Pub Date : 2013-05-06DOI: 10.1109/BSN.2013.6575500
Shanshan Chen, J. Lach, O. Amft, M. Altini, J. Penders
Body sensor networks (BSNs) have provided the opportunity to monitor energy expenditure (EE) in daily life and with that information help reduce sedentary behavior and ultimately improve human health. Current approaches for EE estimation using BSNs require tedious annotation of activity types and multiple body sensor nodes during data collection and high accuracy activity classifiers during post processing. These drawbacks impede deploying this technology in daily life — the primary motivation of using BSNs to monitor EE. With the goal of achieving the highest EE estimation accuracy with the least invasiveness and data collection effort, this paper presents an unsupervised, single-node solution for data collection and activity clustering. Motivated by a previous finding that clusters of similar activities tend to have similar regression models for estimating EE, we apply unsupervised clustering to implicitly group activities with homogeneous features and generate specific regression models for each activity cluster without requiring manual annotation. The framework therefore does not require specific activity classification, hence eliminating activity type labels. With leave-one-subject-out cross-validation across 10 subjects, an RMSE of 0.96 kcal/min was achieved, which is comparable to the activity-specific model and improves upon a single regression model.
{"title":"Unsupervised activity clustering to estimate energy expenditure with a single body sensor","authors":"Shanshan Chen, J. Lach, O. Amft, M. Altini, J. Penders","doi":"10.1109/BSN.2013.6575500","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575500","url":null,"abstract":"Body sensor networks (BSNs) have provided the opportunity to monitor energy expenditure (EE) in daily life and with that information help reduce sedentary behavior and ultimately improve human health. Current approaches for EE estimation using BSNs require tedious annotation of activity types and multiple body sensor nodes during data collection and high accuracy activity classifiers during post processing. These drawbacks impede deploying this technology in daily life — the primary motivation of using BSNs to monitor EE. With the goal of achieving the highest EE estimation accuracy with the least invasiveness and data collection effort, this paper presents an unsupervised, single-node solution for data collection and activity clustering. Motivated by a previous finding that clusters of similar activities tend to have similar regression models for estimating EE, we apply unsupervised clustering to implicitly group activities with homogeneous features and generate specific regression models for each activity cluster without requiring manual annotation. The framework therefore does not require specific activity classification, hence eliminating activity type labels. With leave-one-subject-out cross-validation across 10 subjects, an RMSE of 0.96 kcal/min was achieved, which is comparable to the activity-specific model and improves upon a single regression model.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"16 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":"129489289","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.2013.6575479
A. Sano, Rosalind W. Picard
This paper presents the possibility of recognizing sleep dependent memory consolidation using multi-modal sensor data. We collected visual discrimination task (VDT) performance before and after sleep at laboratory, hospital and home for N=24 participants while recording EEG (electroencepharogram), EDA (electrodermal activity) and ACC (accelerometer) or actigraphy data during sleep. We extracted features and applied machine learning techniques (discriminant analysis, support vector machine and k-nearest neighbor) from the sleep data to classify whether the participants showed improvement in the memory task. Our results showed 60–70% accuracy in a binary classification of task performance using EDA or EDA+ACC features, which provided an improvement over the more traditional use of sleep stages (the percentages of slow wave sleep (SWS) in the 1st quarter and rapid eye movement (REM) in the 4th quarter of the night) to predict VDT improvement.
