Pub Date : 2016-06-14DOI: 10.1109/BSN.2016.7516268
Rachelle L. Horwitz-Martin, T. Quatieri, Elizabeth Godoy, J. Williamson
Speech provides a potential simple and noninvasive “on-body” means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area. Two model components contribute to the envelope of the speech waveform: amplitude modulation (AM) from respiratory muscles, and AM from interaction between vocal tract resonances (formants) and frequency modulation in vocal fold harmonics. Based on the model framework, we test three methods to extract envelopes capturing these modulations of the third formant for synthesized sustained vowels. Using subsequent modulation features derived from the model, we predict MDD severity scores with a Gaussian Mixture Model. Performing global optimization over classifier parameters and number of principal components, we evaluate performance of the features by examining the root-mean-squared error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual and predicted MDD scores. We achieved RMSE and MAE values 10.32 and 8.46, respectively (Spearman correlation=0.487, p<;0.001), relative to a baseline RMSE of 11.86 and MAE of 10.05, obtained by predicting the mean MDD severity score. Ultimately, our model provides a framework for detecting and monitoring vocal modulations that could also be applied to other neurological diseases.
{"title":"A vocal modulation model with application to predicting depression severity","authors":"Rachelle L. Horwitz-Martin, T. Quatieri, Elizabeth Godoy, J. Williamson","doi":"10.1109/BSN.2016.7516268","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516268","url":null,"abstract":"Speech provides a potential simple and noninvasive “on-body” means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area. Two model components contribute to the envelope of the speech waveform: amplitude modulation (AM) from respiratory muscles, and AM from interaction between vocal tract resonances (formants) and frequency modulation in vocal fold harmonics. Based on the model framework, we test three methods to extract envelopes capturing these modulations of the third formant for synthesized sustained vowels. Using subsequent modulation features derived from the model, we predict MDD severity scores with a Gaussian Mixture Model. Performing global optimization over classifier parameters and number of principal components, we evaluate performance of the features by examining the root-mean-squared error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual and predicted MDD scores. We achieved RMSE and MAE values 10.32 and 8.46, respectively (Spearman correlation=0.487, p<;0.001), relative to a baseline RMSE of 11.86 and MAE of 10.05, obtained by predicting the mean MDD severity score. Ultimately, our model provides a framework for detecting and monitoring vocal modulations that could also be applied to other neurological diseases.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"58 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":"132585104","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.2016.7516225
Yan Zhuang, Jiaqi Gong, D. Kerrigan, B. Bennett, J. Lach, S. Russell
Step-by-step determination of gait parameters provides insight into the variability of specific gait patterns associated with frequent injuries in the lower extremities of adolescents and with geriatric syndromes of the elderly. Numerous methods have been developed for the step-by-step estimation of gait parameters, but most are expensive, obtrusive, inconvenient, and/or inaccurate. In this paper, we developed an innovative shoe, called the “Gait Tracker”, with a low power inertial measurement unit (IMU) embedded in a 3D printed sole that provides unobtrusive, continuous, and accurate step-by-step measurement of gait parameters for individual use. This shoe enables out-of-lab gait monitoring in a wide range of activities and over an extended period of time. Experimental results from controlled studies demonstrated that the Gait Tracker can recognize various gait events and provide better accuracy in stride length measurement compared to previous systems and methods.
