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Boiko A, Martínez Madrid N, Seepold R. Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115038. [PMID: 37299762 DOI: 10.3390/s23115038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
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Affiliation(s)
- Andrei Boiko
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
| | - Natividad Martínez Madrid
- Internet of Things Laboratory, School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany
| | - Ralf Seepold
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
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Ott T, Heckel M, Öhl N, Steigleder T, Albrecht NC, Ostgathe C, Dabrock P. Palliative care and new technologies. The use of smart sensor technologies and its impact on the Total Care principle. BMC Palliat Care 2023; 22:50. [PMID: 37101258 PMCID: PMC10131446 DOI: 10.1186/s12904-023-01174-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/14/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Palliative care is an integral part of health care, which in term has become increasingly technologized in recent decades. Lately, innovative smart sensors combined with artificial intelligence promise better diagnosis and treatment. But to date, it is unclear: how are palliative care concepts and their underlying assumptions about humans challenged by smart sensor technologies (SST) and how can care benefit from SST? AIMS The paper aims to identify changes and challenges in palliative care due to the use of SST. In addition, normative guiding criteria for the use of SST are developed. METHODS The principle of Total Care used by the European Association for Palliative Care (EAPC) forms the basis for the ethical analysis. Drawing on this, its underlying conceptions of the human and its socio-ethical aspects are examined with a phenomenological focus. In the second step, the advantages, limitations, and socio-ethical challenges of using SST with respect to the Total Care principle are explored. Finally, ethical-normative requirements for the application of SST are derived. RESULTS AND CONCLUSION First, SST are limited in their measurement capabilities. Second, SST have an impact on human agency and autonomy. This concerns both the patient and the caregiver. Third, some aspects of the Total Care principle are likely to be marginalized due to the use of SST. The paper formulates normative requirements for using SST to serve human flourishing. It unfolds three criteria according to which SST must be aligned: (1) evidence and purposefulness, (2) autonomy, and (3) Total Care.
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Grants
- SFB 1483 - Project-ID 442419336, EmpkinS Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
- SFB 1483 - Project-ID 442419336, EmpkinS Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
- SFB 1483 - Project-ID 442419336, EmpkinS Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
- SFB 1483 - Project-ID 442419336, EmpkinS Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
- SFB 1483 - Project-ID 442419336, EmpkinS Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
- SFB 1483 - Project-ID 442419336, EmpkinS Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
- SFB 1483 - Project-ID 442419336, EmpkinS Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
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Affiliation(s)
- Tabea Ott
- Chair of Systematic Theology II (Ethics), Faculty of Humanities, Social Sciences, and Theology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Kochstraße 6, Erlangen, 91054, Germany.
| | - Maria Heckel
- Department of Palliative Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Werner-von-Siemens-Straße 34, Erlangen, 91052, Germany
| | - Natalie Öhl
- Department of Palliative Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Werner-von-Siemens-Straße 34, Erlangen, 91052, Germany
| | - Tobias Steigleder
- Department of Palliative Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Werner-von-Siemens-Straße 34, Erlangen, 91052, Germany
| | - Nils C Albrecht
- Institute for High Frequency Technology, Hamburg University of Technology, Denickestraße 22 (I), 21073, Hamburg, Germany
| | - Christoph Ostgathe
- Department of Palliative Medicine, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Werner-von-Siemens-Straße 34, Erlangen, 91052, Germany
| | - Peter Dabrock
- Chair of Systematic Theology II (Ethics), Faculty of Humanities, Social Sciences, and Theology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Kochstraße 6, Erlangen, 91054, Germany
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Wu X, Zhou S, Chen M, Zhao Y, Wang Y, Zhao X, Li D, Pu H. Combined spectral and speech features for pig speech recognition. PLoS One 2022; 17:e0276778. [PMID: 36454724 PMCID: PMC9714723 DOI: 10.1371/journal.pone.0276778] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/13/2022] [Indexed: 12/03/2022] Open
Abstract
The sound of the pig is one of its important signs, which can reflect various states such as hunger, pain or emotional state, and directly indicates the growth and health status of the pig. Existing speech recognition methods usually start with spectral features. The use of spectrograms to achieve classification of different speech sounds, while working well, may not be the best approach for solving such tasks with single-dimensional feature input. Based on the above assumptions, in order to more accurately grasp the situation of pigs and take timely measures to ensure the health status of pigs, this paper proposes a pig sound classification method based on the dual role of signal spectrum and speech. Spectrograms can visualize information about the characteristics of the sound under different time periods. The audio data are introduced, and the spectrogram features of the model input as well as the audio time-domain features are complemented with each other and passed into a pre-designed parallel network structure. The network model with the best results and the classifier were selected for combination. An accuracy of 93.39% was achieved on the pig speech classification task, while the AUC also reached 0.99163, demonstrating the superiority of the method. This study contributes to the direction of computer vision and acoustics by recognizing the sound of pigs. In addition, a total of 4,000 pig sound datasets in four categories are established in this paper to provide a research basis for later research scholars.