{"title":"Recognition of sleep dependent memory consolidation with multi-modal sensor data","authors":"A. Sano, Rosalind W. Picard","doi":"10.1109/BSN.2013.6575479","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575479","url":null,"abstract":"This paper presents the possibility of recognizing sleep dependent memory consolidation using multi-modal sensor data. We collected visual discrimination task (VDT) performance before and after sleep at laboratory, hospital and home for N=24 participants while recording EEG (electroencepharogram), EDA (electrodermal activity) and ACC (accelerometer) or actigraphy data during sleep. We extracted features and applied machine learning techniques (discriminant analysis, support vector machine and k-nearest neighbor) from the sleep data to classify whether the participants showed improvement in the memory task. Our results showed 60–70% accuracy in a binary classification of task performance using EDA or EDA+ACC features, which provided an improvement over the more traditional use of sleep stages (the percentages of slow wave sleep (SWS) in the 1st quarter and rapid eye movement (REM) in the 4th quarter of the night) to predict VDT improvement.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"19 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":"123191786","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.2013.6575484
Carlo Alberto Boario, Matteo Lasagni, K. Römer
Long-term accurate measurements of core body temperature are essential to study human thermoregulation in ambulatory settings and during exercise, but they are traditionally carried out using highly-invasive techniques. To enable a continuous unobtrusive monitoring of core body temperature on ambulatory patients and exercising athletes, we have designed a wireless wearable system that measures the tympanic temperature inside the ear, as well as skin and environmental temperature, and that allows remote monitoring of the collected measurements. In this paper, we describe the design and implementation of the system and show that it can be used to identify the circadian rhythms of core body temperature, as well as to detect the variation in core body temperature due to prolonged physical exertion. We further describe the lessons learnt during a pilot deployment of our telemetric system on several athletes during the 5th Lübeck Marathon, and discuss the impact of environmental parameters such as temperature and wind on the accuracy and meaningfulness of the measured values.
{"title":"Non-invasive measurement of core body temperature in Marathon runners","authors":"Carlo Alberto Boario, Matteo Lasagni, K. Römer","doi":"10.1109/BSN.2013.6575484","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575484","url":null,"abstract":"Long-term accurate measurements of core body temperature are essential to study human thermoregulation in ambulatory settings and during exercise, but they are traditionally carried out using highly-invasive techniques. To enable a continuous unobtrusive monitoring of core body temperature on ambulatory patients and exercising athletes, we have designed a wireless wearable system that measures the tympanic temperature inside the ear, as well as skin and environmental temperature, and that allows remote monitoring of the collected measurements. In this paper, we describe the design and implementation of the system and show that it can be used to identify the circadian rhythms of core body temperature, as well as to detect the variation in core body temperature due to prolonged physical exertion. We further describe the lessons learnt during a pilot deployment of our telemetric system on several athletes during the 5th Lübeck Marathon, and discuss the impact of environmental parameters such as temperature and wind on the accuracy and meaningfulness of the measured values.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"2741 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":"127487069","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.2013.6575524
C. Clements, Derek Moody, Adam W. Potter, J. Seay, R. Fellin, M. Buller
Heavy loads often subject foot soldiers and first-responders to increased risk musculoskeletal injury (MSI). Identifying excessive loads in real-time could help identify when soldiers are at greater risk of MSI. Using Principal Component Analysis (PCA) we derived a loaded (>35 kg) versus unloaded Naïve Bayesian classification model from 22 male Soldiers (age 20 ± 3.5 yrs, height 1.76 ± 0.09 m and weight 83 ± 13 kg). Using seven-fold cross validation we demonstrated that using only one feature our model accurately classifies heavily loaded versus unloaded over 90% of the time. This technique lends itself to use in real time accelerometry sensors and shows promise for more complex gait analysis.
重负荷经常使步兵和急救人员增加肌肉骨骼损伤(MSI)的风险。实时识别过度负荷可以帮助识别士兵何时面临更大的MSI风险。通过主成分分析(PCA)，我们建立了22名男性士兵(年龄20±3.5岁，身高1.76±0.09 m，体重83±13 kg)的加载(>35 kg)与卸载(>35 kg) Naïve贝叶斯分类模型。通过七重交叉验证，我们证明了仅使用一个特征，我们的模型就能在90%的时间内准确地对重负载和卸载进行分类。这项技术可以用于实时加速度计传感器，并有望用于更复杂的步态分析。
{"title":"Loaded and unloaded foot movement differentiation using chest mounted accelerometer signatures","authors":"C. Clements, Derek Moody, Adam W. Potter, J. Seay, R. Fellin, M. Buller","doi":"10.1109/BSN.2013.6575524","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575524","url":null,"abstract":"Heavy loads often subject foot soldiers and first-responders to increased risk musculoskeletal injury (MSI). Identifying excessive loads in real-time could help identify when soldiers are at greater risk of MSI. Using Principal Component Analysis (PCA) we derived a loaded (>35 kg) versus unloaded Naïve Bayesian classification model from 22 male Soldiers (age 20 ± 3.5 yrs, height 1.76 ± 0.09 m and weight 83 ± 13 kg). Using seven-fold cross validation we demonstrated that using only one feature our model accurately classifies heavily loaded versus unloaded over 90% of the time. This technique lends itself to use in real time accelerometry sensors and shows promise for more complex gait analysis.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"23 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":"126943539","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.2013.6575488
Kevin T. Sweeney, Edmond Mitchell, J. Gaughran, T. Kane, R. Costello, S. Coyle, N. O’Connor, D. Diamond
Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed.
