Sensors
| Autor: | Mohammad Iman Mokhlespour Esfahani, Maury A. Nussbaum |
|---|---|
| Přispěvatelé: | Industrial and Systems Engineering |
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Adolescent Computer science Posture smart textile system Walking wearable sensor 02 engineering and technology lcsh:Chemical technology human health Machine learning computer.software_genre smart socks 01 natural sciences Biochemistry Article Analytical Chemistry Wearable Electronic Devices Young Adult Human health smart garment Activities of Daily Living Humans lcsh:TP1-1185 Electrical and Electronic Engineering Exercise Instrumentation business.industry Textiles physical activities 010401 analytical chemistry smart shirt 021001 nanoscience & nanotechnology Clothing Atomic and Molecular Physics and Optics 0104 chemical sciences Health promotion classification Female Neural Networks Computer Artificial intelligence 0210 nano-technology business Lying computer Algorithms |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 19 Issue 14 Sensors, Vol 19, Iss 14, p 3133 (2019) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s19143133 |
| Popis: | Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods&mdash K-nearest neighbor, linear discriminant analysis, and artificial neural network&mdash using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion. |
| Databáze: | OpenAIRE |
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