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Ibanez A, Herzog R, Barbey F, Islam MN, Rueda-Delgado L, Nolan H, Prado P, Krylova M, Javaheripour N, Danyeli L, Sen Z, Walter M, Odonnell P, Buhl D, Murphy B, Izyurov I. High-order brain interactions in ketamine during rest and task: A double-blinded cross-over design using portable EEG. RESEARCH SQUARE 2024:rs.3.rs-3954073. [PMID: 38562802 PMCID: PMC10984031 DOI: 10.21203/rs.3.rs-3954073/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
In a double-blinded cross-over design, 30 adults (mean age = 25.57, SD = 3.74; all male) were administered racemic ketamine and compared against saline infusion as a control. Both task-driven (auditory oddball paradigm) and resting-state EEG were recorded. HOI were computed using advanced multivariate information theory tools, allowing us to quantify nonlinear statistical dependencies between all possible electrode combinations. Results: Ketamine increased redundancy in brain dynamics, most significantly in the alpha frequency band. Redundancy was more evident during the resting state, associated with a shift in conscious states towards more dissociative tendencies. Furthermore, in the task-driven context (auditory oddball), the impact of ketamine on redundancy was more significant for predictable (standard stimuli) compared to deviant ones. Finally, associations were observed between ketamine's HOI and experiences of derealization. Conclusions: Ketamine appears to increase redundancy and genuine HOI across metrics, suggesting these effects correlate with consciousness alterations towards dissociation. HOI represents an innovative method to combine all signal spatial interactions obtained from low-density dry EEG in drug interventions, as it is the only approach that exploits all possible combinations from different electrodes. This research emphasizes the potential of complexity measures coupled with portable EEG devices in monitoring shifts in consciousness, especially when paired with low-density configurations, paving the way for better understanding and monitoring of pharmacological-induced changes.
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El-Tallawy SN, Ahmed RS, Nagiub MS. Pain Management in the Most Vulnerable Intellectual Disability: A Review. Pain Ther 2023; 12:939-961. [PMID: 37284926 PMCID: PMC10290021 DOI: 10.1007/s40122-023-00526-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/10/2023] [Indexed: 06/08/2023] Open
Abstract
This review is made up of two parts; the first part discussing intellectual disability (ID) in general, while the second part covers the pain associated with intellectual disability and the challenges and practical tips for the management of pain associated with (ID). Intellectual disability is characterized by deficits in general mental abilities, such as reasoning, problem solving, planning, abstract thinking, judgment, academic learning, and learning from experience. ID is a disorder with no definite cause but has multiple risk factors, including genetic, medical, and acquired. Vulnerable populations such as individuals with intellectual disability may experience more pain than the general population due to additional comorbidities and secondary conditions, or at least the same frequency of pain as in the general population. Pain in patients with ID remains largely unrecognized and untreated due to barriers to verbal and non-verbal communication. It is important to identify patients at risk to promptly prevent or minimize those risk factors. As pain is multifactorial, thus, a multimodal approach using both pharmacotherapy and non-pharmacological management is often the most beneficial. Parents and caregivers should be oriented to this disorder, given adequate training and education, and be actively involved with the treatment program. Significant work to create new pain assessment tools to improve pain practices for individuals with ID has taken place, including neuroimaging and electrophysiological studies. Recent advances in technology-based interventions such as virtual reality and artificial intelligence are rapidly growing to help give patients with ID promising results to develop pain coping skills with effective reduction of pain and anxiety. Therefore, this narrative review highlights the different aspects regarding the current status of the pain associated with intellectual disability, with more emphasis on the recent pieces of evidence for the assessment and management of pain among populations with intellectual disability.
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Affiliation(s)
- Salah N. El-Tallawy
- King Khalid University Hospital, College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Anesthesia Department, Faculty of Medicine, Minia University and NCI, Cairo University, Giza, Egypt
| | - Rania S. Ahmed
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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Vidhya CM, Maithani Y, Singh JP. Recent Advances and Challenges in Textile Electrodes for Wearable Biopotential Signal Monitoring: A Comprehensive Review. BIOSENSORS 2023; 13:679. [PMID: 37504078 PMCID: PMC10377545 DOI: 10.3390/bios13070679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/17/2023] [Accepted: 06/20/2023] [Indexed: 07/29/2023]
Abstract
The technology of wearable medical equipment has advanced to the point where it is now possible to monitor the electrocardiogram and electromyogram comfortably at home. The transition from wet Ag/AgCl electrodes to various types of gel-free dry electrodes has made it possible to continuously and accurately monitor the biopotential signals. Fabrics or textiles, which were once meant to protect the human body, have undergone significant development and are now employed as intelligent textile materials for healthcare monitoring. The conductive textile electrodes provide the benefit of being breathable and comfortable. In recent years, there has been a significant advancement in the fabrication of wearable conductive textile electrodes for monitoring biopotential signals. This review paper provides a comprehensive overview of the advances in wearable conductive textile electrodes for biopotential signal monitoring. The paper covers various aspects of the technology, including the electrode design, various manufacturing techniques utilised to fabricate wearable smart fabrics, and performance characteristics. The advantages and limitations of various types of textile electrodes are discussed, and key challenges and future research directions are identified. This will allow them to be used to their fullest potential for signal gathering during physical activities such as running, swimming, and other exercises while being linked into wireless portable health monitoring systems.
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Affiliation(s)
- C M Vidhya
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Yogita Maithani
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Jitendra P Singh
- Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
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4
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Rockholt MM, Kenefati G, Doan LV, Chen ZS, Wang J. In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand? Front Neurosci 2023; 17:1186418. [PMID: 37389362 PMCID: PMC10301750 DOI: 10.3389/fnins.2023.1186418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/12/2023] [Indexed: 07/01/2023] Open
Abstract
Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.