{"title":"Gait tracker shoe for accurate step-by-step determination of gait parameters","authors":"Yan Zhuang, Jiaqi Gong, D. Kerrigan, B. Bennett, J. Lach, S. Russell","doi":"10.1109/BSN.2016.7516225","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516225","url":null,"abstract":"Step-by-step determination of gait parameters provides insight into the variability of specific gait patterns associated with frequent injuries in the lower extremities of adolescents and with geriatric syndromes of the elderly. Numerous methods have been developed for the step-by-step estimation of gait parameters, but most are expensive, obtrusive, inconvenient, and/or inaccurate. In this paper, we developed an innovative shoe, called the “Gait Tracker”, with a low power inertial measurement unit (IMU) embedded in a 3D printed sole that provides unobtrusive, continuous, and accurate step-by-step measurement of gait parameters for individual use. This shoe enables out-of-lab gait monitoring in a wide range of activities and over an extended period of time. Experimental results from controlled studies demonstrated that the Gait Tracker can recognize various gait events and provide better accuracy in stride length measurement compared to previous systems and methods.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"39 3 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":"125740982","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.2016.7516293
Marc Hesse, Michael Adams, Timm Hormann, U. Rückert
Energy efficiency is the most outstanding design criterion for wireless sensor nodes and especially wireless body sensors. Because a detailed measurement of the system's power consumption is not possible during the design process and often too complex for already manufactured devices, the power consumption has to be estimated. This leads to the need for a comprehensive and modular model for the power consumption of WSNs, which is proposed in this work. Due to the modular structure of the model the user is able to get a first estimate in an early stage of the design process (e.g. choose components) and to get a more accurate estimation later in the design process by lowering the abstraction level. This tackles the demanding trade-off between accuracy and usability in modeling.
{"title":"Towards a comprehensive power consumption model for wireless sensor nodes","authors":"Marc Hesse, Michael Adams, Timm Hormann, U. Rückert","doi":"10.1109/BSN.2016.7516293","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516293","url":null,"abstract":"Energy efficiency is the most outstanding design criterion for wireless sensor nodes and especially wireless body sensors. Because a detailed measurement of the system's power consumption is not possible during the design process and often too complex for already manufactured devices, the power consumption has to be estimated. This leads to the need for a comprehensive and modular model for the power consumption of WSNs, which is proposed in this work. Due to the modular structure of the model the user is able to get a first estimate in an early stage of the design process (e.g. choose components) and to get a more accurate estimation later in the design process by lowering the abstraction level. This tackles the demanding trade-off between accuracy and usability in modeling.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 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":"129783442","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.2016.7516241
Priyanka Rathod, K. George, Nikhil Shinde
In the past several years, significant research has been conducted in the area of real-time emotion recognition. Emotion recognition has several potential applications in education, medicine, assistive technologies and human-machine interaction. A real-time emotion detection device that utilizes heart rate and skin conductance sensors is presented in this paper. OpenCV, open face libraries and insight SDK is utilized to detect emotions from facial expressions. The performance of the device is evaluated using experiments which had subjects watch audiovisual clips in various emotional categories. Also, in order to verify the feasibility of utilizing bio-signals to predict emotions, facial expressions captured from a webcam are processed in parallel to compare and contrast.
在过去的几年里，人们在实时情绪识别领域进行了大量的研究。情感识别在教育、医学、辅助技术和人机交互等领域具有潜在的应用前景。本文介绍了一种利用心率和皮肤电导传感器的实时情绪检测装置。OpenCV、open face libraries和insight SDK用于从面部表情中检测情绪。该装置的性能是通过让受试者观看各种情绪类别的视听片段的实验来评估的。此外，为了验证利用生物信号预测情绪的可行性，从网络摄像头捕获的面部表情被并行处理以进行比较和对比。
{"title":"Bio-signal based emotion detection device","authors":"Priyanka Rathod, K. George, Nikhil Shinde","doi":"10.1109/BSN.2016.7516241","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516241","url":null,"abstract":"In the past several years, significant research has been conducted in the area of real-time emotion recognition. Emotion recognition has several potential applications in education, medicine, assistive technologies and human-machine interaction. A real-time emotion detection device that utilizes heart rate and skin conductance sensors is presented in this paper. OpenCV, open face libraries and insight SDK is utilized to detect emotions from facial expressions. The performance of the device is evaluated using experiments which had subjects watch audiovisual clips in various emotional categories. Also, in order to verify the feasibility of utilizing bio-signals to predict emotions, facial expressions captured from a webcam are processed in parallel to compare and contrast.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"66 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":"126399277","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.2016.7516295
Dawei Fan, Luis Javier Lopez Ruiz, Jiaqi Gong, J. Lach
Energy harvesting offers the promise of mobile sensor systems capable of quasi-perpetual operation, but the discontinuous and dynamic characteristics of harvesting in real-world scenarios - necessary for the design and operation of self-powered systems - are not yet well understood. The paper presents a hardware platform for providing a comprehensive real-world evaluation of two energy harvesting modalities common to body sensor networks: indoor light and thermoelectric. Day-long multi-modal energy harvesting profiles were generated, which were then used to develop a mathematical model to predict real time energy harvesting values from the sampled environmental and human behavioral parameters. Experimental results demonstrate that the model is effective in calculating and predicting harvested energy in real time, and a multi-source scheme for continuous operation of self-powered sensors is demonstrated.
