Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516283
Heike Leutheuser, Stefan Gradl, L. Anneken, M. Arnold, N. Lang, S. Achenbach, B. Eskofier
Arrhythmia detection algorithms require the exact and instantaneous detection of fiducial points in the ECG signal. These fiducial points (QRS-complex, P- and T-wave) correspond to distinct cardiac contraction phases. The performance evaluation of different fiducial points detection algorithms require the existence of large databases (DBs) encompassing reference annotations. Up to last year, P- and T-wave annotations were only available for the QT DB. This was addressed by Elgendi et al. who provided P- and T-wave annotations to the MIT-BIH arrhythmia DB. A variety of ECG fiducial points detection algorithms exists in literature, whereas, to the best knowledge of the authors, we could not identify any single-lead algorithm ready for instantaneous P- and T-wave detection. In this work, we present three P- and T-wave detection algorithms: a revised version for QRS detection using line fitting capable to detect P- and T-wave, an expeditious version of a wavelet based ECG delineation algorithm, and a fast naive fiducial points detection algorithm. The fast naive fiducial points detection algorithm performed best on both DBs with sensitivities ranging from 73.0% (P-wave detection, error interval of ± 40 ms) to 89.4% (T-wave detection, error interval of ± 80 ms). As this algorithm detects a wave event in every search window, it has to be investigated how this affects arrhythmia detection algorithms. The reference Matlab implementations are available for download to encourage the development of high-accurate and automated ECG processing algorithms for the integration in daily life using mobile computers.
{"title":"Instantaneous P- and T-wave detection: Assessment of three ECG fiducial points detection algorithms","authors":"Heike Leutheuser, Stefan Gradl, L. Anneken, M. Arnold, N. Lang, S. Achenbach, B. Eskofier","doi":"10.1109/BSN.2016.7516283","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516283","url":null,"abstract":"Arrhythmia detection algorithms require the exact and instantaneous detection of fiducial points in the ECG signal. These fiducial points (QRS-complex, P- and T-wave) correspond to distinct cardiac contraction phases. The performance evaluation of different fiducial points detection algorithms require the existence of large databases (DBs) encompassing reference annotations. Up to last year, P- and T-wave annotations were only available for the QT DB. This was addressed by Elgendi et al. who provided P- and T-wave annotations to the MIT-BIH arrhythmia DB. A variety of ECG fiducial points detection algorithms exists in literature, whereas, to the best knowledge of the authors, we could not identify any single-lead algorithm ready for instantaneous P- and T-wave detection. In this work, we present three P- and T-wave detection algorithms: a revised version for QRS detection using line fitting capable to detect P- and T-wave, an expeditious version of a wavelet based ECG delineation algorithm, and a fast naive fiducial points detection algorithm. The fast naive fiducial points detection algorithm performed best on both DBs with sensitivities ranging from 73.0% (P-wave detection, error interval of ± 40 ms) to 89.4% (T-wave detection, error interval of ± 80 ms). As this algorithm detects a wave event in every search window, it has to be investigated how this affects arrhythmia detection algorithms. The reference Matlab implementations are available for download to encourage the development of high-accurate and automated ECG processing algorithms for the integration in daily life using mobile computers.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126705944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516269
L. Brattain, R. Landman, Kerry A. Johnson, Patrick C. Chwalek, J. Hyman, Jitendra Sharma, Charles Jennings, R. Desimone, G. Feng, T. Quatieri
The common marmoset is emerging as an important transgenic model for improving the understanding of the underlying neurological basis of many brain disorders. Automated systems for quantitative monitoring of marmoset behaviors in naturalist settings over long period of time are needed to facilitate this process. This paper presents the preliminary work toward building a novel multimodal acquisition system for the automated marmoset behavior analysis in home cage. In addition to integrating commercial available devices such as Microsoft Kinect sensors and microphones of different characteristics, we also developed a wireless flexible neck collar with acoustic and non-acoustic sensors onboard for marmoset vocalization recording and caller identification. Our initial effort has been focused on the real-time synchronization of multiple sensor outputs, the engineering design of the wireless collar, and algorithms for global 3D position and local head movement from a Microsoft Kinect sensor. With limited preliminary data, we are able to estimate 3D trajectories of two marmosets with a RMSE of ~3.2 mm and track colored ear tufts with an accuracy of RMSE ~1.8 mm. A larger dataset is needed for a complete assessment and validation. Our system architecture is modular and flexible, and can be extended to include more sensors and devices if needed.