{"title":"Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals","authors":"Kevin T. Sweeney, Edmond Mitchell, J. Gaughran, T. Kane, R. Costello, S. Coyle, N. O’Connor, D. Diamond","doi":"10.1109/BSN.2013.6575488","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575488","url":null,"abstract":"Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"98 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":"115543954","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.2013.6575528
M. Batalin, Eric M. Yuen, B. Dolezal, Denise L. Smith, Christopher Cooper, J. Mapar
Despite significant advances in Personal Protective Equipment (PPE) and enhanced tactics, line of duty deaths (LODD) and injuries due to cardiovascular events in the emergency responder community, specifically fire service, remain at an unacceptably high level each year. To address the tragic loss of life and often debilitating injuries that are too prevalent in the fire service, the Department of Homeland Security, Science and Technology Directorate, has created a Physiological Health Assessment System for Emergency Responders (PHASER) program. PHASER is charged to develop and deploy innovative technology solutions based on the fundamental medical understanding of risk factors to enhance health and safety of emergency responders. One of the outcomes of the program is a low-cost secure networked system — PHASER-Net — capable of remote physiological monitoring, risk profiling, risk mitigation and guidance of the individual emergency responders. The PHASER-Net system has been deployed at multiple fire departments across the country, as well as academic research laboratories for validation, testing and enhancement. From the days of initial deployments, the system proved vital by identifying individuals with high risk of cardiovascular events and providing targeted training guidance for risk mitigation and prevention.
{"title":"PHASER: Physiological Health Assessment System for emergency responders","authors":"M. Batalin, Eric M. Yuen, B. Dolezal, Denise L. Smith, Christopher Cooper, J. Mapar","doi":"10.1109/BSN.2013.6575528","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575528","url":null,"abstract":"Despite significant advances in Personal Protective Equipment (PPE) and enhanced tactics, line of duty deaths (LODD) and injuries due to cardiovascular events in the emergency responder community, specifically fire service, remain at an unacceptably high level each year. To address the tragic loss of life and often debilitating injuries that are too prevalent in the fire service, the Department of Homeland Security, Science and Technology Directorate, has created a Physiological Health Assessment System for Emergency Responders (PHASER) program. PHASER is charged to develop and deploy innovative technology solutions based on the fundamental medical understanding of risk factors to enhance health and safety of emergency responders. One of the outcomes of the program is a low-cost secure networked system — PHASER-Net — capable of remote physiological monitoring, risk profiling, risk mitigation and guidance of the individual emergency responders. The PHASER-Net system has been deployed at multiple fire departments across the country, as well as academic research laboratories for validation, testing and enhancement. From the days of initial deployments, the system proved vital by identifying individuals with high risk of cardiovascular events and providing targeted training guidance for risk mitigation and prevention.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"77 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":"122632516","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.2013.6575463
Mohd. Noor Islam, J. Khan, M. Yuce
Wireless Body Area Network (WBAN) is well known for accessing data from in body and on body devices. Extensive research has been done on Medium Access Control (MAC) protocol for WBAN for different unlicensed ISM bands, such as 868MHz, 915MHz, 2.4MHz and 433 MHz. However, due to vast use of those frequencies for other applications, there is an unavoidable risk for healthcare application. Inductive link method is another conventional way to communicate with an implanted device. But some limitations are also there, such as low data rate and communication range is very low (few centimeters). To emphasize a patient's health safety and to evade the limitation of inductive link method, a new and different frequency band, Medical Implant Communication System (MICS) band (402–405 MHz), has been accepted worldwide for a small range (3 meters) communication between an external device and implanted devices only. But, different international communication authorities have put some rules and restrictions to use the MICS band. Therefore, it is necessary to design a MAC protocol that will comply with the rules and suitable for the traffics of implanted devices communication network. In this paper, we propose a MAC protocol for MICS band considering the proposed rules and probable traffics in the network. A categorization of all the possible traffics is done. The MAC protocol is verified based on our proposed traffic categorization and it is observed that eight patients, each having eight implants, can be monitored simultaneously.
