Charence Wong, S. McKeague, J. Correa, Jindong Liu, Guang-Zhong Yang
Changes in gait can be caused by a wide range of health complications. As deviations in gait may be an indicator of deteriorating health, abnormalities can be used as a surrogate measure for detecting the onset of certain symptoms. Previous studies have demonstrated the value of wearable sensing for gait analysis. This paper demonstrates the added value of using a depth vision sensor combined with wearable sensors for gait analysis. It also presents a method for extracting a robust set of depth features. The preliminary results from a simulated homecare environment using a three-layer artificial neural network classifier demonstrate the advantages of using a depth sensor for gait analysis.
{"title":"Enhanced Classification of Abnormal Gait Using BSN and Depth","authors":"Charence Wong, S. McKeague, J. Correa, Jindong Liu, Guang-Zhong Yang","doi":"10.1109/BSN.2012.26","DOIUrl":"https://doi.org/10.1109/BSN.2012.26","url":null,"abstract":"Changes in gait can be caused by a wide range of health complications. As deviations in gait may be an indicator of deteriorating health, abnormalities can be used as a surrogate measure for detecting the onset of certain symptoms. Previous studies have demonstrated the value of wearable sensing for gait analysis. This paper demonstrates the added value of using a depth vision sensor combined with wearable sensors for gait analysis. It also presents a method for extracting a robust set of depth features. The preliminary results from a simulated homecare environment using a three-layer artificial neural network classifier demonstrate the advantages of using a depth sensor for gait analysis.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117342639","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}
J. Buckley, B. O’flynn, L. Loizou, P. Haigh, D. Boyle, P. Angove, J. Barton, S. O'Mathuna, E. Popovici, Sean O'Connell
Body Sensor Network (BSN) technology is seeing a rapid emergence in application areas such as health, fitness and sports monitoring. Current BSN wireless sensors typically operate on a single frequency band (e.g. utilizing the IEEE 802.15.4 standard that operates at 2.45GHz) employing a single radio transceiver for wireless communications. This allows a simple wireless architecture to be realized with low cost and power consumption. However, network congestion/failure can create potential issues in terms of reliability of data transfer, quality-of-service (QOS) and data throughput for the sensor. These issues can be especially critical in healthcare monitoring applications where data availability and integrity is crucial. The addition of more than one radio has the potential to address some of the above issues. For example, multi-radio implementations can allow access to more than one network, providing increased coverage and data processing as well as improved interoperability between networks. A small number of multi-radio wireless sensor solutions exist at present but require the use of more than one radio transceiver devices to achieve multi-band operation. This paper presents the design of a novel prototype multi-radio hardware platform that uses a single radio transceiver. The proposed design allows multi-band operation in the 433/868MHz ISM bands and this, together with its low complexity and small form factor, make it suitable for a wide range of BSN applications.
{"title":"A Novel and Miniaturized 433/868MHz Multi-band Wireless Sensor Platform for Body Sensor Network Applications","authors":"J. Buckley, B. O’flynn, L. Loizou, P. Haigh, D. Boyle, P. Angove, J. Barton, S. O'Mathuna, E. Popovici, Sean O'Connell","doi":"10.1109/BSN.2012.6","DOIUrl":"https://doi.org/10.1109/BSN.2012.6","url":null,"abstract":"Body Sensor Network (BSN) technology is seeing a rapid emergence in application areas such as health, fitness and sports monitoring. Current BSN wireless sensors typically operate on a single frequency band (e.g. utilizing the IEEE 802.15.4 standard that operates at 2.45GHz) employing a single radio transceiver for wireless communications. This allows a simple wireless architecture to be realized with low cost and power consumption. However, network congestion/failure can create potential issues in terms of reliability of data transfer, quality-of-service (QOS) and data throughput for the sensor. These issues can be especially critical in healthcare monitoring applications where data availability and integrity is crucial. The addition of more than one radio has the potential to address some of the above issues. For example, multi-radio implementations can allow access to more than one network, providing increased coverage and data processing as well as improved interoperability between networks. A small number of multi-radio wireless sensor solutions exist at present but require the use of more than one radio transceiver devices to achieve multi-band operation. This paper presents the design of a novel prototype multi-radio hardware platform that uses a single radio transceiver. The proposed design allows multi-band operation in the 433/868MHz ISM bands and this, together with its low complexity and small form factor, make it suitable for a wide range of BSN applications.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115025265","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}
Christina Strohrmann, M. Rossi, B. Arnrich, G. Tröster
Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.
