{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:29:30Z","timestamp":1774312170576,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T00:00:00Z","timestamp":1490832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Developments and new advances in medical technology and the improvement of people\u2019s living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this issue, a video conference-based telemedicine system is deployed to break the limitations of medical resources in terms of time and space. By outsourcing medical resources from big hospitals to rural and remote ones, centralized and high quality medical resources can be shared to achieve a higher salvage rate while improving the utilization of medical resources. Though effective, existing telemedicine systems only treat patients\u2019 physiological diseases, leaving another challenging problem unsolved: How to remotely detect patients\u2019 emotional state to diagnose psychological diseases. In this paper, we propose a novel healthcare system based on a 5G Cognitive System (5G-Csys). The 5G-Csys consists of a resource cognitive engine and a data cognitive engine. Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications. Data cognitive intelligence, based on the analysis of healthcare big data, is used to handle a patient\u2019s health status physiologically and psychologically. In this paper, the architecture of 5G-Csys is first presented, and then the key technologies and application scenarios are discussed. To verify our proposal, we develop a prototype platform of 5G-Csys, incorporating speech emotion recognition. We present our experimental results to demonstrate the effectiveness of the proposed system. We hope this paper will attract further research in the field of healthcare based on 5G cognitive systems.<\/jats:p>","DOI":"10.3390\/bdcc1010002","type":"journal-article","created":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T09:49:54Z","timestamp":1490867394000},"page":"2","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":122,"title":["A 5G Cognitive System for Healthcare"],"prefix":"10.3390","volume":"1","author":[{"given":"Min","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-7915","authenticated-orcid":false,"given":"Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China"}]},{"given":"Yixue","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-0007","authenticated-orcid":false,"given":"Shiwen","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849-5201, USA"}]},{"given":"Kai","family":"Hwang","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science, University of Southern California, Los Angeles, CA 90089, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,30]]},"reference":[{"key":"ref_1","unstructured":"Hwang, K., and Chen, M. 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