{"title":"Profiling, modeling, and predicting energy harvesting for self-powered body sensor platforms","authors":"Dawei Fan, Luis Javier Lopez Ruiz, Jiaqi Gong, J. Lach","doi":"10.1109/BSN.2016.7516295","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516295","url":null,"abstract":"Energy harvesting offers the promise of mobile sensor systems capable of quasi-perpetual operation, but the discontinuous and dynamic characteristics of harvesting in real-world scenarios - necessary for the design and operation of self-powered systems - are not yet well understood. The paper presents a hardware platform for providing a comprehensive real-world evaluation of two energy harvesting modalities common to body sensor networks: indoor light and thermoelectric. Day-long multi-modal energy harvesting profiles were generated, which were then used to develop a mathematical model to predict real time energy harvesting values from the sampled environmental and human behavioral parameters. Experimental results demonstrate that the model is effective in calculating and predicting harvested energy in real time, and a multi-source scheme for continuous operation of self-powered sensors is demonstrated.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"263 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":"121424755","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.2016.7516278
Nicolò Strozzi, Federico Parisi, G. Ferrari
In this paper, we compare two novel algorithms for pedestrian navigation based on signals collected by a single wearable Magnetic, Angular Rate, and Gravity (MARG) sensor. The two navigation algorithms, denoted as Enhanced Pedestrian Dead Reckoning (EPDR) and De-Drifted Propagation (DDP), require the placement of the MARG sensor on the foot or on the chest of the test subject, respectively. Different methods for gait characterization are compared, evaluating navigation dynamics by using data collected through an extensive experimental campaign. The main goal of this research is to investigate the peculiarities of different inertial navigation algorithms, in order to highlight the impact of the sensor's placement, together with inertial sensor issues. Considering a closed path (i.e., ending at the starting point), the relative distance error between the starting point and the final estimated position is about 2% of the total travelled distance for both DDP and EPDR navigation algorithms. On the other hand, the error between the initial heading angle and the final estimated one is approximately 10° for EPDR and 7° for DDP, respectively.