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Affiliation(s)
- Mika M. Rockholt
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - George Kenefati
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Lisa V. Doan
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
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5
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Del Percio C, Lopez S, Noce G, Lizio R, Tucci F, Soricelli A, Ferri R, Nobili F, Arnaldi D, Famà F, Buttinelli C, Giubilei F, Marizzoni M, Güntekin B, Yener G, Stocchi F, Vacca L, Frisoni GB, Babiloni C. What a Single Electroencephalographic (EEG) Channel Can Tell us About Alzheimer's Disease Patients With Mild Cognitive Impairment. Clin EEG Neurosci 2023; 54:21-35. [PMID: 36413420 DOI: 10.1177/15500594221125033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abnormalities in cortical sources of resting-state eyes closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10-20 montage) with 19 scalp electrodes characterized Alzheimer's disease (AD) from preclinical to dementia stages. An intriguing rsEEG application is the monitoring and evaluation of AD progression in large populations with few electrodes in low-cost devices. Here we evaluated whether the above-mentioned abnormalities can be observed from fewer scalp electrodes in patients with mild cognitive impairment due to AD (ADMCI). Clinical and rsEEG data acquired in hospital settings (10-20 montage) from 75 ADMCI participants and 70 age-, education-, and sex-matched normal elderly controls (Nold) were available in an Italian-Turkish archive (PDWAVES Consortium; www.pdwaves.eu). Standard spectral fast fourier transform (FFT) analysis of rsEEG data for individual delta, theta, and alpha frequency bands was computed from 6 monopolar scalp electrodes to derive bipolar C3-P3, C4-P4, P3-O1, and P4-O2 markers. The ADMCI group showed increased delta and decreased alpha power density at the C3-P3, C4-P4, P3-O1, and P4-O2 bipolar channels compared to the Nold group. Increased theta power density for ADMCI patients was observed only at the C3-P3 bipolar channel. Best classification accuracy between the ADMCI and Nold individuals reached 81% (area under the receiver operating characteristic curve) using Alpha2/Theta power density computed at the C3-P3 bipolar channel. Standard rsEEG power density computed from six posterior bipolar channels characterized ADMCI status. These results may pave the way toward diffuse clinical applications in health monitoring of dementia using low-cost EEG systems with a strict number of electrodes in lower- and middle-income countries.
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Affiliation(s)
- Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | | | | | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Flavio Nobili
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Dario Arnaldi
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Francesco Famà
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, 218502Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab., 218502Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir University of Economics, Faculty of Medicine, Izmir, Turkey
| | | | | | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and 27212University of Geneva, Geneva, Switzerland
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy.,Hospital San Raffaele Cassino, Cassino (FR), Italy
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Ramasubramanian B, Reddy VS, Chellappan V, Ramakrishna S. Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases. BIOSENSORS 2022; 12:1176. [PMID: 36551143 PMCID: PMC9775999 DOI: 10.3390/bios12121176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.
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Affiliation(s)
- Brindha Ramasubramanian
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Vundrala Sumedha Reddy
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
| | - Vijila Chellappan
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Seeram Ramakrishna
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
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7
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Buckholz AP, Rosenblatt R. Remote monitoring of cognition in cirrhosis and encephalopathy: future opportunity and challenge. Metab Brain Dis 2022; 38:1737-1747. [PMID: 36507937 PMCID: PMC9743123 DOI: 10.1007/s11011-022-01134-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/24/2022] [Indexed: 12/14/2022]
Abstract
Hepatic Encephalopathy (HE) is a critically important complication of chronic liver disease and portal hypertension, but especially in early covert stages remains underdiagnosed and a common cause of hospitalization and morbidity. Defined by often subtle neuropsychiatric changes, significant cognitive deficits have been extensively described. While traditional methods of assessment remain underutilized in practice and subject to significant confounding with other diseases, mobile technology has emerged as a potential future tool to provide simple and dynamic cognitive assessments. This review discusses the proliferation of cognitive assessment tools, describing possible applications in encephalopathy and the challenges such an implementation may face. There are significant potential advantages to assessing cognition in real time in order to aid early detection and intervention and provide a more realistic measurement of real-world function. Despite this, there are issues with reliability, privacy, applicability and more which must be addressed prior to wide proliferation and acceptance for clinical use. Regardless, the rapid uptake of mobile technology in healthcare is likely to have significant implications for the future management of encephalopathy and liver disease at large.
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Affiliation(s)
- Adam P Buckholz
- NewYork-Presbyterian/Weill Cornell Medical College Division of Gastroenterology and Hepatology, New York, NY, 10021, USA
| | - Russell Rosenblatt
- NewYork-Presbyterian/Weill Cornell Medical College Division of Gastroenterology and Hepatology, New York, NY, 10021, USA.
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8
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Lin X, Luo J, Liao M, Su Y, Lv M, Li Q, Xiao S, Xiang J. Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review. BIOSENSORS 2022; 12:1131. [PMID: 36551098 PMCID: PMC9775571 DOI: 10.3390/bios12121131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 11/24/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Recent advances in sensor technology have facilitated the development and use of personalized sensors in monitoring environmental factors and the associated health effects. No studies have reviewed the research advancement in examining population-based health responses to environmental exposure via portable sensors/instruments. This study aims to review studies that use portable sensors to measure environmental factors and health responses while exploring the environmental effects on health. With a thorough literature review using two major English databases (Web of Science and PubMed), 24 eligible studies were included and analyzed out of 16,751 total records. The 24 studies include 5 on physical factors, 19 on chemical factors, and none on biological factors. The results show that particles were the most considered environmental factor among all of the physical, chemical, and biological factors, followed by total volatile organic compounds and carbon monoxide. Heart rate and heart rate variability were the most considered health indicators among all cardiopulmonary outcomes, followed by respiratory function. The studies mostly had a sample size of fewer than 100 participants and a study period of less than a week due to the challenges in accessing low-cost, small, and light wearable sensors. This review guides future sensor-based environmental health studies on project design and sensor selection.
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Affiliation(s)
- Xueer Lin
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Jiaying Luo
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Minyan Liao
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Yalan Su
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Mo Lv
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
| | - Qing Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China
| | - Shenglan Xiao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Jianbang Xiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518107, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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What a single electroencephalographic (EEG) channel can tell us about patients with dementia due to Alzheimer's disease. Int J Psychophysiol 2022; 182:169-181. [DOI: 10.1016/j.ijpsycho.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
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Wu JY, Ching CTS, Wang HMD, Liao LD. Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications. BIOSENSORS 2022; 12:1097. [PMID: 36551064 PMCID: PMC9776100 DOI: 10.3390/bios12121097] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.
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Affiliation(s)
- Ju-Yu Wu
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli County 35053, Taiwan
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
| | - Congo Tak-Shing Ching
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, South District, Taichung City 402, Taiwan
- Department of Electrical Engineering, National Chi Nan University, No. 1 University Road, Puli Township, Nantou County 545301, Taiwan
| | - Hui-Min David Wang
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, South District, Taichung City 402, Taiwan
| | - Lun-De Liao
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli County 35053, Taiwan
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
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Kaklauskas A, Abraham A, Ubarte I, Kliukas R, Luksaite V, Binkyte-Veliene A, Vetloviene I, Kaklauskiene L. A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States. SENSORS (BASEL, SWITZERLAND) 2022; 22:7824. [PMID: 36298176 PMCID: PMC9611164 DOI: 10.3390/s22207824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/28/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.
