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Eleveld N, Harmsen M, Elting JWJ, Maurits NM. Haemosync: A synchronisation algorithm for multimodal haemodynamic signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108298. [PMID: 38936154 DOI: 10.1016/j.cmpb.2024.108298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/30/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results. METHODS We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm's performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined. RESULTS The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° (median, IQR 0.13-0.2°, range 0.0038-1.1°) and 0.54° (median, IQR 0.23-1.7°, range 0.0088-5.6°) for the very low frequency and low frequency ranges, respectively. DISCUSSION This algorithm allows visually interpretable detection and accurate correction of time-shifts between pulsatile haemodynamic signals (ABP and CBv).
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Affiliation(s)
- Nick Eleveld
- University of Groningen, University Medical Center Groningen, Department of Neurology, 9713 GZ Groningen, the Netherlands.
| | - Marije Harmsen
- University of Groningen, University Medical Center Groningen, Department of Neurology, 9713 GZ Groningen, the Netherlands
| | - Jan Willem J Elting
- University of Groningen, University Medical Center Groningen, Department of Neurology, 9713 GZ Groningen, the Netherlands
| | - Natasha M Maurits
- University of Groningen, University Medical Center Groningen, Department of Neurology, 9713 GZ Groningen, the Netherlands
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Martins J, Cerqueira SM, Catarino AW, da Silva AF, Rocha AM, Vale J, Ângelo M, Santos CP. Integrating sEMG and IMU Sensors in an e-Textile Smart Vest for Forward Posture Monitoring: First Steps. SENSORS (BASEL, SWITZERLAND) 2024; 24:4717. [PMID: 39066114 PMCID: PMC11280952 DOI: 10.3390/s24144717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Revised: 07/17/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm.
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Affiliation(s)
- João Martins
- Center for Microelectromechanical Systems (CMEMS), University of Minho, 4800-058 Guimarães, Portugal; (J.M.); (A.F.d.S.)
| | - Sara M. Cerqueira
- Center for Microelectromechanical Systems (CMEMS), University of Minho, 4800-058 Guimarães, Portugal; (J.M.); (A.F.d.S.)
| | - André Whiteman Catarino
- Center of Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimarães, Portugal; (A.W.C.); (A.M.R.)
| | - Alexandre Ferreira da Silva
- Center for Microelectromechanical Systems (CMEMS), University of Minho, 4800-058 Guimarães, Portugal; (J.M.); (A.F.d.S.)
- LABBELS-Associate Laboratory, University of Minho, 4800-058 Guimarães, Portugal
| | - Ana M. Rocha
- Center of Textile Science and Technology (2C2T), University of Minho, 4800-058 Guimarães, Portugal; (A.W.C.); (A.M.R.)
| | - Jorge Vale
- Valérius-Têxteis, SA, Rua Industrial do Aldão, Apartado 219, Vila Frescaínha, S.Martinho, 4750-078 Barcelos, Portugal; (J.V.); (M.Â.)
| | - Miguel Ângelo
- Valérius-Têxteis, SA, Rua Industrial do Aldão, Apartado 219, Vila Frescaínha, S.Martinho, 4750-078 Barcelos, Portugal; (J.V.); (M.Â.)
| | - Cristina P. Santos
- Center for Microelectromechanical Systems (CMEMS), University of Minho, 4800-058 Guimarães, Portugal; (J.M.); (A.F.d.S.)
- LABBELS-Associate Laboratory, University of Minho, 4800-058 Guimarães, Portugal
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Zanoli S, Ansaloni G, Teijeiro T, Atienza D. Event-based sampled ECG morphology reconstruction through self-similarity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107712. [PMID: 37451229 DOI: 10.1016/j.cmpb.2023.107712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 06/19/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important bio-markers, not recoverable with standard interpolation techniques. In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats. METHODS We acquire a set of uniformly sampled heartbeats and use a graph-based clustering algorithm to define representative templates for the patient. Then, for each event-based sampled heartbeat, we select the morphologically nearest template, and we then reconstruct the heartbeat with piece-wise linear deformations of the selected template, according to a novel dynamic time warping algorithm that matches events to template segments. RESULTS Synthetic tests on a standard normal sinus rhythm dataset, composed of approximately 1.8 million normal heartbeats, show a big leap in performance with respect to standard resampling techniques. In particular (when compared to classic linear resampling), we show an improvement in P-wave detection of up to 10 times, an improvement in T-wave detection of up to three times, and a 30% improvement in the dynamic time warping morphological distance. CONCLUSION In this work, we have developed an event-based processing pipeline that leverages signal self-similarity to reconstruct event-based sampled ECG signals. Synthetic tests show clear advantages over classical resampling techniques.
