Pub Date : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827619
Masaaki Yamamoto, T. Ohtsuki
In regard to achieving accurate WLAN positioning, a model of the power absorption by a person was experimentally devised. Parameters of the model are estimated using maximum likelihood estimation (MLE), and the angle of power absorption is measured by a magnetic sensor built into a smartphone. On the basis of the measured angle, the proposed method compensates the power absorption and the influence of multipath fading. According to evaluations by experiment and simulation, positioning error of the proposed method is 28% to 32% lower than that of the conventional method, and the proposed method achieves RMSE of 2.26 m for sparse deployment of access points.
{"title":"Power compensation in WLAN positioning using a smartphone","authors":"Masaaki Yamamoto, T. Ohtsuki","doi":"10.1109/ISSNIP.2014.6827619","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827619","url":null,"abstract":"In regard to achieving accurate WLAN positioning, a model of the power absorption by a person was experimentally devised. Parameters of the model are estimated using maximum likelihood estimation (MLE), and the angle of power absorption is measured by a magnetic sensor built into a smartphone. On the basis of the measured angle, the proposed method compensates the power absorption and the influence of multipath fading. According to evaluations by experiment and simulation, positioning error of the proposed method is 28% to 32% lower than that of the conventional method, and the proposed method achieves RMSE of 2.26 m for sparse deployment of access points.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114407418","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827638
H. Johansen, Wei Zhang, J. Hurley, D. Johansen
Data from affordable body-sensor devices that monitor personal metrics like heart-rate, weight, and movements are changing how athletes train and perform. Existing sport-analytic tools are, however, mostly monolithic proprietary systems where athletes have little control over how their data is used and managed over time. This paper describes Girji, security centered body-sensor data acquisition and management system that embeds a novel form of an active and operational type of computational consent, which gives athletes a high level of control of how their data can be used. This security architecture is implemented using a novel combination of object capabilities that embed executable code and individual meta-code execution containers for flexible consent policies.
{"title":"Management of body-sensor data in sports analytic with operative consent","authors":"H. Johansen, Wei Zhang, J. Hurley, D. Johansen","doi":"10.1109/ISSNIP.2014.6827638","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827638","url":null,"abstract":"Data from affordable body-sensor devices that monitor personal metrics like heart-rate, weight, and movements are changing how athletes train and perform. Existing sport-analytic tools are, however, mostly monolithic proprietary systems where athletes have little control over how their data is used and managed over time. This paper describes Girji, security centered body-sensor data acquisition and management system that embeds a novel form of an active and operational type of computational consent, which gives athletes a high level of control of how their data can be used. This security architecture is implemented using a novel combination of object capabilities that embed executable code and individual meta-code execution containers for flexible consent policies.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121140044","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827632
Tie Qiu, Zhiping Lin, Y. Shan, Y. S. Meng
The effects of different adaptors on microwave power sensor calibration using direct comparison transfer technique have been investigated in this paper. The calibrations are implemented with a mathematical model developed recently. To verify its accuracy in calculating the calibration factor, a comparison is carried out between the mathematical model developed and those reported by other researchers. The comparison indicates that the model is suitable for microwave power sensor calibrations. For its implementations, a 0 dB adaptor, 10 dB, 20 dB and 30 dB attenuators are used to evaluate the effects from different adaptors. Preliminary results show that when the attenuation of an adaptor increases, the expanded uncertainty of the calibration factor increases correspondingly, following the Guide to the Expression of Uncertainty in Measurement (GUM). Moreover, the results of GUM are also verified with the Monte Carlo Method.