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Affiliation(s)
- Arturas Kaklauskas
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ajith Abraham
- Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA 98071, USA
| | - Ieva Ubarte
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Romualdas Kliukas
- Department of Applied Mechanics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Vaida Luksaite
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Arune Binkyte-Veliene
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ingrida Vetloviene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Loreta Kaklauskiene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
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Berger SE, Baria AT. Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches. FRONTIERS IN PAIN RESEARCH 2022; 3:896276. [PMID: 35721658 PMCID: PMC9201034 DOI: 10.3389/fpain.2022.896276] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.
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Affiliation(s)
- Sara E. Berger
- Responsible and Inclusive Technologies Research, Exploratory Sciences Division, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States
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Hanidziar D, Westover MB. Monitoring of sedation in mechanically ventilated patients using remote technology. Curr Opin Crit Care 2022; 28:360-366. [PMID: 35653256 PMCID: PMC9434805 DOI: 10.1097/mcc.0000000000000940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW Two years of coronavirus disease 2019 (COVID-19) pandemic highlighted that excessive sedation in the ICU leading to coma and other adverse outcomes remains pervasive. There is a need to improve monitoring and management of sedation in mechanically ventilated patients. Remote technologies that are based on automated analysis of electroencephalogram (EEG) could enhance standard care and alert clinicians real-time when severe EEG suppression or other abnormal brain states are detected. RECENT FINDINGS High rates of drug-induced coma as well as delirium were found in several large cohorts of mechanically ventilated patients with COVID-19 pneumonia. In patients with acute respiratory distress syndrome, high doses of sedatives comparable to general anesthesia have been commonly administered without defined EEG endpoints. Continuous limited-channel EEG can reveal pathologic brain states such as burst suppression, that cannot be diagnosed by neurological examination alone. Recent studies documented that machine learning-based analysis of continuous EEG signal is feasible and that this approach can identify burst suppression as well as delirium with high specificity. SUMMARY Preventing oversedation in the ICU remains a challenge. Continuous monitoring of EEG activity, automated EEG analysis, and generation of alerts to clinicians may reduce drug-induced coma and potentially improve patient outcomes.
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Affiliation(s)
- Dusan Hanidziar
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA
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15
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"Listen to Your Immune System When It's Calling for You": Monitoring Autoimmune Diseases Using the iShU App. SENSORS 2022; 22:s22103834. [PMID: 35632243 PMCID: PMC9147288 DOI: 10.3390/s22103834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/10/2022] [Accepted: 05/16/2022] [Indexed: 12/02/2022]
Abstract
The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions.
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16
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Schulz H. The history of sleep research and sleep medicine in Europe. J Sleep Res 2022; 31:e13602. [PMID: 35522132 DOI: 10.1111/jsr.13602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 11/27/2022]
Abstract
Sleep became a subject of scientific research in the second half of the 19th century. Since sleep, unlike other physiological functions, cannot be attributed to a specific organ, there was no distinct method available to study sleep until then. With the development of physiology and psychology, and a rapidly increasing knowledge of the structure and functioning of the nervous system, certain aspects of sleep became accessible to objective study. A first step was to measure responsiveness to external stimuli systematically, during sleep, allowing a first representation of the course of sleep (Schlaftiefe = sleep depth). A second method was to register continuously the motor activity across the sleep-wake cycle, which allowed the documentation in detail of rest-activity patterns of monophasic and polyphasic sleep-wake rhythms, or between day or night active animals. The central measurement for sleep research, however, became the electroencephalogram in the 1930s, which allowed observation of the sleeping brain with high temporal resolution. Beside the development of instruments to measure sleep, prolonged sleep deprivation was applied to study physiological and psychological effects of sleep loss. Another input came from clinical and neuropathological observations of patients with pronounced disorders of the sleep-wake cycle, which for the first time allowed localisation of brain areas that are essentially involved in the regulation of sleep and wakefulness. Experimental brain stimulation and lesion studies were carried out with the same aim at this time. Many of these activities came to a halt on the eve of World War II. It was only in the early 1950s, when periods with rapid eye movements during sleep were recognised, that sleep became a research topic of itself. Jouvet and his team explored the brain mechanisms and transmitters of paradoxical sleep, and experimental sleep research became established in all European countries. Sleep medicine evolving simultaneously in different countries, with early centres in Italy and France. In the late 1960s sleep research and chronobiology began to merge. In recent decades, sleep research, dream research, and sleep medicine have benefited greatly from new methods in genetic research and brain imaging techniques. Genes were identified that are involved in the regulation of sleep, circadian rhythms, or sleep disorders. Functional imaging enabled a high spatial resolution of the activity of the sleeping brain, complementing the high temporal resolution of the electroencephalogram.
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Huang K, Yang Q, Han Y, Zhang Y, Wang Z, Wang L, Wei P. An Easily Compatible Eye-tracking System for Freely-moving Small Animals. Neurosci Bull 2022; 38:661-676. [PMID: 35325370 DOI: 10.1007/s12264-022-00834-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 12/03/2021] [Indexed: 12/13/2022] Open
Abstract
Measuring eye movement is a fundamental approach in cognitive science as it provides a variety of insightful parameters that reflect brain states such as visual attention and emotions. Combining eye-tracking with multimodal neural recordings or manipulation techniques is beneficial for understanding the neural substrates of cognitive function. Many commercially-available and custom-built systems have been widely applied to awake, head-fixed small animals. However, the existing eye-tracking systems used in freely-moving animals are still limited in terms of their compatibility with other devices and of the algorithm used to detect eye movements. Here, we report a novel system that integrates a general-purpose, easily compatible eye-tracking hardware with a robust eye feature-detection algorithm. With ultra-light hardware and a detachable design, the system allows for more implants to be added to the animal's exposed head and has a precise synchronization module to coordinate with other neural implants. Moreover, we systematically compared the performance of existing commonly-used pupil-detection approaches, and demonstrated that the proposed adaptive pupil feature-detection algorithm allows the analysis of more complex and dynamic eye-tracking data in free-moving animals. Synchronized eye-tracking and electroencephalogram recordings, as well as algorithm validation under five noise conditions, suggested that our system is flexibly adaptable and can be combined with a wide range of neural manipulation and recording technologies.
