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Kageyama I, Hashiguchi N, Cao J, Niwa M, Lim Y, Tsutsumi M, Yu J, Sengoku S, Okamoto S, Hashimoto S, Kodama K. Determination of Waste Management Workers' Physical and Psychological Load: A Cross-Sectional Study Using Biometric Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192315964. [PMID: 36498046 PMCID: PMC9739088 DOI: 10.3390/ijerph192315964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/18/2022] [Accepted: 11/23/2022] [Indexed: 06/13/2023]
Abstract
Waste management workers experience high stress and physical strain in their work environment, but very little empirical evidence supports effective health management practices for waste management workers. Hence, this study investigated the effects of worker characteristics and biometric indices on workers' physical and psychological loads during waste-handling operations. A biometric measurement system was installed in an industrial waste management facility in Japan to understand the actual working conditions of 29 workers in the facility. It comprised sensing wear for data collection and biometric sensors to measure heart rate (HR) and physical activity (PA) based on electrocardiogram signals. Multiple regression analysis was performed to evaluate significant relationships between the parameters. Although stress level is indicated by the ratio of low frequency (LF) to high frequency (HF) or high LF power in HR, the results showed that compared with workers who did not handle waste, those who did had lower PA and body surface temperature, higher stress, and lower HR variability parameters associated with higher psychological load. There were no significant differences in HR, heart rate interval (RRI), and workload. The psychological load of workers dealing directly with waste was high, regardless of their PA, whereas others had a low psychological load even with high PA. These findings suggest the need to promote sustainable work relationships and a quantitative understanding of harsh working conditions to improve work quality and reduce health hazards.
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Affiliation(s)
- Itsuki Kageyama
- Graduate School of Technology Management, Ritsumeikan University, 2-150 Iwakuracho, Osaka 567-8570, Japan
- Merge System Co., Fukuoka 810-0041, Japan
| | - Nobuki Hashiguchi
- Graduate School of Technology Management, Ritsumeikan University, 2-150 Iwakuracho, Osaka 567-8570, Japan
| | - Jianfei Cao
- Graduate School of Technology Management, Ritsumeikan University, 2-150 Iwakuracho, Osaka 567-8570, Japan
| | - Makoto Niwa
- Graduate School of Technology Management, Ritsumeikan University, 2-150 Iwakuracho, Osaka 567-8570, Japan
| | - Yeongjoo Lim
- Graduate School of Technology Management, Ritsumeikan University, 2-150 Iwakuracho, Osaka 567-8570, Japan
| | | | - Jiakan Yu
- School of Environment and Society, Tokyo Institute of Technology, Tokyo 108-0023, Japan
| | - Shintaro Sengoku
- School of Environment and Society, Tokyo Institute of Technology, Tokyo 108-0023, Japan
| | - Soichiro Okamoto
- College of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Shiga 525-8577, Japan
| | - Seiji Hashimoto
- College of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Shiga 525-8577, Japan
| | - Kota Kodama
- Graduate School of Technology Management, Ritsumeikan University, 2-150 Iwakuracho, Osaka 567-8570, Japan
- Center for Research and Education on Drug Discovery, The Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan
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Hashiguchi N, Cao J, Lim Y, Kuroishi S, Miyazaki Y, Kitahara S, Sengoku S, Matsubayashi K, Kodama K. Psychological Effects of Heart Rate and Physical Vibration on the Operation of Construction Machines: Experimental Study. JMIR Mhealth Uhealth 2021; 9:e31637. [PMID: 34524105 PMCID: PMC8482169 DOI: 10.2196/31637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/29/2021] [Accepted: 07/31/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND A construction method has emerged in which a camera is installed around a construction machine, and the operator remotely controls the machine while synchronizing the vibration of the machine with the images seen from the operator's seat using virtual reality (VR) technology. Indices related to changes in heart rate (HR) and physical