2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)最新文献

Real-Time 3D Arm Motion Tracking Using the 6-axis IMU Sensor of a Smartwatch 基于智能手表6轴IMU传感器的实时3D手臂运动跟踪

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507012

Wenchuan Wei, Keiko Kurita, Jilong Kuang, A. Gao

{"title":"Real-Time 3D Arm Motion Tracking Using the 6-axis IMU Sensor of a Smartwatch","authors":"Wenchuan Wei, Keiko Kurita, Jilong Kuang, A. Gao","doi":"10.1109/BSN51625.2021.9507012","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507012","url":null,"abstract":"Inertial measurement unit (IMU) sensors are widely used in motion tracking for various applications, e.g., virtual physical therapy and fitness training. Traditional IMU-based motion tracking systems use 9-axis IMU sensors that include an accelerometer, gyroscope, and magnetometer. The magnetometer is essential to correct the yaw drift in orientation estimation. However, its magnetic field measurement is often disturbed by the ferromagnetic materials in the environment and requires frequent calibration. Moreover, most IMU-based systems require multiple IMU sensors to track the body motion and are not convenient for use. In this paper, we propose a novel approach that uses a single 6-axis IMU sensor of a consumer smartwatch without any magnetometer to track the user's 3D arm motion in real time. We use a recurrent neural network (RNN) model to estimate the 3D positions of both the wrist and the elbow from the noisy IMU data. Compared with the state-of-the-art approaches that use either the 9-axis IMU sensor or the combination of a 6-axis IMU and an extra device, our proposed approach significantly improves the usability and potential for pervasiveness by not requiring a magnetometer or any extra device, while achieving comparable results.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114902206","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}

引用次数: 11

Wireless respiration monitoring using a flexible sensor and bistable circuit 使用柔性传感器和双稳电路的无线呼吸监测

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507014

Zhipeng Li, Ze Xiong, Chenhui Li, J. S. Ho

引用次数: 1

Towards Motion-Aware Passive Resting Respiratory Rate Monitoring Using Earbuds 利用耳塞实现运动感知被动静息呼吸率监测

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507016

Md. Mahbubur Rahman, Tousif Ahmed, M. Y. Ahmed, Ebrahim Nemati, Minh Dinh, Nathan Folkman, Md Mehedi Hasan, Jilong Kuang, J. Gao

{"title":"Towards Motion-Aware Passive Resting Respiratory Rate Monitoring Using Earbuds","authors":"Md. Mahbubur Rahman, Tousif Ahmed, M. Y. Ahmed, Ebrahim Nemati, Minh Dinh, Nathan Folkman, Md Mehedi Hasan, Jilong Kuang, J. Gao","doi":"10.1109/BSN51625.2021.9507016","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507016","url":null,"abstract":"Breathing rate is an important vital sign and an indicator of overall health and fitness. Traditionally breathing is monitored using specialized devices such as chestband or spirometers which are uncomfortable for daily use. Recent works show the feasibility of estimating breathing rate using earbuds' motion sensors. However, non-breathing head motion is one of the biggest challenges for breathing rate estimation using earbuds. In this paper, we propose algorithms to estimate breathing rate in presence of non-breathing head motion using inertial sensors embedded in commodity earbuds. Using the chestband as a reference device, we show that our algorithms can estimate breathing rate in resting positions with error rate 2.34 breaths per minute (BPM). Our algorithms can handle passive head motion and reduce the error by 27.78%. Furthermore, our algorithms can handle active head motion and help reduce the error by 45.70% when intentional non-breathing head motion is present in the data segment. It can be a big stride towards passive breathing monitoring in daily life using commodity earbuds.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128489994","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}

引用次数: 7

Kirigami Strain Sensing on Balloon Catheters with Temporary Tattoo Paper 用临时纹身纸对气球导管进行Kirigami应变传感

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507031

Jia Li, Yeow Bok Seng, Godwin Ponraj, K. S. Kumar, Catherine Jiayi Cai, Hongliang Ren

{"title":"Kirigami Strain Sensing on Balloon Catheters with Temporary Tattoo Paper","authors":"Jia Li, Yeow Bok Seng, Godwin Ponraj, K. S. Kumar, Catherine Jiayi Cai, Hongliang Ren","doi":"10.1109/BSN51625.2021.9507031","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507031","url":null,"abstract":"The current state of the art of balloon catheters relies solely on the application of a predetermined quantity of mechanical strain to the balloon during diagnostic and therapeutic procedures. In some cases, the surgeons can use radioactive-contrasting agents and x-ray screening to identify the correct position and size of the inflated balloon. Otherwise, there is little information on the inflated size of the balloon catheter in the occluded lumen. This gap in quantitative feedback of the ballooning behavior needs to be addressed to ensure safe operation. With the advancement in technology and breakthrough in flexible electronics in recent years, kirigami, an ancient cutting, bending and folding technique, is explored in the stretchable sensing field due to its ability to transform 2D planar patterns 3D geometry structures. On top of that, kirigami can increase the mechanical strain by over 300% depending on the different cuts and folds and sensitivity over 80%. This manuscript will address this limitation of conventional balloon catheters by introducing strain sensing using kirigami technology to achieve a better, safer, and more efficient treatment procedure. Experimental results show that the change in normalized resistance of the sensor is directly proportional to the change in the size of the balloon.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124786029","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}