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Affiliation(s)
- Kang Huang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qin Yang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yaning Han
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yulin Zhang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhiyi Wang
- Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China
| | - Liping Wang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pengfei Wei
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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18
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Batista E, Moncusi MA, López-Aguilar P, Martínez-Ballesté A, Solanas A. Sensors for Context-Aware Smart Healthcare: A Security Perspective. SENSORS (BASEL, SWITZERLAND) 2021; 21:6886. [PMID: 34696099 PMCID: PMC8537585 DOI: 10.3390/s21206886] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people's welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.
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Affiliation(s)
- Edgar Batista
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
- SIMPPLE S.L., C. Joan Maragall 1A, 43003 Tarragona, Spain
| | - M. Angels Moncusi
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Pablo López-Aguilar
- Anti-Phishing Working Group EU, Av. Diagonal 621–629, 08028 Barcelona, Spain;
| | - Antoni Martínez-Ballesté
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Agusti Solanas
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
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19
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Patel V, Chesmore A, Legner CM, Pandey S. Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity. ADVANCED INTELLIGENT SYSTEMS 2021. [DOI: 10.1002/aisy.202100099] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Vishal Patel
- Department of Electrical & Computer Engineering Iowa State University 2126 Coover Hall Ames IA 50011 USA
| | - Austin Chesmore
- Department of Electrical & Computer Engineering Iowa State University 2126 Coover Hall Ames IA 50011 USA
| | - Christopher M. Legner
- Department of Electrical & Computer Engineering Iowa State University 2126 Coover Hall Ames IA 50011 USA
| | - Santosh Pandey
- Department of Electrical & Computer Engineering Iowa State University 2126 Coover Hall Ames IA 50011 USA
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20
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Portillo-Lara R, Tahirbegi B, Chapman CAR, Goding JA, Green RA. Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces. APL Bioeng 2021; 5:031507. [PMID: 34327294 PMCID: PMC8294859 DOI: 10.1063/5.0047237] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 05/19/2021] [Indexed: 11/14/2022] Open
Abstract
Brain-computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human-machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.
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Affiliation(s)
- Roberto Portillo-Lara
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Bogachan Tahirbegi
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Christopher A. R. Chapman
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Josef A. Goding
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Rylie A. Green
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
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21
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Experimental Study on Wound Area Measurement with Mobile Devices. SENSORS 2021; 21:s21175762. [PMID: 34502653 PMCID: PMC8433956 DOI: 10.3390/s21175762] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/25/2021] [Accepted: 08/25/2021] [Indexed: 01/26/2023]
Abstract
Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach’s feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.
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22
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Persons AK, Ball JE, Freeman C, Macias DM, Simpson CL, Smith BK, Burch V. RF. Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities. MATERIALS (BASEL, SWITZERLAND) 2021; 14:4070. [PMID: 34361264 PMCID: PMC8347841 DOI: 10.3390/ma14154070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/09/2021] [Accepted: 07/16/2021] [Indexed: 12/23/2022]
Abstract
Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors. Other materials, including textiles, carbon nanotubes, graphene, and conductive metals or inks, may be used in conjunction with polymers to fabricate wearable sensors. Depending on the combination of the materials used, the fatigue behaviors of wearable sensors can vary. Additionally, fatigue testing methodologies for the sensors also vary, with most tests focusing only on the low-cycle fatigue (LCF) regime, and few sensors are cycled until failure or runout are achieved. Fatigue life predictions of wearable sensors are also lacking. These issues make direct comparisons of wearable sensors difficult. To facilitate direct comparisons of wearable sensors and to move proof-of-concept sensors from "bench to bedside", fatigue testing standards should be established. Further, both high-cycle fatigue (HCF) and failure data are needed to determine the appropriateness in the use, modification, development, and validation of fatigue life prediction models and to further the understanding of how cracks initiate and propagate in wearable sensing technologies.
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Affiliation(s)
- Andrea Karen Persons
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA; (A.K.P.); (C.L.S.)
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Boulevard, Starkville, MS 39759, USA;
| | - John E. Ball
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Boulevard, Starkville, MS 39759, USA;
- Department of Electrical and Computer Engineering, Mississippi State University, 406 Hardy Road, Starkville, MS 39762, USA
| | - Charles Freeman
- School of Human Sciences, Mississippi State University, 255 Tracy Drive, Starkville, MS 39762, USA;
| | - David M. Macias
- Department of Kinesiology, Mississippi State University, P.O. Box 6186, Starkville, MS 39762, USA;
- Columbus Orthopaedic Clinic, 670 Leigh Drive, Columbus, MS 39705, USA
| | - Chartrisa LaShan Simpson
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA; (A.K.P.); (C.L.S.)
| | - Brian K. Smith
- Department of Industrial and Systems Engineering, Mississippi State University, 479-2 Hardy Road, Starkville, MS 39762, USA;
| | - Reuben F. Burch V.
- Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, 200 Research Boulevard, Starkville, MS 39759, USA;
- Department of Industrial and Systems Engineering, Mississippi State University, 479-2 Hardy Road, Starkville, MS 39762, USA;
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23
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Intelligent Polymers, Fibers and Applications. Polymers (Basel) 2021; 13:polym13091427. [PMID: 33925249 PMCID: PMC8125737 DOI: 10.3390/polym13091427] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 12/21/2022] Open
Abstract
Intelligent materials, also known as smart materials, are capable of reacting to various external stimuli or environmental changes by rearranging their structure at a molecular level and adapting functionality accordingly. The initial concept of the intelligence of a material originated from the natural biological system, following the sensing–reacting–learning mechanism. The dynamic and adaptive nature, along with the immediate responsiveness, of the polymer- and fiber-based smart materials have increased their global demand in both academia and industry. In this manuscript, the most recent progress in smart materials with various features is reviewed with a focus on their applications in diverse fields. Moreover, their performance and working mechanisms, based on different physical, chemical and biological stimuli, such as temperature, electric and magnetic field, deformation, pH and enzymes, are summarized. Finally, the study is concluded by highlighting the existing challenges and future opportunities in the field of intelligent materials.
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24
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Dhillon NS, Sutandi A, Vishwanath M, Lim MM, Cao H, Si D. A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram. SENSORS 2021; 21:s21082779. [PMID: 33920805 PMCID: PMC8071098 DOI: 10.3390/s21082779] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 12/25/2022]
Abstract
Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16–64 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.