{"title":"A MAC protocol for implanted devices communication in the MICS band","authors":"Mohd. Noor Islam, J. Khan, M. Yuce","doi":"10.1109/BSN.2013.6575463","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575463","url":null,"abstract":"Wireless Body Area Network (WBAN) is well known for accessing data from in body and on body devices. Extensive research has been done on Medium Access Control (MAC) protocol for WBAN for different unlicensed ISM bands, such as 868MHz, 915MHz, 2.4MHz and 433 MHz. However, due to vast use of those frequencies for other applications, there is an unavoidable risk for healthcare application. Inductive link method is another conventional way to communicate with an implanted device. But some limitations are also there, such as low data rate and communication range is very low (few centimeters). To emphasize a patient's health safety and to evade the limitation of inductive link method, a new and different frequency band, Medical Implant Communication System (MICS) band (402–405 MHz), has been accepted worldwide for a small range (3 meters) communication between an external device and implanted devices only. But, different international communication authorities have put some rules and restrictions to use the MICS band. Therefore, it is necessary to design a MAC protocol that will comply with the rules and suitable for the traffics of implanted devices communication network. In this paper, we propose a MAC protocol for MICS band considering the proposed rules and probable traffics in the network. A categorization of all the possible traffics is done. The MAC protocol is verified based on our proposed traffic categorization and it is observed that eight patients, each having eight implants, can be monitored simultaneously.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"46 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":"124389032","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.2013.6575504
Xiaoxu Wu, Yan Wang, Chieh Chien, G. Pottie
Human motion monitoring with body worn sensors is becoming increasingly important in health and wellness. However, achieving a robust recognition of physical activities or gestures despite variability in sensor placement is important for the real-world deployment of body sensor networks. A novel self-calibration process of sensor misplacement based on repetitive motion signatures is proposed. A rotation matrix model is introduced to represent the impact of sensor misorientation. Dynamic time warping (DTW) is employed for choosing and synchronizing training and testing datasets. The information from repetitive motion signatures is then used to calibrate sensor misplacement. In this work, walking was used as an example of a motion signature that provides information for sensor misplacement calibration. To investigate the validity of this method, a large dataset of 57 walking traces over seven different subjects was collected. With the proposed algorithm, we show that in the lower body motion tracking experiment, step-length-measurement accuracy can be improved from 45.84% to 94.51%.
{"title":"Self-calibration of sensor misplacement based on motion signatures","authors":"Xiaoxu Wu, Yan Wang, Chieh Chien, G. Pottie","doi":"10.1109/BSN.2013.6575504","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575504","url":null,"abstract":"Human motion monitoring with body worn sensors is becoming increasingly important in health and wellness. However, achieving a robust recognition of physical activities or gestures despite variability in sensor placement is important for the real-world deployment of body sensor networks. A novel self-calibration process of sensor misplacement based on repetitive motion signatures is proposed. A rotation matrix model is introduced to represent the impact of sensor misorientation. Dynamic time warping (DTW) is employed for choosing and synchronizing training and testing datasets. The information from repetitive motion signatures is then used to calibrate sensor misplacement. In this work, walking was used as an example of a motion signature that provides information for sensor misplacement calibration. To investigate the validity of this method, a large dataset of 57 walking traces over seven different subjects was collected. With the proposed algorithm, we show that in the lower body motion tracking experiment, step-length-measurement accuracy can be improved from 45.84% to 94.51%.","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":"129259983","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.2013.6575520
B. Mortazavi, Nabil Alsharufa, S. Lee, M. Lan, M. Sarrafzadeh, Michael Chronley, C. Roberts
The use of accelerometers to approximate energy expenditure and serve as inputs for exergaming, have both increased in prevalence in response to the worldwide obesity epidemic. Exergames have a need to show energy expenditure values to validate their results, often using accelerometer approximations applied to general daily-living activities. This work presents a method for estimating the metabolic equivalent of task (MET) values achieved when users perform exergaming-specific movements. This shows the caloric expenditure achieved by active video games, based upon raw gravity values of accelerations. Results show that, while a fusion of sensors monitoring the entire body achieves the best results, sensors placed closest to the primary location of movement achieve the most accurate approximations to the METs achieved per activity as well as the overall MET achieved for the soccer exergame under consideration. The METs achieved approach 7, the value considered to be actual casual soccer game play.