{"title":"A multimodal sensor system for automated marmoset behavioral analysis","authors":"L. Brattain, R. Landman, Kerry A. Johnson, Patrick C. Chwalek, J. Hyman, Jitendra Sharma, Charles Jennings, R. Desimone, G. Feng, T. Quatieri","doi":"10.1109/BSN.2016.7516269","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516269","url":null,"abstract":"The common marmoset is emerging as an important transgenic model for improving the understanding of the underlying neurological basis of many brain disorders. Automated systems for quantitative monitoring of marmoset behaviors in naturalist settings over long period of time are needed to facilitate this process. This paper presents the preliminary work toward building a novel multimodal acquisition system for the automated marmoset behavior analysis in home cage. In addition to integrating commercial available devices such as Microsoft Kinect sensors and microphones of different characteristics, we also developed a wireless flexible neck collar with acoustic and non-acoustic sensors onboard for marmoset vocalization recording and caller identification. Our initial effort has been focused on the real-time synchronization of multiple sensor outputs, the engineering design of the wireless collar, and algorithms for global 3D position and local head movement from a Microsoft Kinect sensor. With limited preliminary data, we are able to estimate 3D trajectories of two marmosets with a RMSE of ~3.2 mm and track colored ear tufts with an accuracy of RMSE ~1.8 mm. A larger dataset is needed for a complete assessment and validation. Our system architecture is modular and flexible, and can be extended to include more sensors and devices if needed.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114277313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516234
J. Bergmann, J. Noble, M. Thompson
Sensor networks are designed to detect events and their applicability is dependent on the likelihood of a correct detection. A network that can't detect events with a high enough probability becomes ineffective. Therefore, it can be very valuable to be able to establish which network design might yield the best detection rate. The endless possibilities in terms of sensor network designs make it difficult to apply a pure experimental method. Computational modelling using statistical techniques can provide a useful tool to explore the sensor network design space. The concept of a probabilistic sensor network (PSN) model is introduced in this paper. A framework is established and examples are given of the PSN model. The PSN model is tested in a hypothetical scenario by computing Root Mean Square Errors (RMSEs) and Absolute Errors between simulation outcomes and the results of the PSN model. The RMSEs between the simulation and the model were approximately 0.02 indicating a close comparison between the simulation and the model. The proposed probabilistic sensor network method provides an intuitive and promising tool to test sensor network designs virtually.
{"title":"Probabilistic sensor network design","authors":"J. Bergmann, J. Noble, M. Thompson","doi":"10.1109/BSN.2016.7516234","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516234","url":null,"abstract":"Sensor networks are designed to detect events and their applicability is dependent on the likelihood of a correct detection. A network that can't detect events with a high enough probability becomes ineffective. Therefore, it can be very valuable to be able to establish which network design might yield the best detection rate. The endless possibilities in terms of sensor network designs make it difficult to apply a pure experimental method. Computational modelling using statistical techniques can provide a useful tool to explore the sensor network design space. The concept of a probabilistic sensor network (PSN) model is introduced in this paper. A framework is established and examples are given of the PSN model. The PSN model is tested in a hypothetical scenario by computing Root Mean Square Errors (RMSEs) and Absolute Errors between simulation outcomes and the results of the PSN model. The RMSEs between the simulation and the model were approximately 0.02 indicating a close comparison between the simulation and the model. The proposed probabilistic sensor network method provides an intuitive and promising tool to test sensor network designs virtually.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"842 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113999226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516286
Jinyang Li, Yongpan Liu, Hehe Li, Rui Hua, C. Xue, H. Lee, Huazhong Yang
Wearable devices begin to integrate into the daily lives along with recent technology development. One of such important applications is to accurately monitor ultraviolet (UV) radiation received by the human body. To compensate for the localized monitoring area of existing personal UV monitoring devices, this paper proposes a reconstruction method to estimate the UV dose over the entire body based on multiple discrete wearable UV sensor nodes. Ambient factors and individual factors are both considered in this paper. The proposed estimation method is validated by a range of UV data collection experiments in realistic scenarios. Experimental results show that the proposed method reduces 68.3% estimation errors on average compared with existing single sensor based methods.