引用次数: 0

[Copyright notice] (版权)

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/bsn51625.2021.9507028

引用次数: 0

Impedance Pneumography: Assessment of Dual-Frequency Calibration Approaches 阻抗气影:双频校准方法的评估

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507042

Hewon Jung, Samer A. Mabrouk, O. Inan

{"title":"Impedance Pneumography: Assessment of Dual-Frequency Calibration Approaches","authors":"Hewon Jung, Samer A. Mabrouk, O. Inan","doi":"10.1109/BSN51625.2021.9507042","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507042","url":null,"abstract":"Impedance pneumography (IP), a measure of the changes in the lung and thoracic bioimpedance, holds promise for non-invasive monitoring of pulmonary health. A key limitation of IP is the need for complex and frequent calibrations that require the subject to perform various maneuvers. In this work, we explore different calibration approaches to reduce the effects of inter-subject variability and postural changes on IP by utilizing a dual-frequency calibration approach. Dual-frequency IP was deployed for the first time in this work and its performance in estimating tidal volume (TV) was evaluated and compared to the conventional single frequency approaches. TV values obtained from a spirometer estimated with the subject- and posture-specific IP calibration approach are shown to correlate highly with the ground truth TV $(r > 0.9)$ in all postures, including supine, left/right lateral, and seated postures for both 5 kHz and 100 kHz IP signals. Eliminating posture specificity results in a correlation of $r > 0.8$ • With the globalized calibration approach that does not require any subject or posture-specific calibration, a correlation of $r=0.75$ was achieved with the dual-frequency approach, and this was higher than the corresponding correlation of around $r=0.68$ using any single frequency. This result has implications for the feasibility of dual-frequency IP for mitigating inter-subject variability and posture-specific calibrations.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127736832","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}

引用次数: 6

Piezoelectric-Based Respiratory Monitoring: Towards Self-Powered Implantables for the Airways 基于压电的呼吸监测:为气道提供自供电的植入式设备

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507022

Luis Javier Lopez Ruiz, V. Lin, Lucy Fitzgerald, Joe Zhu, L. Borish, Daniel Quinn, J. Lach

{"title":"Piezoelectric-Based Respiratory Monitoring: Towards Self-Powered Implantables for the Airways","authors":"Luis Javier Lopez Ruiz, V. Lin, Lucy Fitzgerald, Joe Zhu, L. Borish, Daniel Quinn, J. Lach","doi":"10.1109/BSN51625.2021.9507022","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507022","url":null,"abstract":"Wearable and implantable technology for respiratory monitoring has created the potential for continuous collection of respiratory parameters for healthcare and other applications. However, battery life, form factor, and user burden impose practical constraints that affect user acceptance and therefore clinical utility. This work introduces a wireless self-powered sensing system for airway monitoring that uses an array of piezoelectric cantilevers that functions as both the respiratory flow sensor and the energy harvester that powers the system. The cantilevers are excited by airflow in the airway, and the harvested energy from the cantilevers is stored in a capacitor. Once a threshold energy is available in the capacitor, a load switch closes and enables a low frequency oscillator that functions as a data-less transmitter. The signal coming from the sensing system is received by an external software-defined radio (SDR), and the rate at which this signal is received is mapped to the respiratory conditions in the airway. A benchtop testing system that incorporates a lung simulator, a data acquisition system, and a hot wire anemometer was created to validate the sensor. Results show that the signal reception rate is affected by the breathing rate and volume, demonstrating the potential for a self-powered, miniaturized, passive implantable device for continuous respiratory health monitoring.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122244767","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}

引用次数: 2

Deep Learning Model with Individualized Fine-tuning for Dynamic and Beat-to-Beat Blood Pressure Estimation 深度学习模型与个性化微调动态和搏动血压估计

