Pub Date : 2013-05-06DOI: 10.1109/BSN.2013.6575501
Ulf Jensen, Franziska Prade, B. Eskofier
The collection of kinematic data with a head-worn sensor is a promising approach for swimming data analysis in the context of athlete support systems. We present a new approach of analyzing these data and describe a system that segments the lanes of a swimming session and classifies the swimming style of each lane. Special emphasis was put on the algorithm efficiency and the analysis of the resource demands to be able to port the implementation to an embedded microcontroller. For developing the system, data of twelve subjects was collected. The data incorporated two different turn styles that mark the end of a lane as well as the four main swimming styles backstroke, breaststroke, butterfly and freestyle. All turns were successfully identified from the turn detection. Our fully automatic swimming style classification reached a classification rate of 95.0%. The results from the resource consumption analysis can be used to support the decision for the embedded target hardware of a head-worn swimming training system.
{"title":"Classification of kinematic swimming data with emphasis on resource consumption","authors":"Ulf Jensen, Franziska Prade, B. Eskofier","doi":"10.1109/BSN.2013.6575501","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575501","url":null,"abstract":"The collection of kinematic data with a head-worn sensor is a promising approach for swimming data analysis in the context of athlete support systems. We present a new approach of analyzing these data and describe a system that segments the lanes of a swimming session and classifies the swimming style of each lane. Special emphasis was put on the algorithm efficiency and the analysis of the resource demands to be able to port the implementation to an embedded microcontroller. For developing the system, data of twelve subjects was collected. The data incorporated two different turn styles that mark the end of a lane as well as the four main swimming styles backstroke, breaststroke, butterfly and freestyle. All turns were successfully identified from the turn detection. Our fully automatic swimming style classification reached a classification rate of 95.0%. The results from the resource consumption analysis can be used to support the decision for the embedded target hardware of a head-worn swimming training system.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115737822","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575517
D. Mehta, M. Zañartu, J. Stan, S. Feng, H. Cheyne, R. Hillman
Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, termed vocal hyperfunction. Thus an ongoing goal in clinical voice assessment is the long-term monitoring of noninvasively derived measures to track hyperfunction. This paper reports on a smartphone-based voice health monitor that records the high-bandwidth accelerometer signal from the neck skin above the collarbone. Data collection is under way from patients with vocal hyperfunction and matched-control subjects to create a dataset designed to identify the best set of diagnostic measures for hyperfunctional patterns of vocal behavior. Vocal status is tracked from neck acceleration using previously-developed vocal dose measures and novel model-based features of glottal airflow estimates. Clinically, the treatment of hyperfunctional disorders would be greatly enhanced by the ability to unobtrusively monitor and quantify detrimental behaviors and, ultimately, to provide real-time biofeedback that could facilitate healthier voice use.
{"title":"Smartphone-based detection of voice disorders by long-term monitoring of neck acceleration features","authors":"D. Mehta, M. Zañartu, J. Stan, S. Feng, H. Cheyne, R. Hillman","doi":"10.1109/BSN.2013.6575517","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575517","url":null,"abstract":"Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, termed vocal hyperfunction. Thus an ongoing goal in clinical voice assessment is the long-term monitoring of noninvasively derived measures to track hyperfunction. This paper reports on a smartphone-based voice health monitor that records the high-bandwidth accelerometer signal from the neck skin above the collarbone. Data collection is under way from patients with vocal hyperfunction and matched-control subjects to create a dataset designed to identify the best set of diagnostic measures for hyperfunctional patterns of vocal behavior. Vocal status is tracked from neck acceleration using previously-developed vocal dose measures and novel model-based features of glottal airflow estimates. Clinically, the treatment of hyperfunctional disorders would be greatly enhanced by the ability to unobtrusively monitor and quantify detrimental behaviors and, ultimately, to provide real-time biofeedback that could facilitate healthier voice use.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116232381","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575496
Omkar Pradhan, K. Newman, F. Barnes
An inverted-F antenna with meandered line is characterized in this paper in terms of its proximity to the human body. Radiation characteristics are simulated and analyzed in the context of proximity of the antenna to human body tissue. The dependence of radiation characteristics like radiation pattern, resonant frequency, radiation efficiency, gain and front-to-back ratio; on the type & dimensions of body model is reported and discussed. Furthermore an improvement in the antenna design using a High Impedance Structure (HIS) as a ground plane is suggested. The antenna operation with this ground plane is simulated for radiation characteristics in close proximity to the body.