在本文中，我们比较了两种基于单个可穿戴磁、角速率和重力(MARG)传感器收集的信号的行人导航新算法。这两种导航算法分别被称为增强行人航迹推算(Enhanced Pedestrian Dead Reckoning, EPDR)和去漂移传播(de - drift Propagation, DDP)，它们要求将MARG传感器分别放置在受试者的脚上或胸部。比较了不同的步态表征方法，通过广泛的实验活动收集的数据来评估导航动力学。本研究的主要目的是研究不同惯性导航算法的特点，以突出传感器放置的影响，以及惯性传感器问题。考虑闭合路径(即在起始点结束)，对于DDP和EPDR导航算法，起始点与最终估计位置之间的相对距离误差约为总行进距离的2%。另一方面，EPDR和DDP的初始航向角与最终估计航向角的误差分别约为10°和7°。
{"title":"On single sensor-based inertial navigation","authors":"Nicolò Strozzi, Federico Parisi, G. Ferrari","doi":"10.1109/BSN.2016.7516278","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516278","url":null,"abstract":"In this paper, we compare two novel algorithms for pedestrian navigation based on signals collected by a single wearable Magnetic, Angular Rate, and Gravity (MARG) sensor. The two navigation algorithms, denoted as Enhanced Pedestrian Dead Reckoning (EPDR) and De-Drifted Propagation (DDP), require the placement of the MARG sensor on the foot or on the chest of the test subject, respectively. Different methods for gait characterization are compared, evaluating navigation dynamics by using data collected through an extensive experimental campaign. The main goal of this research is to investigate the peculiarities of different inertial navigation algorithms, in order to highlight the impact of the sensor's placement, together with inertial sensor issues. Considering a closed path (i.e., ending at the starting point), the relative distance error between the starting point and the final estimated position is about 2% of the total travelled distance for both DDP and EPDR navigation algorithms. On the other hand, the error between the initial heading angle and the final estimated one is approximately 10° for EPDR and 7° for DDP, respectively.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"85 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":"124135710","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.2016.7516239
Mathieu Simonnet, Bernard Gourvennec
In the present study we focused on heart rate sensors and compared the acceptability and usability of the various devices candidates to feed the PRECIOUS (PREventive Care Infrastructure based On Ubiquitous Sensing) system. More precisely, smart-watch, chest-belt and 2-points-electrodes have been tested by users during 24 hours. Each device test lead to consult lifestyle reports about stress, sleep and physical activity. During this experimentation 11 participants completed different acceptability questionnaires. The first results interpretation revealed which sensor is the most acceptable and gave insight into how data reliability of the different devices influenced their respective acceptability in the daily life.
{"title":"Heart rate sensors acceptability: Data reliability vs. ease of use","authors":"Mathieu Simonnet, Bernard Gourvennec","doi":"10.1109/BSN.2016.7516239","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516239","url":null,"abstract":"In the present study we focused on heart rate sensors and compared the acceptability and usability of the various devices candidates to feed the PRECIOUS (PREventive Care Infrastructure based On Ubiquitous Sensing) system. More precisely, smart-watch, chest-belt and 2-points-electrodes have been tested by users during 24 hours. Each device test lead to consult lifestyle reports about stress, sleep and physical activity. During this experimentation 11 participants completed different acceptability questionnaires. The first results interpretation revealed which sensor is the most acceptable and gave insight into how data reliability of the different devices influenced their respective acceptability in the daily life.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"11 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":"117340537","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.2016.7516224
Rui Zhang, Severin Bernhart, O. Amft
We utilise smart eyeglasses for dietary monitoring, in particular to sense food chewing. Our approach is based on a 3D-printed regular eyeglasses design that could accommodate processing electronics and Electromyography (EMG) electrodes. Electrode positioning was analysed and an optimal electrode placement at the temples was identified. We further compared gel and dry fabric electrodes. For the subsequent analysis, fabric electrodes were attached to the eyeglasses frame. The eyeglasses were used in a data recording study with eight participants eating different foods. Two chewing cycle detection methods and two food classification algorithms were compared. Detection rates for individual chewing cycles reached a precision and recall of 80%. For five foods, classification accuracy for individual chewing cycles varied between 43% and 71%. Majority voting across intake sequences improved accuracy, ranging between 63% and 84%. We concluded that EMG-based chewing analysis using smart eyeglasses can contribute essential chewing structure information to dietary monitoring systems, while the eyeglasses remain inconspicuous and thus could be continuously used.
