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Bian S, Liu M, Zhou B, Lukowicz P. The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey. SENSORS (BASEL, SWITZERLAND) 2022; 22:4596. [PMID: 35746376 PMCID: PMC9229953 DOI: 10.3390/s22124596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/13/2022] [Accepted: 06/16/2022] [Indexed: 06/02/2023]
Abstract
Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.
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Affiliation(s)
- Sizhen Bian
- German Research Centre for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.L.); (B.Z.); (P.L.)
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Lavric A, Petrariu AI, Mutescu PM, Coca E, Popa V. Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design. SENSORS (BASEL, SWITZERLAND) 2022; 22:503. [PMID: 35062463 PMCID: PMC8778479 DOI: 10.3390/s22020503] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/13/2022]
Abstract
In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives.
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Affiliation(s)
- Alexandru Lavric
- Computers, Electronics and Automation Department, Stefan Cel Mare University of Suceava, 720229 Suceava, Romania; (A.I.P.); (P.-M.M.); (E.C.); (V.P.)
| | - Adrian I. Petrariu
- Computers, Electronics and Automation Department, Stefan Cel Mare University of Suceava, 720229 Suceava, Romania; (A.I.P.); (P.-M.M.); (E.C.); (V.P.)
- MANSiD Research Center, Stefan Cel Mare University of Suceava, 720229 Suceava, Romania
| | - Partemie-Marian Mutescu
- Computers, Electronics and Automation Department, Stefan Cel Mare University of Suceava, 720229 Suceava, Romania; (A.I.P.); (P.-M.M.); (E.C.); (V.P.)
| | - Eugen Coca
- Computers, Electronics and Automation Department, Stefan Cel Mare University of Suceava, 720229 Suceava, Romania; (A.I.P.); (P.-M.M.); (E.C.); (V.P.)
| | - Valentin Popa
- Computers, Electronics and Automation Department, Stefan Cel Mare University of Suceava, 720229 Suceava, Romania; (A.I.P.); (P.-M.M.); (E.C.); (V.P.)
- MANSiD Research Center, Stefan Cel Mare University of Suceava, 720229 Suceava, Romania
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Hong P, Herigon JC, Uptegraft C, Samuel B, Brown DL, Bickel J, Hron JD. Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic. J Am Med Inform Assoc 2021; 29:142-148. [PMID: 34623426 DOI: 10.1093/jamia/ocab231] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/28/2021] [Accepted: 10/06/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019. MATERIALS AND METHODS We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. RESULTS Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. CONCLUSION Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions.
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Affiliation(s)
- Peter Hong
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua C Herigon
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, Missouri, USA.,Department of Pediatrics, University of Missouri-Kansas City School of Medicine, USA, Kansas City, Missouri
| | - Colby Uptegraft
- Health Informatics Branch, Defense Health Agency, Falls Church, Virginia, USA
| | - Bassem Samuel
- Information Services Department, Boston Children's Hospital, Boston, Massachusetts, USA
| | - D Levin Brown
- Information Services Department, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jonathan Bickel
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Information Services Department, Boston Children's Hospital, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jonathan D Hron
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
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