Pub Date : 2017-06-01DOI: 10.1109/BSN.2017.7936029
Yomna El-Saboni, G. Conway, S. Cotton, W. Scanlon
Applications are emerging that feature multiple implanted devices as part of an intra-body network. Establishing high bandwidth communications between such devices is challenging and there is a need to understand the principles of the intra-body channel. This paper presents a numerical analysis of the wave propagation between identical antennas in the MedRadio operating band (2.36–2.40 GHz) within cylindrical three layered tissue equivalent phantoms. The results presented show the effect of dielectric boundaries and different tissue properties on dominant wave propagation paths and link gain which provides essential information for efficient system design.
{"title":"Radiowave propagation characteristics of the intra-body channel at 2.38 GHz","authors":"Yomna El-Saboni, G. Conway, S. Cotton, W. Scanlon","doi":"10.1109/BSN.2017.7936029","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936029","url":null,"abstract":"Applications are emerging that feature multiple implanted devices as part of an intra-body network. Establishing high bandwidth communications between such devices is challenging and there is a need to understand the principles of the intra-body channel. This paper presents a numerical analysis of the wave propagation between identical antennas in the MedRadio operating band (2.36–2.40 GHz) within cylindrical three layered tissue equivalent phantoms. The results presented show the effect of dielectric boundaries and different tissue properties on dominant wave propagation paths and link gain which provides essential information for efficient system design.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121137020","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 : 2017-05-10DOI: 10.1109/BSN.2017.7936001
G. Colson, P. Laurent, Pierre Bellier, S. Stoukatch, F. Dupont, M. Kraft
Nowadays, electronic devices are more and more compact and can be integrated in nearly every object. One of the remaining challenges is to provide smarter ways to power those electronic devices. Because of the small amount of energy needed by the latest ultra-low power systems, energy harvesting from the environment becomes a viable solution to power them. In this work, we present the integration of an electronic device and an electrodynamic energy harvester (EH) in a shoe. The electronic device measures the acceleration along one axis at a sampling rate of 30 Hz and sends the data every second using a wireless link. The data are then collected by a gateway and processed to count the number of steps, calculate the contact time and the flying time of the foot. To perform this function, the device requires an average power of 951 µW which is provided by the EH.
{"title":"Smart-shoe self-powered by walking","authors":"G. Colson, P. Laurent, Pierre Bellier, S. Stoukatch, F. Dupont, M. Kraft","doi":"10.1109/BSN.2017.7936001","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936001","url":null,"abstract":"Nowadays, electronic devices are more and more compact and can be integrated in nearly every object. One of the remaining challenges is to provide smarter ways to power those electronic devices. Because of the small amount of energy needed by the latest ultra-low power systems, energy harvesting from the environment becomes a viable solution to power them. In this work, we present the integration of an electronic device and an electrodynamic energy harvester (EH) in a shoe. The electronic device measures the acceleration along one axis at a sampling rate of 30 Hz and sends the data every second using a wireless link. The data are then collected by a gateway and processed to count the number of steps, calculate the contact time and the flying time of the foot. To perform this function, the device requires an average power of 951 µW which is provided by the EH.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121466414","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 : 2017-05-09DOI: 10.1109/BSN.2017.7936024
J. Hannink, F. Kluge, H. Gassner, J. Klucken, B. Eskofier
Quantifying dynamic postural stability from inertial sensor data is clinically very relevant for treatment and therapy monitoring in neuromuscular diseases, e.g. Parkinson's disease (PD). We extract peak accelerations in movement direction during the loading phase and in vertical direction at ground contact from gravity-free acceleration signals captured at the patient's feet as novel markers of dynamic postural stability. The approach is tested on a dataset containing 100 idiopathic PD patients and 50 age- and weight-matched healthy controls. Experiments include group separation of the controls and PD patients with/without postural instability as assessed by the pull test and analysis of correlations to existing parameters from inertial sensor data. Both markers show significant clinical differences, specifically between the two conditions in the PD group. At least one parameter provides complementary information to the existing set of spatio-temporal gait parameters while the other one correlates highly to gait velocity but might be measurable more precisely. In conclusion, the inertial sensor derived markers can detect postural instability but further research in this domain is needed.