{"title":"Parametric analysis of meandered inverted-F antenna and use of a High impedance surface based ground plane for WBAN applications","authors":"Omkar Pradhan, K. Newman, F. Barnes","doi":"10.1109/BSN.2013.6575496","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575496","url":null,"abstract":"An inverted-F antenna with meandered line is characterized in this paper in terms of its proximity to the human body. Radiation characteristics are simulated and analyzed in the context of proximity of the antenna to human body tissue. The dependence of radiation characteristics like radiation pattern, resonant frequency, radiation efficiency, gain and front-to-back ratio; on the type & dimensions of body model is reported and discussed. Furthermore an improvement in the antenna design using a High Impedance Structure (HIS) as a ground plane is suggested. The antenna operation with this ground plane is simulated for radiation characteristics in close proximity to the body.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"2001 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123755749","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575481
L. Bouarfa, P. Bembnowicz, B. Crewther, D. Jarchi, Guang-Zhong Yang
Assessing psychological stress is essential for monitoring general health and wellbeing. One key element is the detection of the stimulus, i.e., stressor that evokes a stress response. Visual and verbal stimuli are elementary arousal elements of daily stress responses. The study aim was to discriminate the stress responses from watching videos and speaking using electrodermal activity (EDA) and heart rate variability (HRV) measures. A cohort of 12 subjects completed a laboratory experiment comprising of 4 psychological tasks (watching a relaxing video and a violent video, speaking by counting and speaking on an unknown topic). In total, 17 physiological features were calculated from the EDA and HRV signals. Four classifiers were investigated regarding their ability to discriminate between verbal and visual stimulated stress responses with a maximum accuracy of 92% achieved. This demonstrates that the measured signals have potential for tracking and differentiating the stress responses of watching videos or speaking in real-time by using wearable EDA and HRV devices.
{"title":"Profiling visual and verbal stress responses using electrodermal heart rate and hormonal measures","authors":"L. Bouarfa, P. Bembnowicz, B. Crewther, D. Jarchi, Guang-Zhong Yang","doi":"10.1109/BSN.2013.6575481","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575481","url":null,"abstract":"Assessing psychological stress is essential for monitoring general health and wellbeing. One key element is the detection of the stimulus, i.e., stressor that evokes a stress response. Visual and verbal stimuli are elementary arousal elements of daily stress responses. The study aim was to discriminate the stress responses from watching videos and speaking using electrodermal activity (EDA) and heart rate variability (HRV) measures. A cohort of 12 subjects completed a laboratory experiment comprising of 4 psychological tasks (watching a relaxing video and a violent video, speaking by counting and speaking on an unknown topic). In total, 17 physiological features were calculated from the EDA and HRV signals. Four classifiers were investigated regarding their ability to discriminate between verbal and visual stimulated stress responses with a maximum accuracy of 92% achieved. This demonstrates that the measured signals have potential for tracking and differentiating the stress responses of watching videos or speaking in real-time by using wearable EDA and HRV devices.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127572864","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575515
N. Alshurafa, Wenyao Xu, Jason J. Liu, Ming-chun Huang, B. Mortazavi, M. Sarrafzadeh, C. Roberts
Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates (MET) and extracting human context awareness from on-body inertial sensors. Many classifiers that train on an activity at a subset of intensity levels fail to classify the same activity at other intensity levels. This demonstrates weakness in the underlying activity model. Training a classifier for an activity at every intensity level is also not practical. In this paper we tackle a novel intensity-independent activity recognition application where the class labels exhibit large variability, the data is of high dimensionality, and clustering algorithms are necessary. We propose a new robust Stochastic Approximation framework for enhanced classification of such data. Experiments are reported for each dataset using two clustering techniques, K-Means and Gaussian Mixture Models. The Stochastic Approximation algorithm consistently outperforms other well-known classification schemes which validates the use of our proposed clustered data representation.