{"title":"A Data-Driven Approach to Kinematic Analysis in Running Using Wearable Technology","authors":"Christina Strohrmann, M. Rossi, B. Arnrich, G. Tröster","doi":"10.1109/BSN.2012.1","DOIUrl":"https://doi.org/10.1109/BSN.2012.1","url":null,"abstract":"Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116169974","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}
B. Mortazavi, K. C. Chu, Xialong Li, Jessica Tai, Shwetha Kotekar, M. Sarrafzadeh
Human activity monitoring, through the use of body-wearable sensors, allows for many exciting possibilities, from gaming, to exercise, to preventative health care, where childhood obesity is a growing epidemic. The rapidly increasing nature of this trend requires serious thought at targeting its causes and finding solutions. One major influence is video gaming and the hours of sedentary behavior associated with it. In this paper, we present our system for enforcing physical activity of humans playing video games with our body-worn sensor system as the controller. Body movements are communicated with the host computer that calculates physical activity via the metabolic equivalent of task, and runs signal processing algorithms to classify and enforce movements. A user study was conducted to validate the effectiveness and realism of the system while playing an actual video game and data was collected from these same users in order to verify the accuracy of our system. The results show a system that not only allows physical activity, but also enforces it, leading to healthier gaming and accurate motion analysis.
{"title":"Near-Realistic Motion Video Games with Enforced Activity","authors":"B. Mortazavi, K. C. Chu, Xialong Li, Jessica Tai, Shwetha Kotekar, M. Sarrafzadeh","doi":"10.1109/BSN.2012.18","DOIUrl":"https://doi.org/10.1109/BSN.2012.18","url":null,"abstract":"Human activity monitoring, through the use of body-wearable sensors, allows for many exciting possibilities, from gaming, to exercise, to preventative health care, where childhood obesity is a growing epidemic. The rapidly increasing nature of this trend requires serious thought at targeting its causes and finding solutions. One major influence is video gaming and the hours of sedentary behavior associated with it. In this paper, we present our system for enforcing physical activity of humans playing video games with our body-worn sensor system as the controller. Body movements are communicated with the host computer that calculates physical activity via the metabolic equivalent of task, and runs signal processing algorithms to classify and enforce movements. A user study was conducted to validate the effectiveness and realism of the system while playing an actual video game and data was collected from these same users in order to verify the accuracy of our system. The results show a system that not only allows physical activity, but also enforces it, leading to healthier gaming and accurate motion analysis.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122484481","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}
Obesity and overweight are big healthcare challenges in the world's population. Automatic food intake recognition algorithms based on analysis of food intake sounds offer the potential of being a useful tool for simplifying data logging of consumed food. High inter-individual differences of the users' food intake sounds decrease the classification accuracy achieved with a user-unspecific algorithm. To overcome this problem, the Maximum a Posteriori (MAP) estimation is implemented and tested on one user consuming eight types of food. The dependency of the classification enhancement from the size of the adaptation set is investigated. Overall recognition accuracy can be increased from 48 % to around 79 % using records of 10 intake cycles for every food type of one subject. An increase by 7.5 % can be shown for a second subject. This shows the usability of the MAP adaptation algorithm at food intake sound classification tasks. The algorithm provides a suitable way for adapting models to a user, thereby, enhancing the performance of food intake classification.