{"title":"Microwave power sensor calibration using direct comparison technique with different adaptors","authors":"Tie Qiu, Zhiping Lin, Y. Shan, Y. S. Meng","doi":"10.1109/ISSNIP.2014.6827632","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827632","url":null,"abstract":"The effects of different adaptors on microwave power sensor calibration using direct comparison transfer technique have been investigated in this paper. The calibrations are implemented with a mathematical model developed recently. To verify its accuracy in calculating the calibration factor, a comparison is carried out between the mathematical model developed and those reported by other researchers. The comparison indicates that the model is suitable for microwave power sensor calibrations. For its implementations, a 0 dB adaptor, 10 dB, 20 dB and 30 dB attenuators are used to evaluate the effects from different adaptors. Preliminary results show that when the attenuation of an adaptor increases, the expanded uncertainty of the calibration factor increases correspondingly, following the Guide to the Expression of Uncertainty in Measurement (GUM). Moreover, the results of GUM are also verified with the Monte Carlo Method.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129504582","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827680
Lixia Guan, K. Kuladinithi, Thomas Pötsch, C. Görg
Today, there exist several standards based and proprietary based protocols that can be used in Wireless Sensor Network (WSN) applications. Proprietary technologies are difficult to integrate into larger networks and currently available Internet-based services. The use of IP based technologies allows easy integration with existing network infrastructure. Interoperability has always been a major concern within the IETF. The previous work has proved that the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) protocol, proposed by the IETF is interoperable in widely used TinyOS and Contiki platforms. Compared to the previous work, this work highlights the detailed analysis of the behaviour of RPL in these two platforms, in terms of comparison with two objective functions used (MRHOF and OF0), analysis of RPL control messages (DIO, DIS and DAO) and finally the tolerance of RPL performance under varying traffic loads (1 packets per minute to 60 packets per minute). Further, this work gives a detailed insight into implementation specific issues as reasoning for the degradation of the RPL performance, when mixing nodes of TinyOS and Contiki.
{"title":"A deeper understanding of interoperability between TinyRPL and ContikiRPL","authors":"Lixia Guan, K. Kuladinithi, Thomas Pötsch, C. Görg","doi":"10.1109/ISSNIP.2014.6827680","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827680","url":null,"abstract":"Today, there exist several standards based and proprietary based protocols that can be used in Wireless Sensor Network (WSN) applications. Proprietary technologies are difficult to integrate into larger networks and currently available Internet-based services. The use of IP based technologies allows easy integration with existing network infrastructure. Interoperability has always been a major concern within the IETF. The previous work has proved that the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) protocol, proposed by the IETF is interoperable in widely used TinyOS and Contiki platforms. Compared to the previous work, this work highlights the detailed analysis of the behaviour of RPL in these two platforms, in terms of comparison with two objective functions used (MRHOF and OF0), analysis of RPL control messages (DIO, DIS and DAO) and finally the tolerance of RPL performance under varying traffic loads (1 packets per minute to 60 packets per minute). Further, this work gives a detailed insight into implementation specific issues as reasoning for the degradation of the RPL performance, when mixing nodes of TinyOS and Contiki.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129637094","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827608
Nicholas Chong, Shanhung Wong, B. Vo, S. Nordholm, I. Murray
The "cocktail party problem" has always been a challenging problem to solve and many blind source separation algorithms have been proposed as solutions. This problem has mainly been discussed for non-moving sound sources but it still remains for moving sound sources and high acoustic reverberations. The ability to localise and track multiple moving speakers is a pre-requisite to solving this problem. The aim of this paper is to show that a combination of Degenerate Unmixing Estimation Technique and a Cardinality Balanced Multitarget Multi-Bernoulli Filter provides a viable way to track multiple sound sources and subsequently address the problem of sound separation for moving targets.
{"title":"Multiple moving speaker tracking via degenerate unmixing estimation technique and Cardinality Balanced Multi-target Multi-Bernoulli Filter (DUET-CBMeMBer)","authors":"Nicholas Chong, Shanhung Wong, B. Vo, S. Nordholm, I. Murray","doi":"10.1109/ISSNIP.2014.6827608","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827608","url":null,"abstract":"The \"cocktail party problem\" has always been a challenging problem to solve and many blind source separation algorithms have been proposed as solutions. This problem has mainly been discussed for non-moving sound sources but it still remains for moving sound sources and high acoustic reverberations. The ability to localise and track multiple moving speakers is a pre-requisite to solving this problem. The aim of this paper is to show that a combination of Degenerate Unmixing Estimation Technique and a Cardinality Balanced Multitarget Multi-Bernoulli Filter provides a viable way to track multiple sound sources and subsequently address the problem of sound separation for moving targets.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126208211","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827702
Fang-jing Wu, Tie Luo
Participatory sensing has become a promising data collection approach to crowdsourcing data from multi-modal data sources. This paper proposes a generic participatory sensing framework that consists of a set of well-defined modules in support of diverse use cases. This framework incorporates a concept of “human-as-a-sensor” into participatory sensing and allows the public crowd to contribute human observations as well as sensor measurements from their mobile devices. We specifically address two issues: incentive and extensibility, where the former refers to motivating participants to contribute high-quality data while the latter refers to accommodating heterogeneous and uncertain data sources. To address the incentive issue, we design an incentive engine to attract high-quality contributed data independent of data modalities. This engine works together with a novel social network that we introduce into participatory sensing, where participants are linked together and interact with each other based on data quality and quantity they have contributed. To address the extensibility issue, the proposed framework embodies application-agnostic design and provides an interface to external datasets. To demonstrate and verify this framework, we have developed a prototype mobile application called imReporter, which crowdsources hybrid (image-text) reports from participants in an urban city, and incorporates an external dataset from a public data mall. A pilot study was also carried out with 15 participants for 3 consecutive weeks, and the result confirms that our proposed framework fulfills its design goals.