{"title":"MET calculations from on-body accelerometers for exergaming movements","authors":"B. Mortazavi, Nabil Alsharufa, S. Lee, M. Lan, M. Sarrafzadeh, Michael Chronley, C. Roberts","doi":"10.1109/BSN.2013.6575520","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575520","url":null,"abstract":"The use of accelerometers to approximate energy expenditure and serve as inputs for exergaming, have both increased in prevalence in response to the worldwide obesity epidemic. Exergames have a need to show energy expenditure values to validate their results, often using accelerometer approximations applied to general daily-living activities. This work presents a method for estimating the metabolic equivalent of task (MET) values achieved when users perform exergaming-specific movements. This shows the caloric expenditure achieved by active video games, based upon raw gravity values of accelerations. Results show that, while a fusion of sensors monitoring the entire body achieves the best results, sensors placed closest to the primary location of movement achieve the most accurate approximations to the METs achieved per activity as well as the overall MET achieved for the soccer exergame under consideration. The METs achieved approach 7, the value considered to be actual casual soccer game play.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"9 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":"131689647","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.2013.6575522
Rachelle Horwitz, T. Quatieri, Brian S. Helfer, Bea Yu, J. Williamson, J. Mundt
In Major Depressive Disorder (MDD), neurophysiologic changes can alter motor control [1][2] and therefore alter speech production by influencing vocal fold motion (source), the vocal tract (system), and melody (prosody). In this paper, we use a database of voice recordings from 28 depressed subjects treated over a 6-week period [3] to compare correlations between features from each of the three speech-production components and clinical assessments of MDD. Toward biomarkers for audio-based continuous monitoring of depression severity, we explore the contextual dependence of these correlations with free-response and read speech, and show tradeoffs across categories of features in these two example contexts. Likewise, we also investigate the context-and speech component-dependence of correlations between our vocal features and assessment of individual symptoms of MDD (e.g., depressed mood, agitation, energy). Finally, motivated by our initial findings, we describe how context may be useful in “on-body” monitoring of MDD to facilitate identification of depression and evaluation of its treatment.
{"title":"On the relative importance of vocal source, system, and prosody in human depression","authors":"Rachelle Horwitz, T. Quatieri, Brian S. Helfer, Bea Yu, J. Williamson, J. Mundt","doi":"10.1109/BSN.2013.6575522","DOIUrl":"https://doi.org/10.1109/BSN.2013.6575522","url":null,"abstract":"In Major Depressive Disorder (MDD), neurophysiologic changes can alter motor control [1][2] and therefore alter speech production by influencing vocal fold motion (source), the vocal tract (system), and melody (prosody). In this paper, we use a database of voice recordings from 28 depressed subjects treated over a 6-week period [3] to compare correlations between features from each of the three speech-production components and clinical assessments of MDD. Toward biomarkers for audio-based continuous monitoring of depression severity, we explore the contextual dependence of these correlations with free-response and read speech, and show tradeoffs across categories of features in these two example contexts. Likewise, we also investigate the context-and speech component-dependence of correlations between our vocal features and assessment of individual symptoms of MDD (e.g., depressed mood, agitation, energy). Finally, motivated by our initial findings, we describe how context may be useful in “on-body” monitoring of MDD to facilitate identification of depression and evaluation of its treatment.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"28 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":"124554526","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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