{"title":"Accurate personal ultraviolet dose estimation with multiple wearable sensors","authors":"Jinyang Li, Yongpan Liu, Hehe Li, Rui Hua, C. Xue, H. Lee, Huazhong Yang","doi":"10.1109/BSN.2016.7516286","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516286","url":null,"abstract":"Wearable devices begin to integrate into the daily lives along with recent technology development. One of such important applications is to accurately monitor ultraviolet (UV) radiation received by the human body. To compensate for the localized monitoring area of existing personal UV monitoring devices, this paper proposes a reconstruction method to estimate the UV dose over the entire body based on multiple discrete wearable UV sensor nodes. Ambient factors and individual factors are both considered in this paper. The proposed estimation method is validated by a range of UV data collection experiments in realistic scenarios. Experimental results show that the proposed method reduces 68.3% estimation errors on average compared with existing single sensor based methods.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114739077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516250
Asma Qureshi, Maite Brandt-Pearce, M. Goldman
Multiple sclerosis (MS) is a neurological disorder that disrupts the communication within the brain, and between the brain and body. MS symptoms may vary over time. So we propose to do longitudinal assessments of a patient's gait characteristics using inertial data, in order to evaluate his/her gait for an extended period of time.
{"title":"Longitudinal estimation of gait time series density in multiple sclerosis subjects using inertial data","authors":"Asma Qureshi, Maite Brandt-Pearce, M. Goldman","doi":"10.1109/BSN.2016.7516250","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516250","url":null,"abstract":"Multiple sclerosis (MS) is a neurological disorder that disrupts the communication within the brain, and between the brain and body. MS symptoms may vary over time. So we propose to do longitudinal assessments of a patient's gait characteristics using inertial data, in order to evaluate his/her gait for an extended period of time.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116084870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516290
Andrew B. Udofa, Laurence J. Ryan, P. Weyand
Load carriage was used as an experimental tool to evaluate the ability of an anatomically-based, two-mass model of the human body to predict vertical impact and peak forces during running from only four inputs: body weight (Wb), contact time (tc), aerial time, (ta), and lower-limb acceleration (a1). Simultaneous motion and force data were acquired from seven subjects during steady-speed trials (3.0-6.0 m·s-1) on a custom, force-instrumented treadmill under three loading conditions: unloaded (1.0 Wb), 15% added weight (1.15 Wb) and 30% added weight (1.30 Wb). Model-predicted impact and peak forces corresponded with measured values, on average, to within 14.9±1.3% and 13.8±0.6%, respectively (R2 best-fits=0.82 and 0.88, n=71). Ankle jerk and velocity data derived from optical position-time data suggest wearable sensor acquisition of the model-needed inputs is fully feasible. We conclude that the two-mass model offers a promising approach to quantifying running ground reaction forces using wearable technologies.
{"title":"Impact forces during running: Loaded questions, sensible outcomes","authors":"Andrew B. Udofa, Laurence J. Ryan, P. Weyand","doi":"10.1109/BSN.2016.7516290","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516290","url":null,"abstract":"Load carriage was used as an experimental tool to evaluate the ability of an anatomically-based, two-mass model of the human body to predict vertical impact and peak forces during running from only four inputs: body weight (W<sub>b</sub>), contact time (t<sub>c</sub>), aerial time, (t<sub>a</sub>), and lower-limb acceleration (a<sub>1</sub>). Simultaneous motion and force data were acquired from seven subjects during steady-speed trials (3.0-6.0 m·s<sup>-1</sup>) on a custom, force-instrumented treadmill under three loading conditions: unloaded (1.0 W<sub>b</sub>), 15% added weight (1.15 W<sub>b</sub>) and 30% added weight (1.30 W<sub>b</sub>). Model-predicted impact and peak forces corresponded with measured values, on average, to within 14.9±1.3% and 13.8±0.6%, respectively (R<sup>2</sup> best-fits=0.82 and 0.88, n=71). Ankle jerk and velocity data derived from optical position-time data suggest wearable sensor acquisition of the model-needed inputs is fully feasible. We conclude that the two-mass model offers a promising approach to quantifying running ground reaction forces using wearable technologies.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123440279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516254
M. Berthelot, Ching-Mei Chen, Guang-Zhong Yang, Benny P. L. Lo