{"title":"A generic participatory sensing framework for multi-modal datasets","authors":"Fang-jing Wu, Tie Luo","doi":"10.1109/ISSNIP.2014.6827702","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827702","url":null,"abstract":"Participatory sensing has become a promising data collection approach to crowdsourcing data from multi-modal data sources. This paper proposes a generic participatory sensing framework that consists of a set of well-defined modules in support of diverse use cases. This framework incorporates a concept of “human-as-a-sensor” into participatory sensing and allows the public crowd to contribute human observations as well as sensor measurements from their mobile devices. We specifically address two issues: incentive and extensibility, where the former refers to motivating participants to contribute high-quality data while the latter refers to accommodating heterogeneous and uncertain data sources. To address the incentive issue, we design an incentive engine to attract high-quality contributed data independent of data modalities. This engine works together with a novel social network that we introduce into participatory sensing, where participants are linked together and interact with each other based on data quality and quantity they have contributed. To address the extensibility issue, the proposed framework embodies application-agnostic design and provides an interface to external datasets. To demonstrate and verify this framework, we have developed a prototype mobile application called imReporter, which crowdsources hybrid (image-text) reports from participants in an urban city, and incorporates an external dataset from a public data mall. A pilot study was also carried out with 15 participants for 3 consecutive weeks, and the result confirms that our proposed framework fulfills its design goals.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126000993","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827609
Pengfei Zhang, Jing Yang Koh, Shaowei Lin, Ido Nevat
We present two novel distributed event detection algorithms based on a statistical approach that tolerate Byzantine attacks where malicious (compromised) sensors send false sensing data to the gateway leading to increased false alarm rate. We study the problem of Byzantine attack function optimization and the decision threshold optimization and consider two practical cases in our algorithms. In the first case, the Channel State Information (CSI) between the event generating source and sensors is unknown while CSI between the sensors and gateway is known. In the second case, the CSI between the source and sensors as well as between sensors and gateway are unknown. We develop an optimal event detection decision rule under Byzantine attacks for the first case and a novel low-complexity event detection algorithm based on Gaussian approximation and Moment Matching for the second case which considers a global decision. We evaluate our algorithms through extensive simulations. Simulation results show the Receiver Operating Characteristics (ROC) curves under different cases and scenarios, and therefore provide useful upper bounds for various centralized and distributed scheme designs. We also show that our algorithms provide superior detection performance when compared to local decision based schemes.
{"title":"Distributed event detection under Byzantine attack in wireless sensor networks","authors":"Pengfei Zhang, Jing Yang Koh, Shaowei Lin, Ido Nevat","doi":"10.1109/ISSNIP.2014.6827609","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827609","url":null,"abstract":"We present two novel distributed event detection algorithms based on a statistical approach that tolerate Byzantine attacks where malicious (compromised) sensors send false sensing data to the gateway leading to increased false alarm rate. We study the problem of Byzantine attack function optimization and the decision threshold optimization and consider two practical cases in our algorithms. In the first case, the Channel State Information (CSI) between the event generating source and sensors is unknown while CSI between the sensors and gateway is known. In the second case, the CSI between the source and sensors as well as between sensors and gateway are unknown. We develop an optimal event detection decision rule under Byzantine attacks for the first case and a novel low-complexity event detection algorithm based on Gaussian approximation and Moment Matching for the second case which considers a global decision. We evaluate our algorithms through extensive simulations. Simulation results show the Receiver Operating Characteristics (ROC) curves under different cases and scenarios, and therefore provide useful upper bounds for various centralized and distributed scheme designs. We also show that our algorithms provide superior detection performance when compared to local decision based schemes.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114974604","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827696
Xiaochao Qu, Hyoung-Joong Kim
Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.