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Affiliation(s)
- Navjodh Singh Dhillon
- Computing and Software Systems, University of Washington, Bothell, WA 98011, USA; (N.S.D.); (A.S.)
| | - Agustinus Sutandi
- Computing and Software Systems, University of Washington, Bothell, WA 98011, USA; (N.S.D.); (A.S.)
| | - Manoj Vishwanath
- Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA;
| | - Miranda M. Lim
- VA Portland Health Care System, Portland, OR 97239, USA;
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Hung Cao
- Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA;
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
- Correspondence: (H.C.); (D.S.); Tel.: +1-949-824-8478 (H.C.); +1-425-352-5389 (D.S.)
| | - Dong Si
- Computing and Software Systems, University of Washington, Bothell, WA 98011, USA; (N.S.D.); (A.S.)
- Correspondence: (H.C.); (D.S.); Tel.: +1-949-824-8478 (H.C.); +1-425-352-5389 (D.S.)
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25
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Teixeira E, Fonseca H, Diniz-Sousa F, Veras L, Boppre G, Oliveira J, Pinto D, Alves AJ, Barbosa A, Mendes R, Marques-Aleixo I. Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review. Geriatrics (Basel) 2021; 6:38. [PMID: 33917104 PMCID: PMC8167657 DOI: 10.3390/geriatrics6020038] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/25/2021] [Accepted: 04/02/2021] [Indexed: 01/22/2023] Open
Abstract
The availability of wearable devices (WDs) to collect biometric information and their use during activities of daily living is significantly increasing in the general population. These small electronic devices, which record fitness and health-related outcomes, have been broadly utilized in industries such as medicine, healthcare, and fitness. Since they are simple to use and progressively cheaper, they have also been used for numerous research purposes. However, despite their increasing popularity, most of these WDs do not accurately measure the proclaimed outcomes. In fact, research is equivocal about whether they are valid and reliable methods to specifically evaluate physical activity and health-related outcomes in older adults, since they are mostly designed and produced considering younger subjects' physical and mental characteristics. Additionally, their constant evolution through continuous upgrades and redesigned versions, suggests the need for constant up-to-date reviews and research. Accordingly, this article aims to scrutinize the state-of-the-art scientific evidence about the usefulness of WDs, specifically on older adults, to monitor physical activity and health-related outcomes. This critical review not only aims to inform older consumers but also aid researchers in study design when selecting physical activity and healthcare monitoring devices for elderly people.
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Affiliation(s)
- Eduardo Teixeira
- Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Faculty of Psychology, Education and Sports, Lusófona University of Porto, 4000-098 Porto, Portugal
- Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
| | - Hélder Fonseca
- Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
| | - Florêncio Diniz-Sousa
- Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
| | - Lucas Veras
- Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
| | - Giorjines Boppre
- Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
| | - José Oliveira
- Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
| | - Diogo Pinto
- Research Center in Sports Sciences, Health Sciences and Human Development (CIDESD), University Institute of Maia, 4475-690 Maia, Portugal
| | - Alberto Jorge Alves
- Research Center in Sports Sciences, Health Sciences and Human Development (CIDESD), University Institute of Maia, 4475-690 Maia, Portugal
| | - Ana Barbosa
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
- EPIUnit-Instituto de Saúde Pública, Universidade do Porto, 4050-091 Porto, Portugal
| | - Romeu Mendes
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
- EPIUnit-Instituto de Saúde Pública, Universidade do Porto, 4050-091 Porto, Portugal
- Northern Region Health Administration, 4000-477 Porto, Portugal
| | - Inês Marques-Aleixo
- Research Centre in Physical Activity, Health, and Leisure (CIAFEL), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
- Faculty of Psychology, Education and Sports, Lusófona University of Porto, 4000-098 Porto, Portugal
- Laboratory for Integrative and Translational Research in Population Health (ITR), 4050-600 Porto, Portugal
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Hendriks MMS, van Lotringen JH, Vos-van der Hulst M, Keijsers NLW. Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study. JMIR Mhealth Uhealth 2021; 9:e24339. [PMID: 33555268 PMCID: PMC7971768 DOI: 10.2196/24339] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/13/2020] [Accepted: 01/05/2021] [Indexed: 11/29/2022] Open
Abstract
Background Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. Objective The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. Methods Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. Results In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ≤120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=−2.1, P=.04) and movement activity (t18=−1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. Conclusions It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants.
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Affiliation(s)
- Maartje M S Hendriks
- Department of Research, Sint Maartenskliniek, Nijmegen, Netherlands.,Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | | | | | - Noël L W Keijsers
- Department of Research, Sint Maartenskliniek, Nijmegen, Netherlands.,Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
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Suwazono S, Arao H. A newly developed free software tool set for averaging electroencephalogram implemented in the Perl programming language. Heliyon 2020; 6:e05580. [PMID: 33294707 PMCID: PMC7701343 DOI: 10.1016/j.heliyon.2020.e05580] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/05/2020] [Accepted: 11/19/2020] [Indexed: 11/24/2022] Open
Abstract
Background Considering the need for daily activity analysis of older adults, development of easy-to-use, free electroencephalogram (EEG) analysis tools are desired in order to decrease barriers to accessing this technology and increase the entry of a wide range of new researchers. New method We describe a newly developed tool set for EEG analysis, enabling import, average, waveform display and iso-potential scalp topographies, utilizing the programming language Perl. Results The basic processing, including average, display waveforms, and isopotential scalp topography was implemented in the current system. The validation was examined by making difference waveforms between the results using the current analysis system and a commercial software. Comparison with Existing Method(s): The current software tool set consists of free software. The scripts are easily editable by any user and there are no black boxes. Conclusions The currently reported procedures provide an easy-to-begin, flexible, readable, easy-to-modify basic tool set for EEG analysis and is expected to recruit new EEG researchers.
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Affiliation(s)
- Shugo Suwazono
- Center for Clinical Neuroscience, National Hospital Organization Okinawa National Hospital, Japan
| | - Hiroshi Arao
- Department of Human Sciences, Taisho University, Japan
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Sustainable Digital Transformation of Disaster Risk—Integrating New Types of Digital Social Vulnerability and Interdependencies with Critical Infrastructure. SUSTAINABILITY 2020. [DOI: 10.3390/su12229324] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This article explores the relationship between digital transformation and disaster risk. Vulnerability studies aim at differentiating impacts and losses by using fine-grained information from demographic, social, and personal characteristics of humans. With ongoing digital development, these characteristics will transform and result in new traits, which need to be identified and integrated. Digital transformations will produce new social groups, partly human, semi-human, or non-human—some of which already exist, and some which can be foreseen by extrapolating from recent developments in the field of brain wearables, robotics, and software engineering. Though involved in the process of digital transformation, many researchers and practitioners in the field of Disaster Risk Reduction or Climate Change Adaptation are not yet aware of the repercussions for disaster and vulnerability assessments. Emerging vulnerabilities are due to a growing dependency on digital services and tools in the case of a severe emergency or crisis. This article depicts the different implications for future theoretical frameworks when identifying novel semi-human groups and their vulnerabilities to disaster risks. Findings include assumed changes within common indicators of social vulnerability, new indicators, a typology of humans, and human interrelations with digital extensions and two different perspectives on these groups and their dependencies with critical infrastructure.
