Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies

• Published in: Security and communication networks Vol. 2022; pp. 1 - 9
• Main Authors: Ajay, P., Huang, Ruihang
• Format: Journal Article
• Language: English
• Published: London Hindawi 09.08.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Applications programs; Artificial intelligence; Circuits; Coronaviruses; COVID-19; Deep learning; Electronic devices; Fitness; Generative adversarial networks; Health care; Inertial platforms; Machine learning; Mobile computing; Sensors; Smartphones; Wearable technology
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2022/8706784

Healthcare institutions, policymakers, and leaders around the world all agree that improving people’s health and livelihoods is our number one priority. Aging, disability, long-term care, and palliative care all pose significant challenges to the burden of illness and the health system. Wearable technology has a number of healthcare applications, from patient care to personal health. Wearable devices, sensors, mobile apps, and tracking technologies are essential for the diagnosis, prevention, monitoring, and treatment of chronic diseases. Create and test a method to automatically classify four functional fitness exercises commonly used in current circuit training routines. The proposed algorithm, fuzzy local feature C-means algorithm (FLFCM), enhanced with information-maximizing generative adversarial network, was used to locate five inertial measurement units on the upper and lower limbs, as well as the trunk, of fourteen participants (INFOGAN). The proposed method is suitable for this situation because it yields promising results.