{"title":"Enhanced discriminant linear regression classification for face recognition","authors":"Xiaochao Qu, Hyoung-Joong Kim","doi":"10.1109/ISSNIP.2014.6827696","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827696","url":null,"abstract":"Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122790368","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827658
Thomas Pötsch, Lei Pei, K. Kuladinithi, C. Görg
The increasing interest and utilization of Wireless Sensor Networks has increased the requirements of energy saving for battery powered sensor nodes. Even in modern sensor nodes, communication causes the largest part of energy consumption and therefore ways to reduce the amount of data sending are widely concerned. One solution to reduce data transmission is a model-driven data acquisition technique called Derivative-Based Prediction (DBP). Instead of transmitting every measured sample, a sensor node uses algorithms to compute approximated models to represent the measured data. In this work, we developed an algorithm to monitor temperature samples in different environmental scenarios. We also evaluated the algorithm with regard to its efficiency and classified the recorded temperature patterns to enhance the precision. In our tests, the algorithm successfully suppressed up to 99% of data transmissions while the average error of prediction has been kept below 0.1°C.
{"title":"Model-driven data acquisition for temperature sensor readings in Wireless Sensor Networks","authors":"Thomas Pötsch, Lei Pei, K. Kuladinithi, C. Görg","doi":"10.1109/ISSNIP.2014.6827658","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827658","url":null,"abstract":"The increasing interest and utilization of Wireless Sensor Networks has increased the requirements of energy saving for battery powered sensor nodes. Even in modern sensor nodes, communication causes the largest part of energy consumption and therefore ways to reduce the amount of data sending are widely concerned. One solution to reduce data transmission is a model-driven data acquisition technique called Derivative-Based Prediction (DBP). Instead of transmitting every measured sample, a sensor node uses algorithms to compute approximated models to represent the measured data. In this work, we developed an algorithm to monitor temperature samples in different environmental scenarios. We also evaluated the algorithm with regard to its efficiency and classified the recorded temperature patterns to enhance the precision. In our tests, the algorithm successfully suppressed up to 99% of data transmissions while the average error of prediction has been kept below 0.1°C.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128342773","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 : 2014-04-21DOI: 10.1109/ISSNIP.2014.6827631
Biswas Jit, Zhu Yongwei, Z. Haihong, J. Maniyeri, C. Zhihao, G. Cuntai
We have made use of the microbending fiber optic sensor to capture ballistocardiographic signals or data for vital signs monitoring in ambient settings, with applications ranging from serious games to ambient assistive living for ageing at home for the elderly. To remove noise and extract the vital signs, the first step of the signal data processing is filtering the signal. In this paper we consider the properties of the digital filter for filtering ballistocardiographic signals. The vital signs waveforms are derived from raw data captured by optical transducers that are placed in ambient locations that are in contact with, but not worn by the subject. Data has been collected from various locations and positions and a detailed trial has been conducted for one of these positions. We iteratively improve the filter design so as to lead to the best parameters. The baseline filter performed reasonably well on data collected in a trial study, with a mean error rate less than 10% for half of the subjects and below 20% for three quarters of the subjects. We also present results of an improved filter that improves the performance both in terms of responsiveness and sensitivity. The improved filter demonstrates consistently less than 12% mean error rate. Principles gleaned from this study may also be applied in designing filters for other types of sensors and for other applications in healthcare.
{"title":"Information processing of optical sensor data in ambient applications","authors":"Biswas Jit, Zhu Yongwei, Z. Haihong, J. Maniyeri, C. Zhihao, G. Cuntai","doi":"10.1109/ISSNIP.2014.6827631","DOIUrl":"https://doi.org/10.1109/ISSNIP.2014.6827631","url":null,"abstract":"We have made use of the microbending fiber optic sensor to capture ballistocardiographic signals or data for vital signs monitoring in ambient settings, with applications ranging from serious games to ambient assistive living for ageing at home for the elderly. To remove noise and extract the vital signs, the first step of the signal data processing is filtering the signal. In this paper we consider the properties of the digital filter for filtering ballistocardiographic signals. The vital signs waveforms are derived from raw data captured by optical transducers that are placed in ambient locations that are in contact with, but not worn by the subject. Data has been collected from various locations and positions and a detailed trial has been conducted for one of these positions. We iteratively improve the filter design so as to lead to the best parameters. The baseline filter performed reasonably well on data collected in a trial study, with a mean error rate less than 10% for half of the subjects and below 20% for three quarters of the subjects. We also present results of an improved filter that improves the performance both in terms of responsiveness and sensitivity. The improved filter demonstrates consistently less than 12% mean error rate. Principles gleaned from this study may also be applied in designing filters for other types of sensors and for other applications in healthcare.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124400605","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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