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Poolakkandy RR, Menamparambath MM. Transition metal oxide based non‐enzymatic electrochemical sensors: An arising approach for the meticulous detection of neurotransmitter biomarkers. ELECTROCHEMICAL SCIENCE ADVANCES 2020. [DOI: 10.1002/elsa.202000024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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Graña Possamai C, Ravaud P, Ghosn L, Tran VT. Use of wearable biometric monitoring devices to measure outcomes in randomized clinical trials: a methodological systematic review. BMC Med 2020; 18:310. [PMID: 33153462 PMCID: PMC7646072 DOI: 10.1186/s12916-020-01773-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 09/01/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Wearable biometric monitoring devices (BMDs) have the potential to transform the conduct of randomized controlled trials (RCTs) by shifting the collection of outcome data from single measurements at predefined time points to dense continuous measurements. METHODS Methodological systematic review to understand how recent RCTs used BMDs to measure outcomes and to describe the reporting of these RCTs. Electronic search was performed in the Cochrane Central Register of Controlled Trials, PubMed, and EMBASE and completed a page-by-page hand search in five leading medical journals between January 1, 2018, and December 31, 2018. Three reviewers independently extracted all primary and secondary outcomes collected using BMDs, and assessed (1) the definitions used to summarize BMD outcome data; (2) whether the validity, reliability, and responsiveness of sensors was reported; (3) the discrepancy with outcomes prespecified in public clinical trial registries; and (4) the methods used to manage missing and incomplete BMD outcome data. RESULTS Of the 4562 records screened, 75 RCTs were eligible. Among them, 24% tested a pharmacological intervention and 57% used an inertial measurement sensor to measure physical activity. Included trials involved 464 outcomes (average of 6 [SD = 8] outcomes per trial). In total, 35 trials used a BMD to measure a primary outcome. Several issues affected the value and transparency of trials using BMDs to measure outcomes. First, the definition of outcomes used in the trials was highly heterogeneous (e.g., 21 diabetes trials had 266 outcomes and 153 had different unique definitions to measure diabetes control), which limited the combination and comparison of results. Second, information on the validity, reliability, and responsiveness of sensors used was lacking in 74% of trials. Third, half (53%) of the outcomes measured with BMDs had not been prespecified, with a high risk of outcome reporting bias. Finally, reporting on the management of incomplete outcome data (e.g., due to suboptimal compliance with the BMD) was absent in 68% of RCTs. CONCLUSIONS Use of BMDs to measure outcomes is becoming the norm rather than the exception in many fields. Yet, trialists need to account for several methodological issues when specifying and conducting RCTs using these novel tools.
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Affiliation(s)
- Carolina Graña Possamai
- METHODS Team, Center for Research in Epidemiology and Statistics (CRESS), Université de Paris/INSERM (UMR 1153), 1 Place du Parvis Notre Dame, 75004, Paris, France
| | - Philippe Ravaud
- METHODS Team, Center for Research in Epidemiology and Statistics (CRESS), Université de Paris/INSERM (UMR 1153), 1 Place du Parvis Notre Dame, 75004, Paris, France.,Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu (AP-HP), 1 Place du Parvis Notre Dame, 75004, Paris, France.,Department of Epidemiology, Columbia University Mailman School of Public Health, 22 W 168th St, New York, NY, USA
| | - Lina Ghosn
- METHODS Team, Center for Research in Epidemiology and Statistics (CRESS), Université de Paris/INSERM (UMR 1153), 1 Place du Parvis Notre Dame, 75004, Paris, France.,Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu (AP-HP), 1 Place du Parvis Notre Dame, 75004, Paris, France
| | - Viet-Thi Tran
- METHODS Team, Center for Research in Epidemiology and Statistics (CRESS), Université de Paris/INSERM (UMR 1153), 1 Place du Parvis Notre Dame, 75004, Paris, France. .,Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu (AP-HP), 1 Place du Parvis Notre Dame, 75004, Paris, France.
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Topalovic U, Aghajan ZM, Villaroman D, Hiller S, Christov-Moore L, Wishard TJ, Stangl M, Hasulak NR, Inman CS, Fields TA, Rao VR, Eliashiv D, Fried I, Suthana N. Wireless Programmable Recording and Stimulation of Deep Brain Activity in Freely Moving Humans. Neuron 2020; 108:322-334.e9. [PMID: 32946744 PMCID: PMC7785319 DOI: 10.1016/j.neuron.2020.08.021] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 07/11/2020] [Accepted: 08/20/2020] [Indexed: 12/29/2022]
Abstract
Uncovering the neural mechanisms underlying human natural ambulatory behavior is a major challenge for neuroscience. Current commercially available implantable devices that allow for recording and stimulation of deep brain activity in humans can provide invaluable intrinsic brain signals but are not inherently designed for research and thus lack flexible control and integration with wearable sensors. We developed a mobile deep brain recording and stimulation (Mo-DBRS) platform that enables wireless and programmable intracranial electroencephalographic recording and electrical stimulation integrated and synchronized with virtual reality/augmented reality (VR/AR) and wearables capable of external measurements (e.g., motion capture, heart rate, skin conductance, respiration, eye tracking, and scalp EEG). When used in freely moving humans with implanted neural devices, this platform is adaptable to ecologically valid environments conducive to elucidating the neural mechanisms underlying naturalistic behaviors and to the development of viable therapies for neurologic and psychiatric disorders.