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Affiliation(s)
- Xuan Wu
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Silong Zhou
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Mingwei Chen
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Yihang Zhao
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Yifei Wang
- Department of Economics, University of Calgary, Calgary, AB, Canada
| | - Xianmeng Zhao
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Danyang Li
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
| | - Haibo Pu
- College of Information Engineering, Sichuan Agricultural University, Ya’an, Sichuan, China
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Iqbal MZ, Campbell AG. From luxury to necessity: Progress of touchless interaction technology. TECHNOLOGY IN SOCIETY 2021; 67:101796. [PMID: 36313277 PMCID: PMC9595506 DOI: 10.1016/j.techsoc.2021.101796] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 05/05/2023]
Abstract
Touchless Technology is facilitating the move to Zero User Interface(UI) propelled by the COVID-19 pandemic which has accelerated the use of this technology due to hygiene requirements. Zero UI can be defined as a controlled interface that enables user interaction with technology through voice, gestures, hand interaction, eye tracking, and biometrics such as facial recognition and contactless fingerprints. Smart devices, IoT sensors, smart appliances, smart TVs, smart assistants and consumer robotics are predominant examples of devices in which Zero UI is becoming increasingly adopted. These control interfaces include natural interaction modes such as voice or gestures. Touchscreens and shared devices such as kiosks, self-service counters and interactive displays are present in our everyday lives. Each of these interactions however is a concern for consumers in a post-COVID-19 world where hygiene is of utmost importance. The one-stop solution to hygienic interactions includes touchless technology such as voice control, remote mobile screen take over, biometric, and gesture control as Zero User interfaces. With the breakthroughs in image recognition and natural language processing, powered by advanced computer vision and machine learning, "Zero UI" is becoming a new normal. This paper is focusing on the progress of the touchless interaction technology during the COVID-19 pandemic, which actually accelerated development in this concept and moved it from being a luxury to a life necessity.
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Aghasafari P, Yang PC, Kernik DC, Sakamoto K, Kanda Y, Kurokawa J, Vorobyov I, Clancy CE. A deep learning algorithm to translate and classify cardiac electrophysiology. eLife 2021; 10:68335. [PMID: 34212860 PMCID: PMC8282335 DOI: 10.7554/elife.68335] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/29/2021] [Indexed: 01/15/2023] Open
Abstract
The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.
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Affiliation(s)
- Parya Aghasafari
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States
| | - Pei-Chi Yang
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States
| | - Divya C Kernik
- Washington University in St. Louis, St. Louis, United States
| | - Kazuho Sakamoto
- Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yasunari Kanda
- Division of Pharmacology, National Institute of Health Sciences, Kanagawa, Japan
| | - Junko Kurokawa
- Department of Bio-Informational Pharmacology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Igor Vorobyov
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States.,Department of Pharmacology, University of California, Davis, Davis, United States
| | - Colleen E Clancy
- Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States
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Drury RL, Jarczok M, Owens A, Thayer JF. Wireless Heart Rate Variability in Assessing Community COVID-19. Front Neurosci 2021; 15:564159. [PMID: 34168534 PMCID: PMC8217820 DOI: 10.3389/fnins.2021.564159] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 05/07/2021] [Indexed: 01/09/2023] Open
Affiliation(s)
| | - Marc Jarczok
- Clinic for Psychosomatic Medicine and Psychotherapy, University Clinic Ulm, Ulm, Germany
| | - Andrew Owens
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Julian F Thayer
- Psychological Sciences Faculty, University of California, Irvine, Irvine, CA, United States
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