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Jia W, Gao Y, Mao ZH, Sun M. Editorial for the Special Issue "Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare". SENSORS (BASEL, SWITZERLAND) 2024; 24:1403. [PMID: 38474939 PMCID: PMC10934641 DOI: 10.3390/s24051403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 03/14/2024]
Abstract
The integration of sensor technology in healthcare has become crucial for disease diagnosis and treatment [...].
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Affiliation(s)
- Wenyan Jia
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (Z.-H.M.); (M.S.)
| | - Yi Gao
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China;
| | - Zhi-Hong Mao
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (Z.-H.M.); (M.S.)
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Mingui Sun
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA; (Z.-H.M.); (M.S.)
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA 15260, USA
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Ghosh S, Kim S, Ijaz MF, Singh PK, Mahmud M. Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network. BIOSENSORS 2022; 12:bios12121153. [PMID: 36551120 PMCID: PMC9775098 DOI: 10.3390/bios12121153] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/25/2022] [Accepted: 12/07/2022] [Indexed: 06/12/2023]
Abstract
The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.
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Affiliation(s)
- Sayandeep Ghosh
- Department of Instrumentation and Electronics Engineering, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Kolkata 700106, West Bengal, India
| | - SeongKi Kim
- National Centre of Excellence in Software, Sangmyung University, Seoul 03016, Republic of Korea
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Pawan Kumar Singh
- Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Kolkata 700106, West Bengal, India
- School of Science and Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
| | - Mufti Mahmud
- School of Science and Technology, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
- Department of Computer Science, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK
- Medical Technologies Innovation Facility, Nottingham Trent University, Nottingham NG11 8NS, UK
- Computing and Informatics Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK
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