Blood flow and perfusion monitoring are critical appraisal to ensure survival of tissue flap after reconstructive surgery. Many techniques have been developed over the years: from optical to chemical, invasive or not, they all have limitations in their price, risks and adaptiveness to the patient. A wireless wearable self-calibrated device, based on near infrared spectroscopy (NIRS) was developed for blood flow and perfusion monitoring contingent on tissue oxygen saturation (StO2). The use of such device is particularly relevant in the case of free flap myocutaneous reconstructive surgery; postoperative monitoring of the flap is crucial for a prompt intervention in case of thrombosis. Although failure rate is low, the rate of additional surgery following anastomosis problem is about 50%. NIRS has shown promising results for the monitoring of free flap, however lack of adaptation to its environment (ambient light) and users (body mass index (BMI), skin tone, alcohol and smoking habits or physical activity level) hinders the practical use of this technique. To overcome those limitations, a self-calibrated approach is introduced. Tested with is chaemia and cold water experiments on healthy subjects of different skin tones, its ability to personalize its calibration is demonstrated. Furthermore, using a vascular phantom, it is also able to detect pulses, differentiate venous and arterial coloured-like fluids with distinct clusters and detect significant changes in simulated partial venous occlusion. Placed in the trained classifier, partial occlusion data showed similar results between predicted and true classification. Further analysis from partial occlusion data showed that distinct clusters for 75% and 100% occlusion emerged.
{"title":"Wireless wearable self-calibrated sensor for perfusion assessment of myocutaneous tissue","authors":"M. Berthelot, Ching-Mei Chen, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2016.7516254","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516254","url":null,"abstract":"Blood flow and perfusion monitoring are critical appraisal to ensure survival of tissue flap after reconstructive surgery. Many techniques have been developed over the years: from optical to chemical, invasive or not, they all have limitations in their price, risks and adaptiveness to the patient. A wireless wearable self-calibrated device, based on near infrared spectroscopy (NIRS) was developed for blood flow and perfusion monitoring contingent on tissue oxygen saturation (StO2). The use of such device is particularly relevant in the case of free flap myocutaneous reconstructive surgery; postoperative monitoring of the flap is crucial for a prompt intervention in case of thrombosis. Although failure rate is low, the rate of additional surgery following anastomosis problem is about 50%. NIRS has shown promising results for the monitoring of free flap, however lack of adaptation to its environment (ambient light) and users (body mass index (BMI), skin tone, alcohol and smoking habits or physical activity level) hinders the practical use of this technique. To overcome those limitations, a self-calibrated approach is introduced. Tested with is chaemia and cold water experiments on healthy subjects of different skin tones, its ability to personalize its calibration is demonstrated. Furthermore, using a vascular phantom, it is also able to detect pulses, differentiate venous and arterial coloured-like fluids with distinct clusters and detect significant changes in simulated partial venous occlusion. Placed in the trained classifier, partial occlusion data showed similar results between predicted and true classification. Further analysis from partial occlusion data showed that distinct clusters for 75% and 100% occlusion emerged.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123056293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516256
Timm Hormann, Marc Hesse, Michael Adams, U. Rückert
Body sensors have a promising contribution to health promotion in many areas of daily life (telemedicine, corporate health care or recreational sports). However, the valid measurement of vital signs and kinematic data strongly depends on the signals' quality and the users' compliance (proper usage). Although, there is a lot of research work concerning accuracy and calibration of wireless body sensors the human user is typically not involved. Thus, in this work, we present a software assistant (wizard) that guides users during the process of attaching and setting up a wireless body sensor. Furthermore, insights of the implemented software as well as the utilized quality measures and calibration steps are given (ECG, respiration sensor and accelerometer). With the proposed software assistant, the users are instructed to correctly attach the body sensor and calibrate or verify the operability of the various sensor elements. The primary goal is to encourage compliance and the users' sense of control. In this way, we want to reduce faulty operation and ensure optimal signal quality.