{"title":"Robust human intensity-varying activity recognition using Stochastic Approximation in wearable sensors","authors":"N. Alshurafa, Wenyao Xu, Jason J. Liu, Ming-chun Huang, B. Mortazavi, M. Sarrafzadeh, C. Roberts","doi":"10.1109/BSN.2013.6575515","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575515","url":null,"abstract":"Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates (MET) and extracting human context awareness from on-body inertial sensors. Many classifiers that train on an activity at a subset of intensity levels fail to classify the same activity at other intensity levels. This demonstrates weakness in the underlying activity model. Training a classifier for an activity at every intensity level is also not practical. In this paper we tackle a novel intensity-independent activity recognition application where the class labels exhibit large variability, the data is of high dimensionality, and clustering algorithms are necessary. We propose a new robust Stochastic Approximation framework for enhanced classification of such data. Experiments are reported for each dataset using two clustering techniques, K-Means and Gaussian Mixture Models. The Stochastic Approximation algorithm consistently outperforms other well-known classification schemes which validates the use of our proposed clustered data representation.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129700384","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575494
M. Moazeni, B. Mortazavi, M. Sarrafzadeh
Although most of the medical and healthcare monitoring systems generate multi-dimensional time series (via multiple sensors), most of the work by research community has been focused on defining distance metrics and matching algorithms to improve accuracy and optimize performance of search in single dimensional time series. In this work we motivate the need for multidimensional time series matching and propose a scalable technique that has high accuracy in presence of noise, uncertainty, and lack of synchronization between dimensions. We focus on two medical monitoring devices and their applications to showcase the advantages, performance, and accuracy of our multi-dimensional time series search technique. We demonstrate effectiveness of our signal search technique by using precision and recall metrics.
{"title":"Multi-dimensional signal search with applications in remote medical monitoring","authors":"M. Moazeni, B. Mortazavi, M. Sarrafzadeh","doi":"10.1109/BSN.2013.6575494","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575494","url":null,"abstract":"Although most of the medical and healthcare monitoring systems generate multi-dimensional time series (via multiple sensors), most of the work by research community has been focused on defining distance metrics and matching algorithms to improve accuracy and optimize performance of search in single dimensional time series. In this work we motivate the need for multidimensional time series matching and propose a scalable technique that has high accuracy in presence of noise, uncertainty, and lack of synchronization between dimensions. We focus on two medical monitoring devices and their applications to showcase the advantages, performance, and accuracy of our multi-dimensional time series search technique. We demonstrate effectiveness of our signal search technique by using precision and recall metrics.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121352841","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575472
Nesa Mouzehkesh, T. Zia, Saman Shafigh, Lihong Zheng
Wireless Body Area Networks (WBAN) have emerged as an extension to conventional wireless sensor networks in recent years to comply with the needs in providing timely and effective response in healthcare as one of the many target applications such networks have. The traffic of a WBAN is diverse due to different monitoring tasks carried on by sensor nodes. It brings difficulty in how to efficiently organize the access to the medium for the dynamic and various generated traffic. This paper analyses the traffic diversity problem in WBAN for healthcare applications and proposes a dynamic delayed Medium Access Control (MAC) algorithm. A fuzzy logic system is used to incorporate both application and protocol related parameters of the real time traffic to make the backoff time produced in IEEE 802.15.4 MAC protocol traffic adaptive. The simulation results demonstrate a significant reliability in packet transmissions and decrease in the latency with no change in energy consumption level.
