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This paper presents a dataset related to the performance of the Ten Meter Walking Test, a test to allow locomotor capacity in different research and clinical settings. One of the most important parameters to measure is the gait speed during a path of ten meters. The data available in this dataset consists of accelerometer, magnetometer, and gyroscope data acquired with a mobile device in a waistband. The experiments were performed two times by 109 individuals (30 males and 79 females) in different senior residences in the Fundão municipality (Portugal). The dataset includes 208 samples because the sensors reported some failures. The acquisition of the sensors data allows the creation of a technological method for the automatic measurement of features related to the Ten Meter Walk Test, promoting patient independence in measuring their physical health status.
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[This corrects the article DOI: 10.1016/j.dib.2022.108874.].
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The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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It is increasingly possible to acquire Electrocardiographic data with featured low-cost devices. The proposed dataset will help map different signals for various diseases related to Electrocardiography data. The dataset presented in this paper is related to the acquisition of electrocardiography data during the standing up and seated positions. The data was collected from 219 individuals (112 men, 106 women, and one other) in different environments, but they are in the Covilhã municipality. The dataset includes the 219 recordings and corresponds to the sensors' recordings of a 30 s sitting and a 30 s standing test, which checks to approximately 1 min for each one. This dataset includes 3.7 h (approximately) of recordings for further analysis with data processing techniques and machine learning methods. It will be helpful for the complementary creation of a robust method for identifying the characteristics of individuals related to Electrocardiography signals.
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Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer's disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.
