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Ma M, Du M, Feng Q, Xiahou S. A new particle filter algorithm filtering motion artifact noise for clean electrocardiogram signals in wearable health monitoring system. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:014101. [PMID: 38197770 DOI: 10.1063/5.0153241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/18/2023] [Indexed: 01/11/2024]
Abstract
With the evolution of wearable systems, more and more people tend to wear wearable devices for health monitoring during sports. However, a large amount of motion artifact noise is introduced at this time, which is difficult to filter out due to its stochasticity. The amplitude and characteristics of motion artifact noise vary with changes in motion intensity. In order to filter out the motion artifact noise, the paperproposes a new particle algorithm, which can detect the intensity of the motion artifact for adaptive filtering, especiallysuitable for wearable health monitoring systems. In this algorithm, variational mode decomposition was first introduced to analyze the noisy electrocardiogram (ECG) signal in order to find the clean components. Then, the Laguerre estimation technique was applied to obtain an accurate ECG polar model. Taking this model as the state equation, a particle filter algorithm was defined to filter out the motion artifact noise. In the particle filter algorithm, we defined a parameter γ whose values were obtained from the six-axis data of motion sensor MPU6050 in our wearable device. This parameter γ could reflect the current noise levels and adaptively update the particle weights. Finally, some exercise experiments proved that the parameter γ could map the motion artifacts in real time and also demonstrated the superiority of the algorithm in terms of signal-to-noise ratio improvement and error reduction compared to other algorithms. The new particle filter algorithm proposed in this paper combines the six-axis data (three-axis accelerometer and three-axis gyroscope) with the ECG signal to effectively eliminate a large amount of motion artifact noise, thus solving the problem of excess noise from wearable devices when people are exercising, allowing them to accurately obtain real-time ECG health information.
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Affiliation(s)
- Min Ma
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mingrui Du
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiuyue Feng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shiji Xiahou
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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Abstract
Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. We classify the vast amount of publications into different categories such as sensors, communication, artificial intelligence, infrastructure, and security. Intensively covering 118 research works, we survey (1) applications, (2) key components, their roles and connections, and (3) the challenges. Our survey also discusses the potential solutions to overcome the challenges in this research field.
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Periyaswamy T, Balasubramanian M. Ambulatory cardiac bio-signals: From mirage to clinical reality through a decade of progress. Int J Med Inform 2019; 130:103928. [PMID: 31434042 DOI: 10.1016/j.ijmedinf.2019.07.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 06/05/2019] [Accepted: 07/08/2019] [Indexed: 01/01/2023]
Abstract
BACKGROUND Health monitoring is shifting towards continuous, ambulatory and clinically comparable wearable devices. Telemedicine and remote diagnosis could harness the capability of mobile cardiac health information, as the technology on bio-physical signal monitoring has improved significantly. OBJECTIVES The purpose of this review article is (1) to systematically assess the viability of ambulatory electrocardiography (ECG), (2) to provide a systems level understanding of a broad spectrum of wearable heart signal monitoring approaches and (3) to identify areas of improvement in the existing technology needed to attain clinical grade diagnosis. RESULTS Based on the included literature, we have identified (1) that the developments in ECG monitoring through wearable devices are reaching feasibility, and are capable of delivering diagnostic and prognostic information, (2) that reliable sensing is the major bottleneck in the entire process of ambulatory monitoring, (3) that there is a strong need for artificial intelligence and machine learning techniques to parse and infer the biosignals and (4) that aspects of wearer comfort has largely been ignored in the prevailing developments, which can become a key factor for consumer acceptance. CONCLUSIONS Cardiac health information is crucial for diagnosis and prevention of several disease onsets. Mobile and continuous monitoring can aid avoiding risks involved with acute symptoms. The health information obtained through continuous monitoring can serve as the BigData of heart signals, and can facilitate new treatment methods and devise effective health policies.
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Affiliation(s)
- Thamizhisai Periyaswamy
- Department of Human Environmental Studies, 117 Wightman Hall, Central Michigan University, Mount Pleasant, Michigan, 48859, United States.
| | - Mahendran Balasubramanian
- Apparel Merchandising and Product Development, School of Human Environmental Science, 118 Home Economic Building, University of Arkansas, Fayetteville, Arkansas, 72701, United States.