{"title":"Adaptation of Models for Food Intake Sound Recognition Using Maximum a Posteriori Estimation Algorithm","authors":"S. Päßler, Wolf-Joachim Fischer, I. Kraljevski","doi":"10.1109/BSN.2012.2","DOIUrl":"https://doi.org/10.1109/BSN.2012.2","url":null,"abstract":"Obesity and overweight are big healthcare challenges in the world's population. Automatic food intake recognition algorithms based on analysis of food intake sounds offer the potential of being a useful tool for simplifying data logging of consumed food. High inter-individual differences of the users' food intake sounds decrease the classification accuracy achieved with a user-unspecific algorithm. To overcome this problem, the Maximum a Posteriori (MAP) estimation is implemented and tested on one user consuming eight types of food. The dependency of the classification enhancement from the size of the adaptation set is investigated. Overall recognition accuracy can be increased from 48 % to around 79 % using records of 10 intake cycles for every food type of one subject. An increase by 7.5 % can be shown for a second subject. This shows the usability of the MAP adaptation algorithm at food intake sound classification tasks. The algorithm provides a suitable way for adapting models to a user, thereby, enhancing the performance of food intake classification.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124075693","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}
Wen Dong, Daniel Olguín Olguín, Benjamin N. Waber, T. Kim, A. Pentland
This paper demonstrates a novel approach that combines generative models of organizational dynamics and sensor network data with a stochastic method. Generative models specify how organizational performance is related to who interacts with whom and who performs what. Sensor network data track who interacts with whom and who performs what within an organization, and the stochastic methodology fits multi-agent models to data through the Monte Carlo method. The data set used in this paper documents how employees in a data service center handle tasks with different difficulty levels - tracked with sociometric badges for one month - and documents links between performance and behavior. This paper demonstrates the potential for improving organizational dynamics with body sensor network data, and therefore also shows the need to systematically benchmark differential organizational dynamics models on data sets for different types of organizations.
{"title":"Mapping Organizational Dynamics with Body Sensor Networks","authors":"Wen Dong, Daniel Olguín Olguín, Benjamin N. Waber, T. Kim, A. Pentland","doi":"10.1109/BSN.2012.16","DOIUrl":"https://doi.org/10.1109/BSN.2012.16","url":null,"abstract":"This paper demonstrates a novel approach that combines generative models of organizational dynamics and sensor network data with a stochastic method. Generative models specify how organizational performance is related to who interacts with whom and who performs what. Sensor network data track who interacts with whom and who performs what within an organization, and the stochastic methodology fits multi-agent models to data through the Monte Carlo method. The data set used in this paper documents how employees in a data service center handle tasks with different difficulty levels - tracked with sociometric badges for one month - and documents links between performance and behavior. This paper demonstrates the potential for improving organizational dynamics with body sensor network data, and therefore also shows the need to systematically benchmark differential organizational dynamics models on data sets for different types of organizations.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127844664","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}
A. Spehar-Deleze, Salzitsa Anastasova-Ivanova, J. Popplewell, P. Vadgama
Lactate is one of the most important biomarkers of tissue oxygenation and thus of paramount importance for sports and health care applications. Lactate levels provide information on anaerobic threshold which is very important for tailoring training programs in endurance sports. In this contribution we present an implantable amperometric lactate sensor for continuous in vivo monitoring. A needle based construction is used where a sensing platinum wire is inserted into a stainless steel tube that serves as a combined counter and reference electrode allowing for easy insertion, small size and minimally invasive procedure. The sensing enzyme layer is sandwiched between two polymer membranes which allow high selectivity, a wide lactate linear range and biocompatibility. The sensors have been fully evaluated in vitro and tested in vivo in rats. The measured values of tissue lactate obtained with our sensors were compared with lactate levels measured in blood by the commercial Lactate Pro analyzer. The obtained concentrations were in the same range, however, no clear correlation between blood and tissue values was found. Coldsterilisation by gamma radiation, required for human studies, is currently being investigated. This work will provide valuable information on lactate levels in different physiological compartments and increase our understanding of physiological processes related to endurance sports.