{"title":"Quantifying postural instability in Parkinsonian gait from inertial sensor data during standardised clinical gait tests","authors":"J. Hannink, F. Kluge, H. Gassner, J. Klucken, B. Eskofier","doi":"10.1109/BSN.2017.7936024","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936024","url":null,"abstract":"Quantifying dynamic postural stability from inertial sensor data is clinically very relevant for treatment and therapy monitoring in neuromuscular diseases, e.g. Parkinson's disease (PD). We extract peak accelerations in movement direction during the loading phase and in vertical direction at ground contact from gravity-free acceleration signals captured at the patient's feet as novel markers of dynamic postural stability. The approach is tested on a dataset containing 100 idiopathic PD patients and 50 age- and weight-matched healthy controls. Experiments include group separation of the controls and PD patients with/without postural instability as assessed by the pull test and analysis of correlations to existing parameters from inertial sensor data. Both markers show significant clinical differences, specifically between the two conditions in the PD group. At least one parameter provides complementary information to the existing set of spatio-temporal gait parameters while the other one correlates highly to gait velocity but might be measurable more precisely. In conclusion, the inertial sensor derived markers can detect postural instability but further research in this domain is needed.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116026208","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 : 2017-05-09DOI: 10.1109/BSN.2017.7936037
Shoaib Mohammed, I. Tashev
In body sensor networks, the need to brace sensing devices firmly to the body raises a fundamental barrier to usability. In this paper, we examine the effects of sensing from devices that do not face this mounting limitation. With sensors integrated into common pieces of clothing, we demonstrate that signals in such free-mode body sensor networks are contaminated heavily with motion artifacts leading to mean signal-to-noise ratios (SNRs) as low as −12 dB. Further, we show that motion artifacts at these SNR levels reduce the F1-score of a state-of-the-art algorithm for human-activity recognition by up to 77.1%. In order to mitigate these artifacts, we evaluate the use of statistical (Kalman Filters) and data-driven (Neural Networks) techniques. We show that well-designed methods of representing IMU data with deep neural networks can increase SNRs in free-mode body-sensor networks from −12 dB to +18.2 dB and, as a result, improve the F1-score of recognizing gestures by 14.4% and locomotion activities by 55.3%.
{"title":"Unsupervised deep representation learning to remove motion artifacts in free-mode body sensor networks","authors":"Shoaib Mohammed, I. Tashev","doi":"10.1109/BSN.2017.7936037","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936037","url":null,"abstract":"In body sensor networks, the need to brace sensing devices firmly to the body raises a fundamental barrier to usability. In this paper, we examine the effects of sensing from devices that do not face this mounting limitation. With sensors integrated into common pieces of clothing, we demonstrate that signals in such free-mode body sensor networks are contaminated heavily with motion artifacts leading to mean signal-to-noise ratios (SNRs) as low as −12 dB. Further, we show that motion artifacts at these SNR levels reduce the F1-score of a state-of-the-art algorithm for human-activity recognition by up to 77.1%. In order to mitigate these artifacts, we evaluate the use of statistical (Kalman Filters) and data-driven (Neural Networks) techniques. We show that well-designed methods of representing IMU data with deep neural networks can increase SNRs in free-mode body-sensor networks from −12 dB to +18.2 dB and, as a result, improve the F1-score of recognizing gestures by 14.4% and locomotion activities by 55.3%.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114955828","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 : 2017-05-09DOI: 10.1109/BSN.2017.7936022
J. Lacirignola, Christine Weston, Kate Byrd, Erik Metzger, Ninoshka K. Singh, S. Davis, David Maurer, W. Young, P. Collins, J. Balcius, Mark Richter, Jeff Palmer
Lower-limb musculoskeletal injuries are a pervasive problem in the population and military, especially during basic training where load bearing bones and joints are repeatedly subjected to aggressive movements and high forces. The ability to measure these elements is critical to acquisition decisions affecting or influencing cumulative load carriage of the individual Marine/Warfighter. These data might also serve as a critical enabler for prevention of training injuries and development of more quantitative training procedures that focus on mobility and agility. It has been inherently difficult to acquire this data outside of the laboratory in a robust and repeatable way. Herein, we report the construction and testing of a measurement system packaged within a shoe insert that is capable of measuring forces, accelerations, rotations and elevation changes. The ability to take these measurements in a mobile system facilitates new environments to monitor complex biomechanical actions without compromising natural gait rhythms. This can result in new methods for monitoring changes to gait and also help with rehabilitation strategies.