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Sejdić E, Millecamps A, Teoli J, Rothfuss MA, Franconi NG, Perera S, Jones AK, Brach JS, Mickle MH. Assessing interactions among multiple physiological systems during walking outside a laboratory: An Android based gait monitor. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 122:450-461. [PMID: 26390946 PMCID: PMC4648697 DOI: 10.1016/j.cmpb.2015.08.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2014] [Revised: 07/27/2015] [Accepted: 08/21/2015] [Indexed: 06/05/2023]
Abstract
Gait function is traditionally assessed using well-lit, unobstructed walkways with minimal distractions. In patients with subclinical physiological abnormalities, these conditions may not provide enough stress on their ability to adapt to walking. The introduction of challenging walking conditions in gait can induce responses in physiological systems in addition to the locomotor system. There is a need for a device that is capable of monitoring multiple physiological systems in various walking conditions. To address this need, an Android-based gait-monitoring device was developed that enabled the recording of a patient's physiological systems during walking. The gait-monitoring device was tested during self-regulated overground walking sessions of fifteen healthy subjects that included 6 females and 9 males aged 18-35 years. The gait-monitoring device measures the patient's stride interval, acceleration, electrocardiogram, skin conductance and respiratory rate. The data is stored on an Android phone and is analyzed offline through the extraction of features in the time, frequency and time-frequency domains. The analysis of the data depicted multisystem physiological interactions during overground walking in healthy subjects. These interactions included locomotion-electrodermal, locomotion-respiratory and cardiolocomotion couplings. The current results depicting strong interactions between the locomotion system and the other considered systems (i.e., electrodermal, respiratory and cardiovascular systems) warrant further investigation into multisystem interactions during walking, particularly in challenging walking conditions with older adults.
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Affiliation(s)
- E Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
| | - A Millecamps
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Teoli
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - M A Rothfuss
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - N G Franconi
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - S Perera
- Division of Geriatric Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - A K Jones
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - J S Brach
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA
| | - M H Mickle
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
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Kher R, Pawar T, Thakar V, Shah H. Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines. J Med Eng Technol 2015; 39:138-52. [DOI: 10.3109/03091902.2014.998372] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Dunne LE, Gioberto G, Ramesh V, Koo H. Measuring movement of denim trousers for garment-integrated sensing applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:3990-3. [PMID: 22255214 DOI: 10.1109/iembs.2011.6090991] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The movement of everyday garments over the body surface during wearing often presents a problematic source of noise or error for garment-integrated wearable sensors. This paper describes early results of an assessment of the impact of body location and garment ease on movement of the garment over the body surface. The method implemented uses a running mannequin with a repeatable gait cycle as the source of humanoid motion, and measures the movement of a set of custom-graded denim trousers in 5 ease amounts over the mannequin's surface during the gait cycle. Initial results show consistent patterns of displacement over the body surface.
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Affiliation(s)
- Lucy E Dunne
- Department of Design, Housing, and Apparel, University of Minnesota, St Paul, MN 55108, USA.
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Li M, Rozgica V, Thatte G, Lee S, Emken A, Annavaram M, Mitra U, Spruijt-Metz D, Narayanan S. Multimodal physical activity recognition by fusing temporal and cepstral information. IEEE Trans Neural Syst Rehabil Eng 2010; 18:369-80. [PMID: 20699202 DOI: 10.1109/tnsre.2010.2053217] [Citation(s) in RCA: 87] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
A physical activity (PA) recognition algorithm for a wearable wireless sensor network using both ambulatory electrocardiogram (ECG) and accelerometer signals is proposed. First, in the time domain, the cardiac activity mean and the motion artifact noise of the ECG signal are modeled by a Hermite polynomial expansion and principal component analysis, respectively. A set of time domain accelerometer features is also extracted. A support vector machine (SVM) is employed for supervised classification using these time domain features. Second, motivated by their potential for handling convolutional noise, cepstral features extracted from ECG and accelerometer signals based on a frame level analysis are modeled using Gaussian mixture models (GMMs). Third, to reduce the dimension of the tri-axial accelerometer cepstral features which are concatenated and fused at the feature level, heteroscedastic linear discriminant analysis is performed. Finally, to improve the overall recognition performance, fusion of the multimodal (ECG and accelerometer) and multidomain (time domain SVM and cepstral domain GMM) subsystems at the score level is performed. The classification accuracy ranges from 79.3% to 97.3% for various testing scenarios and outperforms the state-of-the-art single accelerometer based PA recognition system by over 24% relative error reduction on our nine-category PA database.
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Affiliation(s)
- Ming Li
- Viterbi School of Engineering, University ofSouthern California, Los Angeles, CA 90089, USA.
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