{"title":"D2MAC: Dynamic delayed Medium Access Control (MAC) protocol with fuzzy technique for Wireless Body Area Networks","authors":"Nesa Mouzehkesh, T. Zia, Saman Shafigh, Lihong Zheng","doi":"10.1109/BSN.2013.6575472","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575472","url":null,"abstract":"Wireless Body Area Networks (WBAN) have emerged as an extension to conventional wireless sensor networks in recent years to comply with the needs in providing timely and effective response in healthcare as one of the many target applications such networks have. The traffic of a WBAN is diverse due to different monitoring tasks carried on by sensor nodes. It brings difficulty in how to efficiently organize the access to the medium for the dynamic and various generated traffic. This paper analyses the traffic diversity problem in WBAN for healthcare applications and proposes a dynamic delayed Medium Access Control (MAC) algorithm. A fuzzy logic system is used to incorporate both application and protocol related parameters of the real time traffic to make the backoff time produced in IEEE 802.15.4 MAC protocol traffic adaptive. The simulation results demonstrate a significant reliability in packet transmissions and decrease in the latency with no change in energy consumption level.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116983672","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 : 2013-05-06DOI: 10.1109/BSN.2013.6575461
F. Dadashi, A. Arami, F. Crettenand, G. Millet, J. Komar, L. Seifert, K. Aminian
The recent advances in wearable inertial sensors opened a new horizon for pervasive measurement of human locomotion even in aquatic environment. In this paper we proposed an automatic approach of detecting the key temporal events of breaststroke swimming as a tentatively explored technique due to the complexity of the stroke. We used two inertial measurement units worn on the right arm and right leg of seven swimmers to capture the kinematics of the breaststroke. The detection of the temporal phases from the inertial signals was undertaken in the framework of a Hidden Markov Model (HMM). Supervised learning of the HMM parameters was achieved using the reference data from manual video analysis by an expert. The outputs of two well-known classifiers on the inertial signals were fused to unfold the input space of the HMM for an enhanced performance. An average correct phase detection of 93.5% for the arm stroke, 94.4% for the leg stroke and the minimum precision of 67 milliseconds in detection of the key events, suggests the accuracy of the method.
{"title":"A Hidden Markov Model of the breaststroke swimming temporal phases using wearable inertial measurement units","authors":"F. Dadashi, A. Arami, F. Crettenand, G. Millet, J. Komar, L. Seifert, K. Aminian","doi":"10.1109/BSN.2013.6575461","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575461","url":null,"abstract":"The recent advances in wearable inertial sensors opened a new horizon for pervasive measurement of human locomotion even in aquatic environment. In this paper we proposed an automatic approach of detecting the key temporal events of breaststroke swimming as a tentatively explored technique due to the complexity of the stroke. We used two inertial measurement units worn on the right arm and right leg of seven swimmers to capture the kinematics of the breaststroke. The detection of the temporal phases from the inertial signals was undertaken in the framework of a Hidden Markov Model (HMM). Supervised learning of the HMM parameters was achieved using the reference data from manual video analysis by an expert. The outputs of two well-known classifiers on the inertial signals were fused to unfold the input space of the HMM for an enhanced performance. An average correct phase detection of 93.5% for the arm stroke, 94.4% for the leg stroke and the minimum precision of 67 milliseconds in detection of the key events, suggests the accuracy of the method.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126473907","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 : 2013-05-01DOI: 10.1109/BSN.2013.6575498
S. Lee, Hassan Ghasemzadeh, B. Mortazavi, Andrew Yew, Ruth Getachew, M. Razaghy, Nima Ghalehsari, Brian H. Paak, Jordan H. Garst, Marie Espinal, Jon Kimball, Daniel C. Lu, M. Sarrafzadeh
Hyperexcitability in hand is a disorder characterized by exaggerated muscle movement, and is a common symptom associated with neuro-degenerative diseases and spinal cord injuries. Current assessment methods for hyperexcitability rely on subjective examination, or on methods that evaluate the overall hand grip performance without particularization in the excitation. This paper introduces a system that utilizes an inexpensive body sensor device combined with a series of signal processing units that extract information specifically related to physiological phenomena generated by hyperexcitability. A clinical cohort study has been conducted on nine patients with cervical spinal cord injuries (mean age 58.2 ± 13.5). The experimental results show that the proposed signal processing mechanism accurately detects and analyzes the body signal. The medical significance of the experimental results is also investigated. This opens up a new opportunity for patients and clinical professionals to obtain accurate feedback of patient's motor function in an economical and ubiquitous manner.