{"title":"Instrumented footwear inserts: A new tool for measuring forces and biomechanical state changes during dynamic movements","authors":"J. Lacirignola, Christine Weston, Kate Byrd, Erik Metzger, Ninoshka K. Singh, S. Davis, David Maurer, W. Young, P. Collins, J. Balcius, Mark Richter, Jeff Palmer","doi":"10.1109/BSN.2017.7936022","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936022","url":null,"abstract":"Lower-limb musculoskeletal injuries are a pervasive problem in the population and military, especially during basic training where load bearing bones and joints are repeatedly subjected to aggressive movements and high forces. The ability to measure these elements is critical to acquisition decisions affecting or influencing cumulative load carriage of the individual Marine/Warfighter. These data might also serve as a critical enabler for prevention of training injuries and development of more quantitative training procedures that focus on mobility and agility. It has been inherently difficult to acquire this data outside of the laboratory in a robust and repeatable way. Herein, we report the construction and testing of a measurement system packaged within a shoe insert that is capable of measuring forces, accelerations, rotations and elevation changes. The ability to take these measurements in a mobile system facilitates new environments to monitor complex biomechanical actions without compromising natural gait rhythms. This can result in new methods for monitoring changes to gait and also help with rehabilitation strategies.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123443991","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 : 2017-05-09DOI: 10.1109/BSN.2017.7936010
D. Dias, Nuno Ferreira, J. P. Cunha
Current mobile revolution is leading to an increase of wearable health devices development and consequently a growth in ambulatory monitoring area. These systems can be applied in ambulatory diseases management and diagnosis, personal health monitoring or sports performance enhancement, providing physiological and body-area ambiance data during daily normal activities. Nowadays several devices in the market have this type of technology, being one of them the VitalJacket® (VJ®), a product from Biodevices, S.A. This device is a medical certified smart t-shirt with textile embedded electronics for ambulatory monitoring of electrocardiogram (ECG), Heart Rate (HR) and Accelerometer (Acc) data that is in the market since 2008.
{"title":"VitalLogger: An adaptable wearable physiology and body-area ambiance data logger for mobile applications","authors":"D. Dias, Nuno Ferreira, J. P. Cunha","doi":"10.1109/BSN.2017.7936010","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936010","url":null,"abstract":"Current mobile revolution is leading to an increase of wearable health devices development and consequently a growth in ambulatory monitoring area. These systems can be applied in ambulatory diseases management and diagnosis, personal health monitoring or sports performance enhancement, providing physiological and body-area ambiance data during daily normal activities. Nowadays several devices in the market have this type of technology, being one of them the VitalJacket® (VJ®), a product from Biodevices, S.A. This device is a medical certified smart t-shirt with textile embedded electronics for ambulatory monitoring of electrocardiogram (ECG), Heart Rate (HR) and Accelerometer (Acc) data that is in the market since 2008.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127591056","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 : 2017-05-09DOI: 10.1109/BSN.2017.7936032
Muhamed Farooq, O. Dehzangi
Steady State Visual Evoked Potential (SSVEP) has been commonly adopted in Brain Computer Interface (BCI) applications. For wearable BCI applications, several aspects of SSVEP-based BCI systems, such as speed, subject variability, and accurate target detection, are under ongoing research investigations. Up to date, Canonical Correlation Analysis (CCA) has been considered the state-of-the-art feature extraction method for SSVEP-based BCI systems. Nevertheless, although CCA outperforms traditional SSVEP detection methods, such as Power Spectral Density Analysis (PSDA), achieving high accuracies when detecting target frequencies is still a challenging task due to user variation and physiological changes in the human body. In this paper, we investigate an SSVEP-based BCI application using wireless EEG recording and an Android tablet-based user interface. We propose a fusion of CCA and PSDA solutions at the score level by dividing their score space into multiple partitions, and extract and combine their complementary discriminative information to minimize the detection error in a linear fashion. We investigated transforming the fusion score space to lower dimensions with the purpose of alleviating redundancy. As such, we employed Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). Our experimental results demonstrated that our proposed score fusion method is effective in reducing the effect of noise and non-stationary elements in EEG dynamics. Average detection accuracies improved from 63% for CCA to 72% for fusion+PCA and further improved to 98% for fusion+LDA.