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Affiliation(s)
- Uros Topalovic
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Zahra M Aghajan
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Diane Villaroman
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sonja Hiller
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Leonardo Christov-Moore
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Tyler J Wishard
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Matthias Stangl
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | | | - Cory S Inman
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA
| | - Tony A Fields
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Dawn Eliashiv
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Itzhak Fried
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA; Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Tel Aviv Sourasky Medical Center and Sackler Faculty School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90024, USA; Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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Mondal S, Zehra N, Choudhury A, Iyer PK. Wearable Sensing Devices for Point of Care Diagnostics. ACS APPLIED BIO MATERIALS 2020; 4:47-70. [DOI: 10.1021/acsabm.0c00798] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Subrata Mondal
- Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India
| | - Nehal Zehra
- Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India
| | - Anwesha Choudhury
- Center for Nanotechnology, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India
| | - Parameswar Krishnan Iyer
- Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India
- Center for Nanotechnology, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India
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Shinohara S, Toda H, Nakamura M, Omiya Y, Higuchi M, Takano T, Saito T, Tanichi M, Boku S, Mitsuyoshi S, So M, Yoshino A, Tokuno S. Evaluation of the Severity of Major Depression Using a Voice Index for Emotional Arousal. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5041. [PMID: 32899881 PMCID: PMC7570922 DOI: 10.3390/s20185041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/03/2020] [Accepted: 09/03/2020] [Indexed: 11/16/2022]
Abstract
Recently, the relationship between emotional arousal and depression has been studied. Focusing on this relationship, we first developed an arousal level voice index (ALVI) to measure arousal levels using the Interactive Emotional Dyadic Motion Capture database. Then, we calculated ALVI from the voices of depressed patients from two hospitals (Ginza Taimei Clinic (H1) and National Defense Medical College hospital (H2)) and compared them with the severity of depression as measured by the Hamilton Rating Scale for Depression (HAM-D). Depending on the HAM-D score, the datasets were classified into a no depression (HAM-D < 8) and a depression group (HAM-D ≥ 8) for each hospital. A comparison of the mean ALVI between the groups was performed using the Wilcoxon rank-sum test and a significant difference at the level of 10% (p = 0.094) at H1 and 1% (p = 0.0038) at H2 was determined. The area under the curve (AUC) of the receiver operating characteristic was 0.66 when categorizing between the two groups for H1, and the AUC for H2 was 0.70. The relationship between arousal level and depression severity was indirectly suggested via the ALVI.
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Affiliation(s)
- Shuji Shinohara
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; (M.N.); (M.H.); (S.M.); (S.T.)
| | - Hiroyuki Toda
- Department of Psychiatry, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama 359-8513, Japan; (H.T.); (T.S.); (M.T.); (A.Y.)
| | - Mitsuteru Nakamura
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; (M.N.); (M.H.); (S.M.); (S.T.)
| | - Yasuhiro Omiya
- PST Inc., Industry & Trade Center Building 905, 2 Yamashita-cho, Naka-ku, Yokohama, Kanagawa 231-0023, Japan; (Y.O.); (T.T.)
| | - Masakazu Higuchi
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; (M.N.); (M.H.); (S.M.); (S.T.)
| | - Takeshi Takano
- PST Inc., Industry & Trade Center Building 905, 2 Yamashita-cho, Naka-ku, Yokohama, Kanagawa 231-0023, Japan; (Y.O.); (T.T.)
| | - Taku Saito
- Department of Psychiatry, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama 359-8513, Japan; (H.T.); (T.S.); (M.T.); (A.Y.)
| | - Masaaki Tanichi
- Department of Psychiatry, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama 359-8513, Japan; (H.T.); (T.S.); (M.T.); (A.Y.)
| | - Shuken Boku
- Department of Neuropsychiatry, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto 860-8556, Japan;
| | - Shunji Mitsuyoshi
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; (M.N.); (M.H.); (S.M.); (S.T.)
| | - Mirai So
- Department of Psychiatry, Tokyo Dental College, 2-9-18, Misakicho, Chiyoda-ku, Tokyo 101-0061, Japan;
| | - Aihide Yoshino
- Department of Psychiatry, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama 359-8513, Japan; (H.T.); (T.S.); (M.T.); (A.Y.)
| | - Shinichi Tokuno
- Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan; (M.N.); (M.H.); (S.M.); (S.T.)
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Kelleher BL, Halligan T, Witthuhn N, Neo WS, Hamrick L, Abbeduto L. Bringing the Laboratory Home: PANDABox Telehealth-Based Assessment of Neurodevelopmental Risk in Children. Front Psychol 2020; 11:1634. [PMID: 32849001 PMCID: PMC7399221 DOI: 10.3389/fpsyg.2020.01634] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 06/16/2020] [Indexed: 12/18/2022] Open
Abstract
Background Advances in clinical trials have revealed a pressing need for outcome measures appropriate for children with neurogenetic syndromes (NGS). However, the field lacks a standardized, flexible protocol for collecting laboratory-grade experimental data remotely. To address this challenge, we developed PANDABox (Parent-Administered Neurodevelopmental Assessment), a caregiver-facilitated, remotely administered assessment protocol for collecting integrated and high quality clinical, behavioral, and spectral data relevant to a wide array of research questions. Here, we describe PANDABox development and report preliminary data regarding: (1) logistics and cost, (2) caregiver fidelity and satisfaction, and (3) data quality. Methods We administered PANDABox to a cohort of 16 geographically diverse caregivers and their infants with Down syndrome. Tasks assessed attention, language, motor, and atypical behaviors. Behavioral and physiological data were synchronized and coded offline by trained research assistants. Results PANDABox required low resources to administer and was well received by families, with high caregiver fidelity (94%) and infant engagement (91%), as well as high caregiver-reported satisfaction (97% positive). Missing data rates were low for video frames (3%) and vocalization recordings (6%) but were higher for heart rate (25% fully missing and 13% partially missing) and discrete behavioral presses (8% technical issues and 19% not enough codable behavior), reflecting the increased technical demands for these activities. Conclusion With further development, low-cost laboratory-grade research protocols may be remotely administered by caregivers in the family home, opening a new frontier for cost-efficient, scalable assessment studies for children with NGS other neurodevelopmental disorders.