{"title":"Objective assessment of overexcited hand movements using a lightweight sensory device","authors":"S. Lee, Hassan Ghasemzadeh, B. Mortazavi, Andrew Yew, Ruth Getachew, M. Razaghy, Nima Ghalehsari, Brian H. Paak, Jordan H. Garst, Marie Espinal, Jon Kimball, Daniel C. Lu, M. Sarrafzadeh","doi":"10.1109/BSN.2013.6575498","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575498","url":null,"abstract":"Hyperexcitability in hand is a disorder characterized by exaggerated muscle movement, and is a common symptom associated with neuro-degenerative diseases and spinal cord injuries. Current assessment methods for hyperexcitability rely on subjective examination, or on methods that evaluate the overall hand grip performance without particularization in the excitation. This paper introduces a system that utilizes an inexpensive body sensor device combined with a series of signal processing units that extract information specifically related to physiological phenomena generated by hyperexcitability. A clinical cohort study has been conducted on nine patients with cervical spinal cord injuries (mean age 58.2 ± 13.5). The experimental results show that the proposed signal processing mechanism accurately detects and analyzes the body signal. The medical significance of the experimental results is also investigated. This opens up a new opportunity for patients and clinical professionals to obtain accurate feedback of patient's motor function in an economical and ubiquitous manner.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132158044","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 : 2013-05-01DOI: 10.1109/BSN.2013.6575502
R. D'Angelo, M. Trakimas, S. Sonkusale, S. Aeron
Wireless physiological sensors are often limited by energy consumption of the hardware. Power consumption is typically related to the amount of data being transmitted, conventionally the Nyquist rate which is twice the bandwidth of the signal. However, if the signals are sparse in a known basis, compressed sensing facilitates accurate reconstruction of data when sampled below the Nyquist rate. Thus, power consumption at the sensor node could be improved, which would allow long-term use of wireless physiological sensors. We have implemented a random sampling based compressed analog to information converter (AIC) in 90nm CMOS technology. Sufficiently sparse signals were reconstructed using the ℓ1-minimization algorithm. Here we present experimental results that demonstrate reconstruction of non-sparse signals, in this case EEG, by using an ℓ1, 2 regularization algorithm exploiting group sparsity. These results demonstrate the performance achievable by physical compressed sensing AIC systems for brain computer interface applications.
{"title":"Compressed sensing of EEG using a random sampling ADC in 90nm CMOS","authors":"R. D'Angelo, M. Trakimas, S. Sonkusale, S. Aeron","doi":"10.1109/BSN.2013.6575502","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575502","url":null,"abstract":"Wireless physiological sensors are often limited by energy consumption of the hardware. Power consumption is typically related to the amount of data being transmitted, conventionally the Nyquist rate which is twice the bandwidth of the signal. However, if the signals are sparse in a known basis, compressed sensing facilitates accurate reconstruction of data when sampled below the Nyquist rate. Thus, power consumption at the sensor node could be improved, which would allow long-term use of wireless physiological sensors. We have implemented a random sampling based compressed analog to information converter (AIC) in 90nm CMOS technology. Sufficiently sparse signals were reconstructed using the ℓ1-minimization algorithm. Here we present experimental results that demonstrate reconstruction of non-sparse signals, in this case EEG, by using an ℓ1, 2 regularization algorithm exploiting group sparsity. These results demonstrate the performance achievable by physical compressed sensing AIC systems for brain computer interface applications.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122171954","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：