In the new era of the Internet-of-Things, athletic big data collection and analysis based on widely distributed sensing networks are particularly important in the development of intelligent sports. Conventional sensors usually require an external power supply, with limitations such as limited lifetime and high maintenance cost. As a newly developed mechanical energy harvesting and self-powered sensing technology, the triboelectric nanogenerator (TENG) shows great potential to overcome these limitations. Most importantly, TENGs can be fabricated using wood, paper, fibers, and polymers, which are the most frequently used materials for sports. Recent progress on the development of TENGs for the field of intelligent sports is summarized. First, the working mechanism of TENG and its association with athletic big data are introduced. Subsequently, the development of TENG-based sports sensing systems, including smart sports facilities and wearable equipment is highlighted. At last, the remaining challenges and open opportunities are also discussed.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/adma.202004178 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Dynamic simulation of breast behaviour during different activities based on finite element modelling of multiple components of breast.
Sci Rep
January 2025
School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
This study presents an advanced dynamic finite element (FE) model of multiple components of the breast to examine the biomechanical impact of different types of physical activities and activity intensity on the breast tissues. Using 4D scanning and motion capture technologies, dynamic data are collected during different activities. The accuracy of the FE model is verified based on relative mean absolute error (RMAE), and optimal material parameters are identified by using a validated stepwise grid search method.
View Article and Find Full Text PDFThe Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.
JMIR Mhealth Uhealth
January 2025
Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany.
Background: Artificial intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with wearable inertial measurement units (IMUs), AI could transform health care again by opening new opportunities in patient care and medical research.
Objective: This systematic review aims to evaluate the integration of AI models with wearable IMUs in health care, identifying current applications, challenges, and future opportunities.
Quantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.
Physiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
View Article and Find Full Text PDFExploring motivation, goals, facilitators, and barriers to adopt health behaviors at retirement age: a focus group study.
BMC Public Health
January 2025
Department of Psychology, Comillas Pontifical University, Comillas, 3-5, Madrid, 28049, Spain.
Background: This study qualitatively investigates retirement-age adults' perspectives on engaging in health behaviors such as physical activity or a healthy diet, distinguishing facilitators, barriers, goals, and motivations (the two later in line with Self-Determination Theory).
Methods: Two clinical psychologists conducted four focus groups with Spanish adults around retirement age. We conducted inductive and deductive content analysis.
Can response to ADHD medication be predicted?
Eur Child Adolesc Psychiatry
January 2025
Department of Clinical Sciences, Child and Adolescent Psychiatry, Umea University, Umea, Sweden.
Predictors for the pharmacological effect of ADHD medication in children and adolescents are lacking. This study examined clinically relevant factors in a large (N = 638) prospective cohort reflecting real-world evidence. Children and adolescents aged 6-17 diagnosed with ADHD were evaluated at baseline and three months following ADHD medication initiation.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!