient and the physician. Besides managing the treatment sessions, the app is also in charge of operating the wearable system during the session. Thus, the communication defined between the systems is flexible; some parameter can be adjusted during the session. In this way, algorithms that can improve treatment performance can be implemented in the future. The tests performed showed that the app could successfully execute all the steps of the proposed home treatment scenario. Index Terms—mhealth, electrostimulation, wearable