{"title":"A software assistant for user-centric calibration of a wireless body sensor","authors":"Timm Hormann, Marc Hesse, Michael Adams, U. Rückert","doi":"10.1109/BSN.2016.7516256","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516256","url":null,"abstract":"Body sensors have a promising contribution to health promotion in many areas of daily life (telemedicine, corporate health care or recreational sports). However, the valid measurement of vital signs and kinematic data strongly depends on the signals' quality and the users' compliance (proper usage). Although, there is a lot of research work concerning accuracy and calibration of wireless body sensors the human user is typically not involved. Thus, in this work, we present a software assistant (wizard) that guides users during the process of attaching and setting up a wireless body sensor. Furthermore, insights of the implemented software as well as the utilized quality measures and calibration steps are given (ECG, respiration sensor and accelerometer). With the proposed software assistant, the users are instructed to correctly attach the body sensor and calibrate or verify the operability of the various sensor elements. The primary goal is to encourage compliance and the users' sense of control. In this way, we want to reduce faulty operation and ensure optimal signal quality.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125806973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516240
Yi Steven Ding, Z. Zilic
This paper presents a low-complexity compression scheme of electrocardiogram (ECG) signals based on the Haar wavelet transform (HWT) for use on mobile devices. An experimental, wearable, multi-lead ECG monitor was also developed and served as a testing platform for the proposed compression scheme. The proposed scheme was applied to all 48 recordings of the MIT-BIH arrhythmia database, where a percent root mean square difference (PRD) of 3.11 along with a compression ratio (CR) of 21.38:1 was achieved on average. The proposed scheme was also tested using raw multi-lead captures from the experimental device where an average PRD and CR of 9.77 and 24.95:1 was achieved respectively. The proposed HWT based compression scheme was efficiently implemented on a mobile platform and is capable of compressing multi-lead ECG signals, allowing for efficient data management in the context of data storage or data transmission.
{"title":"ECG compression for mobile sensor platforms","authors":"Yi Steven Ding, Z. Zilic","doi":"10.1109/BSN.2016.7516240","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516240","url":null,"abstract":"This paper presents a low-complexity compression scheme of electrocardiogram (ECG) signals based on the Haar wavelet transform (HWT) for use on mobile devices. An experimental, wearable, multi-lead ECG monitor was also developed and served as a testing platform for the proposed compression scheme. The proposed scheme was applied to all 48 recordings of the MIT-BIH arrhythmia database, where a percent root mean square difference (PRD) of 3.11 along with a compression ratio (CR) of 21.38:1 was achieved on average. The proposed scheme was also tested using raw multi-lead captures from the experimental device where an average PRD and CR of 9.77 and 24.95:1 was achieved respectively. The proposed HWT based compression scheme was efficiently implemented on a mobile platform and is capable of compressing multi-lead ECG signals, allowing for efficient data management in the context of data storage or data transmission.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134003519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516263
Xian Li, Huang Hui, Ye Sun
The goal of this study is to investigate the two major reasons of motion artifacts, impedance variation and triboelectric charge accumulation. A theoretical model is established to analyze and estimate the dominant factor in different scenarios. This model also quantitatively explains how the major factors influence signal quality. A wearable device as small as a button was developed and used for experiment validation. The results showed that the body triboelectricity was the dominant factor to two-electrode settings where caused little influence on three-electrode settings. Also the impedance variation due to motion resulted in ECG baseline fluctuating whereas the surface charge accumulation might cause failure of ECG acquisition. This study aims to provide fundamental understanding of motion artifacts and new evidence for technical improvement for wearable ExG systems.
{"title":"Investigation of motion artifacts for biopotential measurement in wearable devices","authors":"Xian Li, Huang Hui, Ye Sun","doi":"10.1109/BSN.2016.7516263","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516263","url":null,"abstract":"The goal of this study is to investigate the two major reasons of motion artifacts, impedance variation and triboelectric charge accumulation. A theoretical model is established to analyze and estimate the dominant factor in different scenarios. This model also quantitatively explains how the major factors influence signal quality. A wearable device as small as a button was developed and used for experiment validation. The results showed that the body triboelectricity was the dominant factor to two-electrode settings where caused little influence on three-electrode settings. Also the impedance variation due to motion resulted in ECG baseline fluctuating whereas the surface charge accumulation might cause failure of ECG acquisition. This study aims to provide fundamental understanding of motion artifacts and new evidence for technical improvement for wearable ExG systems.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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