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Affiliation(s)
- Silvio Zanoli
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015.
| | - Giovanni Ansaloni
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015
| | - Tomás Teijeiro
- Department of Mathematics, University of the Basque Country (UPV/EHU), Bilbao, Spain
| | - David Atienza
- Embedded Systems Laboratory (ESL), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015
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Liang B, Han S, Li W, Huang G, He R. Spatial-temporal alignment of time series with different sampling rates based on cellular multi-objective whale optimization. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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5
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Liang M, Wang X, Wu S. Improving stock trend prediction through financial time series classification and temporal correlation analysis based on aligning change point. Soft comput 2022. [DOI: 10.1007/s00500-022-07630-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Li Z, Song Y, Li R, Gu S, Fan X. A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8187. [PMID: 36365885 PMCID: PMC9657032 DOI: 10.3390/s22218187] [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: 09/19/2022] [Revised: 10/19/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.
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Polak AG, Klich B, Saganowski S, Prucnal MA, Kazienko P. Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability. SENSORS (BASEL, SWITZERLAND) 2022; 22:7047. [PMID: 36146394 PMCID: PMC9502353 DOI: 10.3390/s22187047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.
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Affiliation(s)
- Adam G. Polak
- Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, 50-317 Wrocław, Poland
| | - Bartłomiej Klich
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Stanisław Saganowski
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Monika A. Prucnal
- Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, 50-317 Wrocław, Poland
| | - Przemysław Kazienko
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
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Zou Z, Nie MX, Liu XS, Liu SJ. Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree. SENSORS 2022; 22:s22145305. [PMID: 35890988 PMCID: PMC9318603 DOI: 10.3390/s22145305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 02/01/2023]
Abstract
Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.
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Affiliation(s)
- Zheng Zou
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China;
| | - Ming-Xing Nie
- School of Computer Science, University of South China, Hengyang 421001, China
- Correspondence: (M.-X.N.); (S.-J.L.)
| | - Xing-Sheng Liu
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
| | - Shi-Jian Liu
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350108, China
- Correspondence: (M.-X.N.); (S.-J.L.)
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Design and Implementation of Multiple Music System Based on Internet of Things. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3908188. [PMID: 35676960 PMCID: PMC9170442 DOI: 10.1155/2022/3908188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 04/28/2022] [Accepted: 05/15/2022] [Indexed: 11/18/2022]
Abstract
With the rapid development of social economy and Internet of Things, the society has entered the era of networking, digitalization, and intelligence, bringing great convenience to people's life; Internet of Things music system also has begun to get people's extensive attention. Due to the influence of such factors as strong music professionalism, complex music theory knowledge, and diverse changes, it is difficult to identify music features. In order to strengthen the user's personal experience of the music system, the multimusic systems are interconnected through information technology to realize the connection between objects and people. The system uses an embedded processor to realize the central control module and then according to network standard the sensor network is built, through radio frequency identification (RFID) technology for light, sound, infrared sensor, temperature, and other sensors for information reading. Music selection logic is designed based on the theory of music psychology and user behavior log, so as to select the best music for users to improve their mood and improve their life quality and work and study efficiency. At the same time, the system uses voice recognition technology to enhance user interaction, through the system, to provide the website to share their own music data and comments on songs and view song information, and the system runs stably and can collect high quality music signals and correctly identify the characteristics of music form and emotional characteristics.