{"title":"Extreme Physiological State: Development of Tissue Lactate Sensor","authors":"A. Spehar-Deleze, Salzitsa Anastasova-Ivanova, J. Popplewell, P. Vadgama","doi":"10.1109/BSN.2012.32","DOIUrl":"https://doi.org/10.1109/BSN.2012.32","url":null,"abstract":"Lactate is one of the most important biomarkers of tissue oxygenation and thus of paramount importance for sports and health care applications. Lactate levels provide information on anaerobic threshold which is very important for tailoring training programs in endurance sports. In this contribution we present an implantable amperometric lactate sensor for continuous in vivo monitoring. A needle based construction is used where a sensing platinum wire is inserted into a stainless steel tube that serves as a combined counter and reference electrode allowing for easy insertion, small size and minimally invasive procedure. The sensing enzyme layer is sandwiched between two polymer membranes which allow high selectivity, a wide lactate linear range and biocompatibility. The sensors have been fully evaluated in vitro and tested in vivo in rats. The measured values of tissue lactate obtained with our sensors were compared with lactate levels measured in blood by the commercial Lactate Pro analyzer. The obtained concentrations were in the same range, however, no clear correlation between blood and tissue values was found. Coldsterilisation by gamma radiation, required for human studies, is currently being investigated. This work will provide valuable information on lactate levels in different physiological compartments and increase our understanding of physiological processes related to endurance sports.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114594072","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}
Jindong Liu, Edward Johns, L. Atallah, C. Pettitt, Benny P. L. Lo, G. Frost, Guang-Zhong Yang
The prevalence of obesity worldwide presents a great challenge to existing healthcare systems. There is a general need for pervasive monitoring of the dietary behaviour of those who are at risk of co-morbidities. Currently, however, there is no accurate method of assessing the nutritional intake of people in their home environment. Traditional methods require subjects to manually respond to questionnaires for analysis, which is subjective, prone to errors, and difficult to ensure consistency and compliance. In this paper, we present a wearable sensor platform that autonomously provides detailed information regarding a subject's dietary habits. The sensor consists of a microphone and a camera and is worn discretely on the ear. Sound features are extracted in real-time and if a chewing activity is classified, the camera captures a video sequence for further analysis. From this sequence, a number of key frames are extracted to represent important episodes during the course of a meal. Results show a high classification rate of chewing activities, and the visual log demonstrates a detailed overview of the subject's food intake that is difficult to quantify from manually-acquired food records.
{"title":"An Intelligent Food-Intake Monitoring System Using Wearable Sensors","authors":"Jindong Liu, Edward Johns, L. Atallah, C. Pettitt, Benny P. L. Lo, G. Frost, Guang-Zhong Yang","doi":"10.1109/BSN.2012.11","DOIUrl":"https://doi.org/10.1109/BSN.2012.11","url":null,"abstract":"The prevalence of obesity worldwide presents a great challenge to existing healthcare systems. There is a general need for pervasive monitoring of the dietary behaviour of those who are at risk of co-morbidities. Currently, however, there is no accurate method of assessing the nutritional intake of people in their home environment. Traditional methods require subjects to manually respond to questionnaires for analysis, which is subjective, prone to errors, and difficult to ensure consistency and compliance. In this paper, we present a wearable sensor platform that autonomously provides detailed information regarding a subject's dietary habits. The sensor consists of a microphone and a camera and is worn discretely on the ear. Sound features are extracted in real-time and if a chewing activity is classified, the camera captures a video sequence for further analysis. From this sequence, a number of key frames are extracted to represent important episodes during the course of a meal. Results show a high classification rate of chewing activities, and the visual log demonstrates a detailed overview of the subject's food intake that is difficult to quantify from manually-acquired food records.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130489854","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}
Charence Wong, Zhiqiang Zhang, R. Kwasnicki, Jindong Liu, Guang-Zhong Yang
Detailed motion reconstruction is a prerequisite of biomotion analysis and physical function assessment for a variety of scenarios. For example, biomechanical analysis can be used to assess physical activity to diagnose pathological conditions, to provide an objective measure of biomechanics for peri-operative care, and to monitor patients with mobility issues. Unfortunately, current motion capture systems cannot perform biomechanical analysis continuously in the patient's natural environment. In this paper, a pose estimation scheme from a sparse network of accelerometer-based wearable sensors, which does not impose restrictions upon the patient's daily life, is presented. In the proposed method, a marker-based motion capture system is used for acquiring the 3D motion data, and partial least squares regression (PLSR) is used to establish the implicit model between 3D body pose and the wearable sensor measurements. A linear constant velocity process model and measurement model are designed and a Kalman filter is then deployed to estimate the posture. Experimental results demonstrate the strength of the technique and how it can be used to estimate detailed 3D motion from a sparse set of sensors.