{"title":"High accuracy wearable SSVEP detection using feature profiling and dimensionality reduction","authors":"Muhamed Farooq, O. Dehzangi","doi":"10.1109/BSN.2017.7936032","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936032","url":null,"abstract":"Steady State Visual Evoked Potential (SSVEP) has been commonly adopted in Brain Computer Interface (BCI) applications. For wearable BCI applications, several aspects of SSVEP-based BCI systems, such as speed, subject variability, and accurate target detection, are under ongoing research investigations. Up to date, Canonical Correlation Analysis (CCA) has been considered the state-of-the-art feature extraction method for SSVEP-based BCI systems. Nevertheless, although CCA outperforms traditional SSVEP detection methods, such as Power Spectral Density Analysis (PSDA), achieving high accuracies when detecting target frequencies is still a challenging task due to user variation and physiological changes in the human body. In this paper, we investigate an SSVEP-based BCI application using wireless EEG recording and an Android tablet-based user interface. We propose a fusion of CCA and PSDA solutions at the score level by dividing their score space into multiple partitions, and extract and combine their complementary discriminative information to minimize the detection error in a linear fashion. We investigated transforming the fusion score space to lower dimensions with the purpose of alleviating redundancy. As such, we employed Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). Our experimental results demonstrated that our proposed score fusion method is effective in reducing the effect of noise and non-stationary elements in EEG dynamics. Average detection accuracies improved from 63% for CCA to 72% for fusion+PCA and further improved to 98% for fusion+LDA.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133440977","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 : 2017-05-09DOI: 10.1109/BSN.2017.7936039
M. O'Reilly, W. Johnston, Cillian Buckley, D. Whelan, B. Caulfield
Inertial measurement unit (IMU) based systems are becoming increasingly popular in the classification of human movement. While research in the area has established the utility of various machine learning classification methods, there is a paucity of evidence investigating the effect of feature selection on classification efficacy. The aim of this study was therefore to investigate the influence of feature selection methodology on the classification accuracy of human movement data. The efficacy of four commonly used feature selection and classification methods were compared using four IMU human movement data sets. Optimisation of classification and features selection methodologies resulted in an overall improvement in F1 score of between 1–8% for all four data sets. The findings from this study illustrate the need for researchers to consider the effect classification and feature selection methodologies may have on system efficacy.
{"title":"The influence of feature selection methods on exercise classification with inertial measurement units","authors":"M. O'Reilly, W. Johnston, Cillian Buckley, D. Whelan, B. Caulfield","doi":"10.1109/BSN.2017.7936039","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936039","url":null,"abstract":"Inertial measurement unit (IMU) based systems are becoming increasingly popular in the classification of human movement. While research in the area has established the utility of various machine learning classification methods, there is a paucity of evidence investigating the effect of feature selection on classification efficacy. The aim of this study was therefore to investigate the influence of feature selection methodology on the classification accuracy of human movement data. The efficacy of four commonly used feature selection and classification methods were compared using four IMU human movement data sets. Optimisation of classification and features selection methodologies resulted in an overall improvement in F1 score of between 1–8% for all four data sets. The findings from this study illustrate the need for researchers to consider the effect classification and feature selection methodologies may have on system efficacy.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124745105","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 : 2017-05-09DOI: 10.1109/BSN.2017.7936040
Cillian Buckley, M. O'Reilly, D. Whelan, A. Farrell, L. Clark, V. Longo, M. Gilchrist, B. Caulfield
The popularity of running has increased in recent years. A rise in the incidence of running-related overuse musculoskeletal injuries has occurred parallel to this. This study investigates the capability of using data from a single inertial measurement unit (IMU) to differentiate between running form in a non-fatigued and fatigued state. Data was captured from an IMU placed on the lumbar spine, right shank and left shank in 21 recreational runners (10 male, 11 female) during separate 400m running trials. The trials were performed prior to and following a fatiguing protocol. Following stride segmentation, IMU signal features were extracted from the labelled (non-fatigued vs fatigued) sensor data and used to train both a Global and Personalised classifier for each individual IMU location. A single IMU on the Lumbar spine displayed 75% accuracy, 73% sensitivity and 77% specificity when using a Global Classifier. A single IMU on the Right Shank displayed 100% accuracy, 100% sensitivity and 100% specificity when using a Personalised Classifier. These results indicate that a single IMU has the potential to differentiate between non-fatigued and fatigued running states with a high level of accuracy.