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507019

Jingyuan Hong, Jiasheng Gao, Qing Liu, Yuan-ting Zhang, Yali Zheng

{"title":"Deep Learning Model with Individualized Fine-tuning for Dynamic and Beat-to-Beat Blood Pressure Estimation","authors":"Jingyuan Hong, Jiasheng Gao, Qing Liu, Yuan-ting Zhang, Yali Zheng","doi":"10.1109/BSN51625.2021.9507019","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507019","url":null,"abstract":"Deep learning (DL) models have demonstrated great potential in cuffless blood pressure (BP) estimation under static conditions, while the performance under dynamic conditions was still not fully validated. This study developed a DL model using population data for training and followed by individualized fine-tuning to directly learn features from multisensory signals including electrocardiogram (ECG), photoplethysmogram (PPG) and PPG derivatives for beat-to-beat BP estimation under water drinking. 25 healthy subjects were recruited, and the leave-one-subject-out approach was used to evaluate the model performance. The results showed that individualized fine-tuning using a small amount of individual baseline data did not change the tracking capability of the model, while can largely reduce the individual bias in dynamic BP estimation, with the mean absolute errors decreased from 13.43 to 9.49 mmHg and 8.48 to 5.54 mmHg for systolic BP and diastolic BP, respectively. It was also found that the model presented better results around the baseline BP levels than that at larger deviations from the baseline, indicating that future work should incorporate individual dynamic data in the fine-tuning to improve dynamic BP estimation further.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131391211","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}

引用次数: 1

The Use of Thermal Imaging and Deep Learning for Pulmonary Diagnostics and Infection Detection 热成像和深度学习在肺部诊断和感染检测中的应用

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507018

Suzie Byun, Bernardo Garcia Bulle Bueno, Yogesh Gupta, N. Dhadge, Shrikant Pawar, R. Kodgule, R. Fletcher

{"title":"The Use of Thermal Imaging and Deep Learning for Pulmonary Diagnostics and Infection Detection","authors":"Suzie Byun, Bernardo Garcia Bulle Bueno, Yogesh Gupta, N. Dhadge, Shrikant Pawar, R. Kodgule, R. Fletcher","doi":"10.1109/BSN51625.2021.9507018","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507018","url":null,"abstract":"Pulmonary diseases are a leading cause of mortality and disability, but lack of simple low-cost tools to help diagnose and screen for such diseases. In this paper, we present results from a preliminary study exploring the use of thermal imaging as a possible diagnostic tool for several common pulmonary diseases including Asthma, COPD, ILD, Allergic Rhinitis, and Respiratory Infection. As part of a global health study, thermal images of the face were collected from 125 pulmonary disease patients as well as 11 healthy controls. All subjects were evaluated using a full pulmonary function test (PFT) and diagnosed by an experienced chest physician. For each pulmonary disease, we developed a separate naïve 2-layer CNN model as well as a transfer learning CNN model, using a more complex pre-trained ResNet50 model. The naïve CNN models demonstrated an accuracy of AUC = 0.75 for respiratory infection and an AUC=0.76 for COPD, but lacked any significant predictive value for other pulmonary diseases. The transfer learning CNN models demonstrated an accuracy of AUC = 0.82 for respiratory infection and AUC=0.81 for COPD, but exhibited poor performance for other pulmonary diseases. From these results, we conclude that a facial thermal image can be a useful tool to help identify respiratory infections as well as COPD. It is also important to note that none of the patients in our study had a significant fever (T >100.4 °F) that would be predictive of infection, and our CNN models were also able to distinguish Respiratory Infection from other pulmonary diseases including COPD. Given that thermal imaging is a non-contact measurement, such a tool could be of tremendous value in low resource settings or global health.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128904661","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}

引用次数: 1

Effect of Noise on Generic Cough Models 噪声对普通咳嗽模型的影响

2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Pub Date : 2021-07-27 DOI: 10.1109/BSN51625.2021.9507040

S. V. Dibbo, Yugyeong Kim, Sudip Vhaduri

{"title":"Effect of Noise on Generic Cough Models","authors":"S. V. Dibbo, Yugyeong Kim, Sudip Vhaduri","doi":"10.1109/BSN51625.2021.9507040","DOIUrl":"https://doi.org/10.1109/BSN51625.2021.9507040","url":null,"abstract":"Respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and asthma, are two major reasons for people's death across the globe. In addition to these common inflammatory respiratory diseases, some human transmissible respiratory diseases, such as coronaviruses, cause a global pandemic. One major symptom of these inflammatory respiratory diseases is coughing. Identifying coughing using smartphone-microphone recordings is easily doable from a remote setup and can help physicians and researchers early guess a situation for an individual and a community. However, smartphone-microphone recordings can be affected by environmental noises and that can impact the performance of models that are developed to detect coughing from microphone recording. Thereby, in this work, we present a detailed analysis of noise impacts on cough detection models. We develop models using voluntary coughs and other background sounds obtained from three public datasets and test the performance of those models while detecting various types of coughs, including COPD and COVID-19, obtain from three separate datasets in the presence of background noises.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133245534","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}

引用次数: 9