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Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Xiao R, Ding C, Hu X. Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health. J Imaging 2022; 8:jimaging8050120. [PMID: 35621884 PMCID: PMC9145353 DOI: 10.3390/jimaging8050120] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/14/2022] [Accepted: 04/19/2022] [Indexed: 02/01/2023] Open
Abstract
Background: Despite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. Existing algorithms mainly rely on specific physiological features that restrict the use cases to certain signal types. The present study aims to complement previous algorithms and solve a niche time alignment problem when a common signal type is available across different devices. Methods: We proposed a simple time alignment approach based on the direct cross-correlation of temporal amplitudes, making it agnostic and thus generalizable to different signal types. The approach was tested on a public electrocardiographic (ECG) dataset to simulate the synchronization of signals collected from an ECG watch and an ECG patch. The algorithm was evaluated considering key practical factors, including sample durations, signal quality index (SQI), resilience to noise, and varying sampling rates. Results: The proposed approach requires a short sample duration (30 s) to operate, and demonstrates stable performance across varying sampling rates and resilience to common noise. The lowest synchronization delay achieved by the algorithm is 0.13 s with the integration of SQI thresholding. Conclusions: Our findings help improve the time alignment of multimodal signals in digital health and advance healthcare toward precise remote monitoring and disease prevention.
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Affiliation(s)
- Ran Xiao
- School of Nursing, Duke University, Durham, NC 27708, USA
- Correspondence:
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA 30332, USA;
| | - Xiao Hu
- School of Nursing, Emory University, Atlanta, GA 30322, USA;
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
- Department of Computer Science, College of Arts and Sciences, Emory University, Atlanta, GA 30322, USA
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The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds. SENSORS 2022; 22:s22041678. [PMID: 35214579 PMCID: PMC8874685 DOI: 10.3390/s22041678] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 02/06/2022] [Accepted: 02/17/2022] [Indexed: 12/16/2022]
Abstract
Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms.
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Adhikary S, Ghosh A. Dynamic time warping approach for optimized locomotor impairment detection using biomedical signal processing. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping. REMOTE SENSING 2022. [DOI: 10.3390/rs14030501] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Efficient methodologies for vegetation-type mapping are significant for wetland’s management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9.
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LANDA-JIMÉNEZ MIGUELA, GONZÁLEZ-GASPAR PATRICIA, MONTES-GONZÁLEZ FERNANDOM, MORGADO-VALLE CONSUELO, BELTRÁN-PARRAZAL LUIS. An open-source low-cost wireless sensor system for acquisition of human movement data. AN ACAD BRAS CIENC 2022; 94:e20191419. [DOI: 10.1590/0001-3765202220191419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 12/03/2020] [Indexed: 11/22/2022] Open
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Classification of Diabetic Walking for Senior Citizens and Personal Home Training System Using Single RGB Camera through Machine Learning. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11199029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Senior citizens have increased plasma glucose and a higher risk of diabetes-related complications than young people. However, it is difficult to diagnose and manage elderly diabetics because there is no clear symptom according to current diagnostic criteria. They also dislike the invasive blood sample test. This study aimed to classify a difference in gait and physical fitness characteristics between senior citizens with and without diabetes for a non-invasive method and propose a machine-learning-based personal home-training system for training abnormal gait motions by oneself. We used a dataset for classification with 200 over 65-year-old elders who walked a flat and straight 15 m route in 3 different walking speed conditions using an inertial measurement unit and physical fitness test. Then, questionnaires for participants were included to identify life patterns. Through results, it was found that there were abnormalities in gait and physical fitness characteristics related to balance ability and walking speed. Using a single RGB camera, the developed training system for improving abnormalities enabled us to correct the exercise posture and speed in real-time. It was discussed that there are risks and errors in the training system based on human pose estimation for future works.