{"title":"Diet eyeglasses: Recognising food chewing using EMG and smart eyeglasses","authors":"Rui Zhang, Severin Bernhart, O. Amft","doi":"10.1109/BSN.2016.7516224","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516224","url":null,"abstract":"We utilise smart eyeglasses for dietary monitoring, in particular to sense food chewing. Our approach is based on a 3D-printed regular eyeglasses design that could accommodate processing electronics and Electromyography (EMG) electrodes. Electrode positioning was analysed and an optimal electrode placement at the temples was identified. We further compared gel and dry fabric electrodes. For the subsequent analysis, fabric electrodes were attached to the eyeglasses frame. The eyeglasses were used in a data recording study with eight participants eating different foods. Two chewing cycle detection methods and two food classification algorithms were compared. Detection rates for individual chewing cycles reached a precision and recall of 80%. For five foods, classification accuracy for individual chewing cycles varied between 43% and 71%. Majority voting across intake sequences improved accuracy, ranging between 63% and 84%. We concluded that EMG-based chewing analysis using smart eyeglasses can contribute essential chewing structure information to dietary monitoring systems, while the eyeglasses remain inconspicuous and thus could be continuously used.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"4 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":"123923738","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.2016.7516264
P. Bhat, Ajay K. Gupta
The purpose of this study is to develop a localized muscle fatigue detection system to assist patients during isometric exercise. A mobile device is fastened to the forehand of the subject to receive electromyography (EMG) signals sent wirelessly from BITalino board. Our proposed system then uses surface electromyography (sEMG) technique to capture electromyography (EMG) signals that are processed in time-frequency domain using short-term Fourier transform. The vibration speed received through accelerometer sensor caused by the muscle fatigue at biceps brachii of a subject is calculated in parallel. The downward shift in Median Frequency and increase in vibration speed are taken as parameters to determine the localized muscle fatigue. Results indicate that localized muscle fatigue can be observed effectively with these two parameters combined together.
{"title":"A novel approach to detect localized muscle fatigue during isometric exercises","authors":"P. Bhat, Ajay K. Gupta","doi":"10.1109/BSN.2016.7516264","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516264","url":null,"abstract":"The purpose of this study is to develop a localized muscle fatigue detection system to assist patients during isometric exercise. A mobile device is fastened to the forehand of the subject to receive electromyography (EMG) signals sent wirelessly from BITalino board. Our proposed system then uses surface electromyography (sEMG) technique to capture electromyography (EMG) signals that are processed in time-frequency domain using short-term Fourier transform. The vibration speed received through accelerometer sensor caused by the muscle fatigue at biceps brachii of a subject is calculated in parallel. The downward shift in Median Frequency and increase in vibration speed are taken as parameters to determine the localized muscle fatigue. Results indicate that localized muscle fatigue can be observed effectively with these two parameters combined together.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"59 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":"122085168","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.2016.7516233
J. A. Khan, Haroon Ali Akbar, Usama Pervaiz, Osman Hassan
This paper presents a low cost, low power and wireless wearable solution for real-time analysis and monitoring of cardiac activity co-related with physical activity. Utilizing an analogue filter chain for signal conditioning, the device performs continuous measurement of the Electrocardiogram. The wearable also includes an accelerometer enabling it to detect the current physical activity along with body orientation. The device communicates wirelessly, using Bluetooth Smart /Bluetooth Low Energy, with a smart phone, where a complete analysis can be performed on the received data, and decisions about the current health conditions can be made.
{"title":"A wearable wireless sensor for cardiac monitoring","authors":"J. A. Khan, Haroon Ali Akbar, Usama Pervaiz, Osman Hassan","doi":"10.1109/BSN.2016.7516233","DOIUrl":"https://doi.org/10.1109/BSN.2016.7516233","url":null,"abstract":"This paper presents a low cost, low power and wireless wearable solution for real-time analysis and monitoring of cardiac activity co-related with physical activity. Utilizing an analogue filter chain for signal conditioning, the device performs continuous measurement of the Electrocardiogram. The wearable also includes an accelerometer enabling it to detect the current physical activity along with body orientation. The device communicates wirelessly, using Bluetooth Smart /Bluetooth Low Energy, with a smart phone, where a complete analysis can be performed on the received data, and decisions about the current health conditions can be made.","PeriodicalId":205735,"journal":{"name":"2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"27 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":"123519168","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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