{"title":"Binary classification of running fatigue using a single inertial measurement unit","authors":"Cillian Buckley, M. O'Reilly, D. Whelan, A. Farrell, L. Clark, V. Longo, M. Gilchrist, B. Caulfield","doi":"10.1109/BSN.2017.7936040","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936040","url":null,"abstract":"The popularity of running has increased in recent years. A rise in the incidence of running-related overuse musculoskeletal injuries has occurred parallel to this. This study investigates the capability of using data from a single inertial measurement unit (IMU) to differentiate between running form in a non-fatigued and fatigued state. Data was captured from an IMU placed on the lumbar spine, right shank and left shank in 21 recreational runners (10 male, 11 female) during separate 400m running trials. The trials were performed prior to and following a fatiguing protocol. Following stride segmentation, IMU signal features were extracted from the labelled (non-fatigued vs fatigued) sensor data and used to train both a Global and Personalised classifier for each individual IMU location. A single IMU on the Lumbar spine displayed 75% accuracy, 73% sensitivity and 77% specificity when using a Global Classifier. A single IMU on the Right Shank displayed 100% accuracy, 100% sensitivity and 100% specificity when using a Personalised Classifier. These results indicate that a single IMU has the potential to differentiate between non-fatigued and fatigued running states with a high level of accuracy.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123482043","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 : 2017-05-09DOI: 10.1109/BSN.2017.7935996
Ziwei Zhu, Sebastian W. Ober, R. Jafari
The cognitive states of students in a lecture can give good indications of student concentration and learning, and therefore, modeling them would have a positive impact on their quality of education by enabling the intervention of instructors. In a traditional class, the instructor would assess the students' level of attention. However, the assessment may not be accurate for a variety of reasons. Additionally, this creates a burden for the instructors. Wearable sensors and signal processing techniques could provide opportunities to assist teachers with this assessment. In this paper, we propose a methodology to model students' cognitive states by leveraging hand motion and heart activity captured with smart watches. Following the application of a sequence of signal processing techniques to the raw data, we generate features, which describe characteristics of the hand motion and heart activity in a group of students. The most prominent features are selected for machine learning algorithms. By applying cross validation, the results of experiments on 30 students in two lectures offer accuracies of 98.99% and 95.78% for predictions of ‘interest level’ and ‘perception of difficulty’ on the topics covered during the lectures.
{"title":"Modeling and detecting student attention and interest level using wearable computers","authors":"Ziwei Zhu, Sebastian W. Ober, R. Jafari","doi":"10.1109/BSN.2017.7935996","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935996","url":null,"abstract":"The cognitive states of students in a lecture can give good indications of student concentration and learning, and therefore, modeling them would have a positive impact on their quality of education by enabling the intervention of instructors. In a traditional class, the instructor would assess the students' level of attention. However, the assessment may not be accurate for a variety of reasons. Additionally, this creates a burden for the instructors. Wearable sensors and signal processing techniques could provide opportunities to assist teachers with this assessment. In this paper, we propose a methodology to model students' cognitive states by leveraging hand motion and heart activity captured with smart watches. Following the application of a sequence of signal processing techniques to the raw data, we generate features, which describe characteristics of the hand motion and heart activity in a group of students. The most prominent features are selected for machine learning algorithms. By applying cross validation, the results of experiments on 30 students in two lectures offer accuracies of 98.99% and 95.78% for predictions of ‘interest level’ and ‘perception of difficulty’ on the topics covered during the lectures.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350283","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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