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Cellular Network Radio Monitoring and Management through Virtual UE Probes: A Study Case Based on Crowded Events. SENSORS 2021; 21:s21103404. [PMID: 34068286 PMCID: PMC8153328 DOI: 10.3390/s21103404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/26/2021] [Accepted: 05/08/2021] [Indexed: 11/17/2022]
Abstract
Although log processing of network equipment is a common technique in cellular network management, several factors make said task challenging, especially during mass attendance events. The present paper assesses classic methods for cellular network measurement and acquisition, showing how the use of on-the-field user probes can provide relevant capabilities to the analysis of cellular network performance. Therefore, a framework for the deployment of this kind of system is proposed here based on the development of a new hardware virtualization platform with radio frequency capabilities. Accordingly, an analysis of the characteristics and requirements for the use of virtual probes was performed. Moreover, the impact that social events (e.g., sports matches) may have on the service provision was evaluated through a real cellular scenario. For this purpose, a long-term measurement study during crowded events (i.e., football matches) in a stadium has been conducted, and the performances of different services and operators under realistic settings has been evaluated. As a result, several considerations are presented that can be used for better management of future networks.
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Hollerbach AL, Conant CR, Nagy G, Monroe ME, Gupta K, Donor M, Giberson CM, Garimella SVB, Smith RD, Ibrahim YM. Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:996-1007. [PMID: 33666432 PMCID: PMC8216491 DOI: 10.1021/jasms.1c00005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Detection of arrival time shifts between ion mobility spectrometry (IMS) separations can limit achievable resolving power (Rp), particularly when multiple separations are summed or averaged, as commonly practiced in IMS. Such variations can be apparent in higher Rp measurements and are particularly evident in long path length traveling wave structures for lossless ion manipulations (SLIM) IMS due to their typically much longer separation times. Here, we explore data processing approaches employing single value alignment (SVA) and nonlinear dynamic time warping (DTW) to correct for variations between IMS separations, such as due to pressure fluctuations, to enable more effective spectrum summation for improving Rp and detection of low-intensity species. For multipass SLIM IMS separations, where narrow mobility range measurements have arrival times that can extend to several seconds, the SVA approach effectively corrected for such variations and significantly improved Rp for summed separations. However, SVA was much less effective for broad mobility range separations, such as obtained with multilevel SLIM IMS. Changes in ions' arrival times were observed to be correlated with small pressure changes, with approximately 0.6% relative arrival time shifts being common, sufficient to result in a loss of Rp for summed separations. Comparison of the approaches showed that DTW alignment performed similarly to SVA when used over a narrow mobility range but was significantly better (providing narrower peaks and higher signal intensities) for wide mobility range data. We found that the DTW approach increased Rp by as much as 115% for measurements in which 50 IMS separations over 2 s were summed. We conclude that DTW is superior to SVA for ultra-high-resolution broad mobility range SLIM IMS separations and leads to a large improvement in effective Rp, correcting for ion arrival time shifts regardless of the cause, as well as improving the detectability of low-abundance species. Our tool is publicly available for use with universal ion mobility format (.UIMF) and text (.txt) files.
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Affiliation(s)
- Adam L Hollerbach
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Christopher R Conant
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Gabe Nagy
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Khushboo Gupta
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Micah Donor
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Cameron M Giberson
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Sandilya V B Garimella
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Yehia M Ibrahim
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
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Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification. SENSORS 2021; 21:s21041511. [PMID: 33671583 PMCID: PMC7926887 DOI: 10.3390/s21041511] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 01/01/2023]
Abstract
The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.
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20
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Bent B, Wang K, Grzesiak E, Jiang C, Qi Y, Jiang Y, Cho P, Zingler K, Ogbeide FI, Zhao A, Runge R, Sim I, Dunn J. The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data. J Clin Transl Sci 2020; 5:e19. [PMID: 33948242 PMCID: PMC8057397 DOI: 10.1017/cts.2020.511] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/07/2020] [Accepted: 07/05/2020] [Indexed: 02/06/2023] Open
Abstract
INTRODUCTION Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. METHODS In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP). RESULTS Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. CONCLUSIONS The clear need for such a platform will accelerate the DBDP's adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.
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Affiliation(s)
- Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Emilia Grzesiak
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Chentian Jiang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Yuankai Qi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Yihang Jiang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Peter Cho
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Kyle Zingler
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Arthur Zhao
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ryan Runge
- School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Ida Sim
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Bioinformatics and Biostatistics, Duke University, Durham, NC, USA
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