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Wang W, den Brinker AC, Stuijk S, de Haan G. Robust heart rate from fitness videos. Physiol Meas 2017; 38:1023-1044. [DOI: 10.1088/1361-6579/aa6d02] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Shao D, Tsow F, Liu C, Yang Y, Tao N. Simultaneous Monitoring of Ballistocardiogram and Photoplethysmogram Using a Camera. IEEE Trans Biomed Eng 2017; 64:1003-1010. [PMID: 27362754 PMCID: PMC5523454 DOI: 10.1109/tbme.2016.2585109] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present a noncontact method to measure ballistocardiogram (BCG) and photoplethysmogram (PPG) simultaneously using a single camera. The method tracks the motion of facial features to determine displacement BCG, and extracts the corresponding velocity and acceleration BCGs by taking first and second temporal derivatives from the displacement BCG, respectively. The measured BCG waveforms are consistent with those reported in the literature and also with those recorded with an accelerometer-based reference method. The method also tracks PPG based on the reflected light from the same facial region, which makes it possible to track both BCG and PPG with the same optics. We verify the robustness and reproducibility of the noncontact method with a small pilot study with 23 subjects. The presented method is the first demonstration of simultaneous BCG and PPG monitoring without wearing any extra equipment or marker by the subject.
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Affiliation(s)
- Dangdang Shao
- Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Francis Tsow
- Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Chenbin Liu
- School of Chemistry & Chemical Engineering, Nanjing University, Nanjing, Jiangsu 210093, China
| | - Yuting Yang
- School of Chemistry & Chemical Engineering, Nanjing University, Nanjing, Jiangsu 210093, China
| | - Nongjian Tao
- Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA, and School of Chemistry & Chemical Engineering, Nanjing University, Nanjing, Jiangsu 210093, China
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Natarajan A, Xu KS, Eriksson B. Detecting divisions of the autonomic nervous system using wearables. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:5761-5764. [PMID: 28269563 DOI: 10.1109/embc.2016.7592036] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The ability to assess a user's emotional reaction from biometrics has applications in personalization, recommendation, and enhancing user experiences, among other areas. Unfortunately, understanding the connection between biometric signals and user reactions has previously focused on black box techniques that are opaque to the underlying physiology of the user. In this paper, we explore a novel user study connecting biometric reaction to external stimuli and changes in the user's autonomic nervous system. Specifically, we focus on two competing responses, namely the sympathetic and parasympathetic nervous system, and how differing activations are related to different user responses. Our experiments demonstrate how prior psychophysiological research distinguishing this activation can be replicated using biometric data collected from wearables. The insights from this work have applications in better understanding emotional state from biometric sensors.
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W Adams Z, McClure EA, Gray KM, Danielson CK, Treiber FA, Ruggiero KJ. Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. J Psychiatr Res 2017; 85:1-14. [PMID: 27814455 PMCID: PMC5191962 DOI: 10.1016/j.jpsychires.2016.10.019] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 10/19/2016] [Accepted: 10/20/2016] [Indexed: 01/08/2023]
Abstract
Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders.
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Affiliation(s)
- Zachary W Adams
- Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, 67 President Street, Charleston, SC, USA; Department of Psychiatry, Indiana University School of Medicine, 410 West 10th Street, Indianapolis, IN, USA.
| | - Erin A McClure
- Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, 67 President Street, Charleston, SC, USA
| | - Kevin M Gray
- Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, 67 President Street, Charleston, SC, USA
| | - Carla Kmett Danielson
- Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, 67 President Street, Charleston, SC, USA
| | - Frank A Treiber
- Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, 67 President Street, Charleston, SC, USA; Technology Applications Center for Healthful Lifestyles, College of Nursing, Medical University of South Carolina, 99 Jonathan Lucas Street, Charleston, SC, USA
| | - Kenneth J Ruggiero
- Technology Applications Center for Healthful Lifestyles, College of Nursing, Medical University of South Carolina, 99 Jonathan Lucas Street, Charleston, SC, USA; Ralph H. Johnson VA Medical Center, 109 Bee Street, Charleston, SC, USA
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McDuff DJ, Estepp JR, Piasecki AM, Blackford EB. A survey of remote optical photoplethysmographic imaging methods. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:6398-404. [PMID: 26737757 DOI: 10.1109/embc.2015.7319857] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.
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Abstract
This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method, where a projection plane orthogonal to the skin tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model.
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García-Betances RI, Arredondo Waldmeyer MT, Fico G, Cabrera-Umpiérrez MF. A succinct overview of virtual reality technology use in Alzheimer's disease. Front Aging Neurosci 2015; 7:80. [PMID: 26029101 PMCID: PMC4428215 DOI: 10.3389/fnagi.2015.00080] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Accepted: 04/26/2015] [Indexed: 12/20/2022] Open
Abstract
We provide a brief review and appraisal of recent and current virtual reality (VR) technology for Alzheimer’s disease (AD) applications. We categorize them according to their intended purpose (e.g., diagnosis, patient cognitive training, caregivers’ education, etc.), focus feature (e.g., spatial impairment, memory deficit, etc.), methodology employed (e.g., tasks, games, etc.), immersion level, and passive or active interaction. Critical assessment indicates that most of them do not yet take full advantage of virtual environments with high levels of immersion and interaction. Many still rely on conventional 2D graphic displays to create non-immersive or semi-immersive VR scenarios. Important improvements are needed to make VR a better and more versatile assessment and training tool for AD. The use of the latest display technologies available, such as emerging head-mounted displays and 3D smart TV technologies, together with realistic multi-sensorial interaction devices, and neuro-physiological feedback capacity, are some of the most beneficial improvements this mini-review suggests. Additionally, it would be desirable that such VR applications for AD be easily and affordably transferable to in-home and nursing home environments.
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Affiliation(s)
- Rebeca I García-Betances
- Life Supporting Technologies (LifeSTech), ETSI Telecomunicaciones, Universidad Politécnica de Madrid , Madrid , Spain
| | | | - Giuseppe Fico
- Life Supporting Technologies (LifeSTech), ETSI Telecomunicaciones, Universidad Politécnica de Madrid , Madrid , Spain
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