vibration, such as heart rate variability (HRV) and multiscale entropy (MSE), can then be measured among the operators. As these indices are quantitative measures of autonomic regulation in the cardiovascular system, they can provide a useful means of assessing operational stress. OBJECTIVE In this study, we aimed to evaluate changes in HR and body vibration of machine operators and investigate appropriate methods of machine operation while considering the psychological load. METHODS We enrolled 9 remote operators (18-50 years old) in the experiment, which involved 42 measurements. A construction machine was driven on a test course simulating a construction site, and three patterns of operation-riding operation, remote operation using monitor images, and VR operation combining monitor images and machine vibration-were compared. The heartbeat, body vibration, and driving time of the participants were measured using sensing wear made of a woven film-like conductive material and a three-axis acceleration measurement device (WHS-2). We used HRV analysis in the time and frequency domains, MSE analysis as a measure of the complexity of heart rate changes, and the ISO (International Standards Organization) 2631 vibration index. Multiple regression analysis was conducted to model the relationship among the low frequency (LF)/high frequency (HF) HRV, MSE, vibration index, and driving time of construction equipment. Efficiency in driving time was investigated with a focus on stress reduction. RESULTS Multiple comparisons conducted via the Bonferroni test and Kruskal-Wallis test showed statistically significant differences (P=.05) in HRV-LF/HF, the vibration index, weighted acceleration, motion sickness dose value (MSDVz), and the driving time among the three operation patterns. The riding operation was found to reduce the driving time of the machine, but the operation stress was the highest in this case; operation based on the monitor image was found to have the lowest operation stress but the longest operation time. Multiple regression analysis showed that the explanatory variables (LH/HF), RR interval, and vibration index (MSDVz by vertical oscillation at 0.5-5 Hz) had a negative effect on the driving time (adjusted coefficient of determination R2=0.449). CONCLUSIONS A new method was developed to calculate the appropriate operating time by considering operational stress and suppressing the physical vibration within an acceptable range. By focusing on the relationship between psychological load and physical vibration, which has not been explored in previous studies, the relationship of these variables with the driving time of construction machines was clarified.
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Affiliation(s)
- Nobuki Hashiguchi
- Graduate School of Technology Management, Ritsumeikan University, Ibaraki, Japan
| | - Jianfei Cao
- Graduate School of Technology Management, Ritsumeikan University, Ibaraki, Japan
| | - Yeongjoo Lim
- Faculty of Business Administration, Ritsumeikan University, Ibaraki, Japan
| | - Shinichi Kuroishi
- Metropolitan Area Branch Civil Engineering Department, Kumagai Gumi Co, Ltd, Shinjuku-ku, Japan
| | - Yasuhiro Miyazaki
- Civil Engineering Business Headquarters, Kumagai Gumi Co, Ltd, Shinjuku-ku, Japan
| | - Shigeo Kitahara
- Civil Engineering Business Headquarters, Kumagai Gumi Co, Ltd, Shinjuku-ku, Japan
| | - Shintaro Sengoku
- School of Environment and Society, Tokyo Institute of Technology, Minato-ku, Japan
| | | | - Kota Kodama
- Graduate School of Technology Management, Ritsumeikan University, Ibaraki, Japan
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Chen YS, Lu WA, Pagaduan JC, Kuo CD. A Novel Smartphone App for the Measurement of Ultra-Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study. JMIR Mhealth Uhealth 2020; 8:e18761. [PMID: 32735219 PMCID: PMC7428904 DOI: 10.2196/18761] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/05/2020] [Accepted: 06/13/2020] [Indexed: 01/05/2023] Open
Abstract
Background Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined. Objective The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP. Methods In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis. Results Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF. Conclusions Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement.