{"title":"Motion Reconstruction from Sparse Accelerometer Data Using PLSR","authors":"Charence Wong, Zhiqiang Zhang, R. Kwasnicki, Jindong Liu, Guang-Zhong Yang","doi":"10.1109/BSN.2012.28","DOIUrl":"https://doi.org/10.1109/BSN.2012.28","url":null,"abstract":"Detailed motion reconstruction is a prerequisite of biomotion analysis and physical function assessment for a variety of scenarios. For example, biomechanical analysis can be used to assess physical activity to diagnose pathological conditions, to provide an objective measure of biomechanics for peri-operative care, and to monitor patients with mobility issues. Unfortunately, current motion capture systems cannot perform biomechanical analysis continuously in the patient's natural environment. In this paper, a pose estimation scheme from a sparse network of accelerometer-based wearable sensors, which does not impose restrictions upon the patient's daily life, is presented. In the proposed method, a marker-based motion capture system is used for acquiring the 3D motion data, and partial least squares regression (PLSR) is used to establish the implicit model between 3D body pose and the wearable sensor measurements. A linear constant velocity process model and measurement model are designed and a Kalman filter is then deployed to estimate the posture. Experimental results demonstrate the strength of the technique and how it can be used to estimate detailed 3D motion from a sparse set of sensors.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132211340","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}
Brain Computer Interface (BCI) is gaining popularity due to recent advances in developing small and compact electronic technology and electrodes. Miniaturization and form factor reduction in particular are the key objectives for Body Sensor Networks (BSNs) and wearable systems that implement BCIs. More complex signal processing techniques have been developed in the past few years for BCI which create further challenges for form factor reduction. In this paper, we perform a computational profiling on signal processing tasks for a typical BCI system. We employ several common feature extraction techniques. We define a cost function based on the computational complexity for each feature dimension and present a sequential feature selection to explore the complexity versus the accuracy. We discuss the trade-offs between the computational cost and the accuracy of the system. This will be useful for emerging mobile, wearable and power-aware BCI systems where the computational complexity, the form factor, the size of the battery and the power consumption are of significant importance. We investigate adaptive algorithms that will adjust the computational complexity of the signal processing based on the amount of energy available, while guaranteeing that the accuracy is minimally compromised. We perform an analysis on a standard inhibition (Go/NoGo) task. We demonstrate while classification accuracy is reduced by 2%, compared to the best classification accuracy obtained, the computational complexity of the system can be reduced by more than 60%. Furthermore, we investigate the performance of our technique on real-time EEG signals provided by an eMotiv® device for a Push/No Push task.
{"title":"Brain-Computer Interface Signal Processing Algorithms: A Computational Cost vs. Accuracy Analysis for Wearable Computers","authors":"A. Ahmadi, O. Dehzangi, R. Jafari","doi":"10.1109/BSN.2012.19","DOIUrl":"https://doi.org/10.1109/BSN.2012.19","url":null,"abstract":"Brain Computer Interface (BCI) is gaining popularity due to recent advances in developing small and compact electronic technology and electrodes. Miniaturization and form factor reduction in particular are the key objectives for Body Sensor Networks (BSNs) and wearable systems that implement BCIs. More complex signal processing techniques have been developed in the past few years for BCI which create further challenges for form factor reduction. In this paper, we perform a computational profiling on signal processing tasks for a typical BCI system. We employ several common feature extraction techniques. We define a cost function based on the computational complexity for each feature dimension and present a sequential feature selection to explore the complexity versus the accuracy. We discuss the trade-offs between the computational cost and the accuracy of the system. This will be useful for emerging mobile, wearable and power-aware BCI systems where the computational complexity, the form factor, the size of the battery and the power consumption are of significant importance. We investigate adaptive algorithms that will adjust the computational complexity of the signal processing based on the amount of energy available, while guaranteeing that the accuracy is minimally compromised. We perform an analysis on a standard inhibition (Go/NoGo) task. We demonstrate while classification accuracy is reduced by 2%, compared to the best classification accuracy obtained, the computational complexity of the system can be reduced by more than 60%. Furthermore, we investigate the performance of our technique on real-time EEG signals provided by an eMotiv® device for a Push/No Push task.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115644077","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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