Literature DB >> 26799721

Convergence Revolution Comes to Wearables: Multiple Advances are Taking Biosensor Networks to the Next Level in Health Care.

Leslie Mertz.   

Abstract

In the field of wearable biomedical sensors, the convergence revolution is more than a fanciful, utopian view of the way innovation should be done. Medical-grade wearable sensors rely on it. Their development requires technical know-how, computing expertise, clinical input, and collaboration-a true meeting of the minds to permit the conversion of wearables from neat gadgets into practical and proficient tools that will propel health care to new heights. Beyond the increasing miniaturization of hardware and the shift to wireless communication technology, flexible electronics and more powerful computing capabilities, including application specific integrated circuits (ASICs) and microelectromechanical systems (MEMS), have enabled new work on body sensor networks (BSNs) that monitor, analyze, and make sense of body signals for the prevention, diagnosis, and treatment of health disorders. Developments in processing, such as sensor-connected nodes combined with evolving algorithms and decreasing power requirements, have also contributed. In addition, new approaches to subjective measures (pain and emotion) have opened possibilities.

Entities:  

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Year:  2016        PMID: 26799721     DOI: 10.1109/MPUL.2015.2498475

Source DB:  PubMed          Journal:  IEEE Pulse        ISSN: 2154-2287            Impact factor:   0.924


  4 in total

Review 1.  Challenges and opportunities of digital health in a post-COVID19 world.

Authors:  Amirreza Manteghinejad; Shaghayegh Haghjooy Javanmard
Journal:  J Res Med Sci       Date:  2021-02-16       Impact factor: 1.852

2.  Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method.

Authors:  Mengxuan Li; Pengfei Li; Shanshan Tian; Kai Tang; Xi Chen
Journal:  Sensors (Basel)       Date:  2018-05-28       Impact factor: 3.576

3.  Combining Wearable Devices and Mobile Surveys to Study Child and Youth Development in Malawi: Implementation Study of a Multimodal Approach.

Authors:  Onicio Leal Neto; Simon Haenni; John Phuka; Laura Ozella; Daniela Paolotti; Ciro Cattuto; Daniel Robles; Guilherme Lichand
Journal:  JMIR Public Health Surveill       Date:  2021-03-05

4.  Personalized Activity Recognition with Deep Triplet Embeddings.

Authors:  David Burns; Philip Boyer; Colin Arrowsmith; Cari Whyne
Journal:  Sensors (Basel)       Date:  2022-07-13       Impact factor: 3.847

  4 in total

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