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Affiliation(s)
- Yung-Sheng Chen
- Department of Exercise and Health Sciences, University of Taipei, Taipei, Taiwan
| | - Wan-An Lu
- Institute of Cultural Asset and Reinvention, Fo-Guang University, Yilan, Taiwan
| | - Jeffrey C Pagaduan
- College of Health and Medicine, School of Health Sciences, University of Tasmania, Launceston, Australia
| | - Cheng-Deng Kuo
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.,Tanyu Research Laboratory, Taipei, Taiwan
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Shabaan M, Arshid K, Yaqub M, Jinchao F, Zia MS, Bojja GR, Iftikhar M, Ghani U, Ambati LS, Munir R. Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis. BMC Med Inform Decis Mak 2020; 20:177. [PMID: 32727453 PMCID: PMC7392662 DOI: 10.1186/s12911-020-01199-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/22/2020] [Indexed: 11/17/2022] Open
Abstract
A number of resources, every year, being spent to tackle early detection of cardiac abnormalities which is one of the leading causes of deaths all over the Globe. The challenges for healthcare systems includes early detection, portability and mobility of patients. This paper presents a categorical review of smartphone-based systems that can detect cardiac abnormalities by the analysis of Electrocardiogram (ECG) and Photoplethysmography (PPG) and the limitation and challenges of these system. The ECG based systems can monitor, record and forward signals for analysis and an alarm can be triggered in case of abnormality, however the limitation of smart phone’s processing capabilities, lack of storage and speed of network are major challenges. The systems based on PPG signals are non-invasive and provides mobility and portability. This study aims to critically review the existing systems, their limitation, challenges and possible improvements to serve as a reference for researchers and developers.
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Affiliation(s)
| | - Kaleem Arshid
- Beijing University of Technology, Chaoyang District, Beijing, China
| | - Muhammad Yaqub
- Beijing University of Technology, Chaoyang District, Beijing, China
| | - Feng Jinchao
- Beijing University of Technology, Chaoyang District, Beijing, China. .,Beijing Laboratory of Advanced Information Networks, Beijing, China.
| | - M Sultan Zia
- The University of Lahore, Gujarat Campus, Gujarat, Pakistan
| | | | | | - Usman Ghani
- Punjab Education Department, Gugarat, Pakistan
| | | | - Rizwan Munir
- Beijing University of Post and Telecommunication, Beijing, China
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Landreani F, Faini A, Martin-Yebra A, Morri M, Parati G, Caiani EG. Assessment of Ultra-Short Heart Variability Indices Derived by Smartphone Accelerometers for Stress Detection. SENSORS 2019; 19:s19173729. [PMID: 31466391 PMCID: PMC6749599 DOI: 10.3390/s19173729] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/12/2019] [Accepted: 08/23/2019] [Indexed: 12/18/2022]
Abstract
Body acceleration due to heartbeat-induced reaction forces can be measured as mobile phone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (standard deviation of normal-to-normal interval—SDNN and root mean square of successive differences—RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing at rest conditions (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional electrocardiogram (ECG). Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10 s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 s) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.
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Affiliation(s)
- Federica Landreani
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Andrea Faini
- Istituto Auxologico Italiano, IRCCS, Department of Cardiovascular Neural and Metabolic Sciences, S. Luca Hospital, 20149 Milan, Italy
| | - Alba Martin-Yebra
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
- Department of Biomedical Engineering, Lund University, 22100 Lund, Sweden
| | - Mattia Morri
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Gianfranco Parati
- Istituto Auxologico Italiano, IRCCS, Department of Cardiovascular Neural and Metabolic Sciences, S. Luca Hospital, 20149 Milan, Italy
- Department of Medicine and Surgery, Università di Milano-Bicocca, 20126 Milan, Italy
| | - Enrico Gianluca Caiani
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.
- Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, 20133 Milan, Italy.
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Siecinski S, Tkacz EJ, Kostka PS. Comparison of HRV indices obtained from ECG and SCG signals from CEBS database. Biomed Eng Online 2019; 18:69. [PMID: 31153383 PMCID: PMC6545220 DOI: 10.1186/s12938-019-0687-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 05/21/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. METHODS We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. RESULTS Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals. CONCLUSIONS Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.
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Affiliation(s)
- Szymon Siecinski
- Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology, 40 Roosevelt's Street, 41-800, Zabrze, Poland
| | - Ewaryst J Tkacz
- Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology, 40 Roosevelt's Street, 41-800, Zabrze, Poland. .,Katowice School of Technology, 43 Rolna Street, 40-055, Katowice, Poland.
| | - Pawel S Kostka
- Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology, 40 Roosevelt's Street, 41-800, Zabrze, Poland
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Abstract
Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.