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Affiliation(s)
- Bridgette L Kelleher
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States
| | - Taylor Halligan
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States
| | - Nicole Witthuhn
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States
| | - Wei Siong Neo
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States
| | - Lisa Hamrick
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States
| | - Leonard Abbeduto
- Department of Psychiatry and Behavioral Sciences, MIND Institute, University of California, Davis, Davis, CA, United States
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An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors. Symmetry (Basel) 2020. [DOI: 10.3390/sym12030460] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Intimate Partner Violence (IPV) dramatically compromises the free and complete development of many women around the world, therefore leading to social asymmetry regarding the right to personal safety. In many cases, a woman who has reported her partner to police for gender-based violence needs to ensure her protection (either before the trial of the aggressor or after their freedom). Thus, it would be ideal if autonomous alarm systems could be developed in order to call the police if necessary. Up to now, many proposals have been presented in this regard, including solutions based on Information and Communication Technologies (ICT) but, unfortunately, these approaches usually rely on the active participation of the victims (survivors), who have to turn the system on by themselves if needed. Therefore, in order to overcome such limitations, in this work, a passive continuous monitoring system is proposed which uses biosensors attached to the survivor as well as machine learning techniques to infer if an abnormal situation related to gender-based violence is taking place, activating in this case an alarm. The monitoring structure of the system supervises a great deal of bio-signals according to the current status of technology of wearables and biomedical devices. The presented biosensors-based surveillance solution can also be manually disconnected for 30/60/90 min (on demand) in order to avoid false positives when a woman is, for example, practicing sports or carrying out other inoffensive activities that could incorrectly activate the alarm.
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Verhulst N, De Keyser A, Gustafsson A, Shams P, Van Vaerenbergh Y. Neuroscience in service research: an overview and discussion of its possibilities. JOURNAL OF SERVICE MANAGEMENT 2019. [DOI: 10.1108/josm-05-2019-0135] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Purpose
The purpose of this paper is to discuss recent developments in neuroscientific methods and demonstrate its potential for the service field. This work is a call to action for more service researchers to adopt promising and increasingly accessible neuro-tools that allow the service field to benefit from neuroscience theories and insights.
Design/methodology/approach
The paper synthesizes key literature from a variety of domains (e.g. neuroscience, consumer neuroscience and organizational neuroscience) to provide an in-depth background to start applying neuro-tools. Specifically, this paper outlines the most important neuro-tools today and discusses their theoretical and empirical value.
Findings
To date, the use of neuro-tools in the service field is limited. This is surprising given the great potential they hold to advance service research. To stimulate the use of neuro-tools in the service area, the authors provide a roadmap to enable neuroscientific service studies and conclude with a discussion on promising areas (e.g. service experience and servicescape) ripe for neuroscientific input.
Originality/value
The paper offers service researchers a starting point to understand the potential benefits of adopting the neuroscientific method and shows their complementarity with traditional service research methods like surveys, experiments and qualitative research. In addition, this paper may also help reviewers and editors to better assess the quality of neuro-studies in service.
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Abstract
Recent applications of eye tracking for diagnosis, prognosis and follow-up of therapy in age-related neurological or psychological deficits have been reviewed. The review is focused on active aging, neurodegeneration and cognitive impairments. The potential impacts and current limitations of using characterizing features of eye movements and pupillary responses (oculometrics) as objective biomarkers in the context of aging are discussed. A closer look into the findings, especially with respect to cognitive impairments, suggests that eye tracking is an invaluable technique to study hidden aspects of aging that have not been revealed using any other noninvasive tool. Future research should involve a wider variety of oculometrics, in addition to saccadic metrics and pupillary responses, including nonlinear and combinatorial features as well as blink- and fixation-related metrics to develop biomarkers to trace age-related irregularities associated with cognitive and neural deficits.
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Affiliation(s)
- Ramtin Z Marandi
- Department of Health Science & Technology, Aalborg University, Aalborg E 9220, Denmark
| | - Parisa Gazerani
- Department of Health Science & Technology, Aalborg University, Aalborg E 9220, Denmark
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Abstract
Statistics plays three important roles in brain studies. They are (1) the study of differences between brains in distinctive populations; (2) the study of the variability in the structure and functioning of the brain; and (3) the study of data reduction on large-scale brain data. I discuss these concepts using examples from past and ongoing research in brain connectivity, brain information flow, information extraction from large-scale neuroimaging data, and neural predictive modeling. Having dispensed with the past, I attempt to present a few areas where statistical science facilitates brain decoding and to write prospectively, in the light of present knowledge and in the quest for artificial intelligence, about questions that statistical and neurobiological communities could work closely together to address in the future.
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Lohani M, Payne BR, Strayer DL. A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving. Front Hum Neurosci 2019; 13:57. [PMID: 30941023 PMCID: PMC6434408 DOI: 10.3389/fnhum.2019.00057] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 02/01/2019] [Indexed: 11/13/2022] Open
Abstract
As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.
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Affiliation(s)
- Monika Lohani
- Department of Educational Psychology, University of Utah, Salt Lake City, UT, United States
| | - Brennan R. Payne
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - David L. Strayer
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
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Bell L, Vogt J, Willemse C, Routledge T, Butler LT, Sakaki M. Beyond Self-Report: A Review of Physiological and Neuroscientific Methods to Investigate Consumer Behavior. Front Psychol 2018; 9:1655. [PMID: 30245657 PMCID: PMC6137131 DOI: 10.3389/fpsyg.2018.01655] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Accepted: 08/17/2018] [Indexed: 11/13/2022] Open
Abstract
The current paper investigates the value and application of a range of physiological and neuroscientific techniques in applied marketing research and consumer science, highlighting new insights from research in social psychology and neuroscience. We review measures of sweat secretion, heart rate, facial muscle activity, eye movements, and electrical brain activity, using techniques including skin conductance, pupillometry, eyetracking, and magnetic brain imaging. For each measure, after a brief explanation of the underlying technique, we illustrate concepts and mechanisms that the measure allows researchers in marketing and consumer science to investigate, with a focus on consumer attitudes and behavior. By providing reviews on recent research that applied these methods in consumer science and relevant related fields, we also highlight methodological and theoretical strengths and limitations, with an emphasis on ecological validity. We argue that the inclusion of physiological and neuroscientific techniques can advance consumer research by providing insights into the often unconscious mechanisms underlying consumer behavior. Therefore, such technologies can help researchers and marketing practitioners understand the mechanisms of consumer behavior and improve predictions of consumer behavior.
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Affiliation(s)
- Lynne Bell
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
| | - Julia Vogt
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
| | | | | | - Laurie T. Butler
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
| | - Michiko Sakaki
- School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
- Research Institute, Kochi University of Technology, Kami, Japan
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Smith B, Sverdlov A. Digital Technology: The Future Is Bright. Clin Pharmacol Ther 2018; 104:9-11. [PMID: 29890004 DOI: 10.1002/cpt.1092] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 04/17/2018] [Indexed: 11/06/2022]
Affiliation(s)
- Brian Smith
- Novartis Institutes of Biomedical Research, Cambridge, Massachusetts, USA
| | - Alex Sverdlov
- Novartis Pharmaceuticals, Cambridge, Massachusetts, USA
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