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Affiliation(s)
- Amirtahà Taebi
- Department of Biomedical Engineering, University of California Davis, One Shields Ave, Davis, CA 95616, USA
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
- Correspondence: ; Tel.: +1-407-580-4654
| | - Brian E. Solar
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
| | - Andrew J. Bomar
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
- College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL 32827, USA
| | - Richard H. Sandler
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
- College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL 32827, USA
| | - Hansen A. Mansy
- Biomedical Acoustics Research Laboratory, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
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De Ridder B, Van Rompaey B, Kampen JK, Haine S, Dilles T. Smartphone Apps Using Photoplethysmography for Heart Rate Monitoring: Meta-Analysis. JMIR Cardio 2018; 2:e4. [PMID: 31758768 PMCID: PMC6834218 DOI: 10.2196/cardio.8802] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 12/03/2017] [Accepted: 12/18/2017] [Indexed: 11/19/2022] Open
Abstract
Background Smartphone ownership is rising at a stunning rate. Moreover, smartphones prove to be suitable for use in health care due to their availability, portability, user-friendliness, relatively low price, wireless connectivity, far-reaching computing capabilities, and comprehensive memory. To measure vital signs, smartphones are often connected to a mobile sensor or a medical device. However, by using the white light-emitting diode as light source and the phone camera as photodetector, a smartphone could be used to perform photoplethysmography (PPG), enabling the assessment of vital signs. Objective The objective of this meta-analysis was to evaluate the available evidence on the use of smartphone apps to measure heart rate by performing PPG in comparison with a validated method. Methods PubMed and ISI Web of Knowledge were searched for relevant studies published between January 1, 2009 and December 7, 2016. The reference lists of included studies were hand-searched to find additional eligible studies. Critical Appraisal Skills Programme (CASP) Diagnostic Test Study checklist and some extra items were used for quality assessment. A fixed effects model of the mean difference and a random effects model of Pearson correlation coefficient were applied to pool the outcomes of the studies. Results In total, 14 studies were included. The pooled result showed no significant difference between heart rate measurements with a smartphone and a validated method (mean difference −0.32; 99% CI −1.24 to 0.60; P=.37). In adults, the Pearson correlation coefficient of the relation between heart rate measurement with a smartphone and a validated method was always ≥.90. In children, the results varied depending on measuring point and heart rate. The pooled result showed a strong correlation that was significant (correlation coefficient .951; 95% CI 0.906-0.975; P<.001). The reported limits of agreement showed good agreement between a smartphone and a validated method. There was a moderately strong significant negative correlation between the year of publication of the included studies and the mean difference (r=−.69; P<.001). Conclusions Smartphone apps measuring heart rate by performing PPG appear to agree with a validated method in an adult population during resting sinus rhythm. In a pediatric population, the use of these apps is currently not validated.
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Affiliation(s)
- Benjamin De Ridder
- University Hospital Ghent, Ghent, Belgium.,Department of Nursing and Midwifery, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Bart Van Rompaey
- Department of Nursing and Midwifery, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Jarl K Kampen
- Wageningen University, Biometris, Wageningen, Netherlands.,StatUa Center for Statistics, University of Antwerp, Antwerp, Belgium
| | - Steven Haine
- Department of Cardiology, Antwerp University Hospital, Edegem, Belgium.,Department of Cardiology, University of Antwerp, Antwerp, Belgium
| | - Tinne Dilles
- Department of Nursing and Midwifery, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
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Wahlstrom J, Skog I, Handel P, Khosrow-Khavar F, Tavakolian K, Stein PK, Nehorai A. A Hidden Markov Model for Seismocardiography. IEEE Trans Biomed Eng 2017; 64:2361-2372. [PMID: 28092512 DOI: 10.1109/tbme.2017.2648741] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and [Formula: see text], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.
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