Pub Date : 2015-04-13DOI: 10.1109/SAS.2015.7133642
P. Pierleoni, Luca Pernini, Alberto Belli, Lorenzo Palma, Simone Valenti, M. Paniccia
Nowadays society is moving to a scenery where autonomous elderly live alone in their houses. An automatic remote monitoring system using wearable and ambient sensors is becoming even more important, and is a challenge for the future in WSNs, AAL, and Home Automation areas. Relating to this, one of the most critical events for the safety and the health of the elderly is the fall. Lot of methods, applications, and stand-alone devices have been presented so far. This work proposes a novel method based on the Support Vector Machine technique and addressed to Android low-cost smartphones. Our method starts from data acquired from accelerometer and magnetometer, now available in all the low-end devices, and uses a set of features extracted from a processing of the two signals. After an initial training, the classification of fall events and non-fall events is performed by the Support Vector Machine algorithm. Since we have decided to use the smartphone as monitoring device, the use of other invasive wearable sensors is avoided, and the user have simply to hold the phone on his pocket. Moreover, we can use the cellular network for the eventual sending of notifications and alerts to relatives in case of falls. Actually, our tests show a good performance with a sensitivity of 99.3% and a specificity of 96%.
{"title":"SVM-based fall detection method for elderly people using Android low-cost smartphones","authors":"P. Pierleoni, Luca Pernini, Alberto Belli, Lorenzo Palma, Simone Valenti, M. Paniccia","doi":"10.1109/SAS.2015.7133642","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133642","url":null,"abstract":"Nowadays society is moving to a scenery where autonomous elderly live alone in their houses. An automatic remote monitoring system using wearable and ambient sensors is becoming even more important, and is a challenge for the future in WSNs, AAL, and Home Automation areas. Relating to this, one of the most critical events for the safety and the health of the elderly is the fall. Lot of methods, applications, and stand-alone devices have been presented so far. This work proposes a novel method based on the Support Vector Machine technique and addressed to Android low-cost smartphones. Our method starts from data acquired from accelerometer and magnetometer, now available in all the low-end devices, and uses a set of features extracted from a processing of the two signals. After an initial training, the classification of fall events and non-fall events is performed by the Support Vector Machine algorithm. Since we have decided to use the smartphone as monitoring device, the use of other invasive wearable sensors is avoided, and the user have simply to hold the phone on his pocket. Moreover, we can use the cellular network for the eventual sending of notifications and alerts to relatives in case of falls. Actually, our tests show a good performance with a sensitivity of 99.3% and a specificity of 96%.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133372495","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133591
N. Harris, A. Cranny, M. Rivers, K. Smettem
There is an established need to measure soil salinity, and wireless sensor networks offer the potential to achieve this, coupled with a suitable sensor. However, suitable sensors, up until very recently, have not been available. In this paper we report on the fabrication and calibration of a new low-cost, robust, screen-printed sensor for detecting chloride ions. We also report on two experiments using this sensor. The first is a laboratory-based experiment that shows how sensors can be used to validate modeling results by installing several sensors in a soil column and tracking the vertical migration of a chloride pulse in real time. The second is a trial of multiple sensors installed in a fluvarium (stream simulator) showing that distributed sensors are able to monitor real time changes in horizontal chloride flux in an emulated natural environment. We report on results from both surface flows as well as from sensors at a depth of a few mm in the fluvarium sediment, and differences and trends between the two are discussed. As an example of how such sensors are useful, we note that for the flow regime and sediment type tested, penetration of surface chloride into the river bed is unexpectedly slow and raises questions regarding the dynamics of pollutants in such systems. We conclude that such sensors, coupled with a distributed network, offer a new paradigm in hydrological monitoring and will enable new applications, such as irrigation using mixtures of potable and brackish water with significant cost and resource saving.
{"title":"Applications of a wireless chloride sensor in environmental monitoring","authors":"N. Harris, A. Cranny, M. Rivers, K. Smettem","doi":"10.1109/SAS.2015.7133591","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133591","url":null,"abstract":"There is an established need to measure soil salinity, and wireless sensor networks offer the potential to achieve this, coupled with a suitable sensor. However, suitable sensors, up until very recently, have not been available. In this paper we report on the fabrication and calibration of a new low-cost, robust, screen-printed sensor for detecting chloride ions. We also report on two experiments using this sensor. The first is a laboratory-based experiment that shows how sensors can be used to validate modeling results by installing several sensors in a soil column and tracking the vertical migration of a chloride pulse in real time. The second is a trial of multiple sensors installed in a fluvarium (stream simulator) showing that distributed sensors are able to monitor real time changes in horizontal chloride flux in an emulated natural environment. We report on results from both surface flows as well as from sensors at a depth of a few mm in the fluvarium sediment, and differences and trends between the two are discussed. As an example of how such sensors are useful, we note that for the flow regime and sediment type tested, penetration of surface chloride into the river bed is unexpectedly slow and raises questions regarding the dynamics of pollutants in such systems. We conclude that such sensors, coupled with a distributed network, offer a new paradigm in hydrological monitoring and will enable new applications, such as irrigation using mixtures of potable and brackish water with significant cost and resource saving.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121708095","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133611
Domenico Balsamo, Gianluca Gallo, D. Brunelli, L. Benini
Energy efficiency in smart buildings requires distributed sensing infrastructure to monitor the power consumption of appliances, machines and lighting sources. The analysis of current and voltage waveforms is fundamental for gathering diagnostic information about the power quality and for reducing power wastage. Moreover, it enables Non-intrusive Load Monitoring (NILM), which is the process of disaggregating a household's total electricity consumption into its contributing appliances, by analysing the voltage and current changes. In this paper, an innovative full Energy-neutral (i.e. battery free) and Non-intrusive Wireless Energy Meter (NIWEM) is presented to measure current, voltage and power factor. As key features, the NIWEM is completely non-invasive and it can self-sustain its operations by harvesting energy from the monitored load. It also features a standard (Zigbee) wireless interface for communication with the smart-building system. Experimental results have confirmed that complete energy sustainability can be achieved also with very low-power loads.
{"title":"Non-intrusive Zigbee power meter for load monitoring in smart buildings","authors":"Domenico Balsamo, Gianluca Gallo, D. Brunelli, L. Benini","doi":"10.1109/SAS.2015.7133611","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133611","url":null,"abstract":"Energy efficiency in smart buildings requires distributed sensing infrastructure to monitor the power consumption of appliances, machines and lighting sources. The analysis of current and voltage waveforms is fundamental for gathering diagnostic information about the power quality and for reducing power wastage. Moreover, it enables Non-intrusive Load Monitoring (NILM), which is the process of disaggregating a household's total electricity consumption into its contributing appliances, by analysing the voltage and current changes. In this paper, an innovative full Energy-neutral (i.e. battery free) and Non-intrusive Wireless Energy Meter (NIWEM) is presented to measure current, voltage and power factor. As key features, the NIWEM is completely non-invasive and it can self-sustain its operations by harvesting energy from the monitored load. It also features a standard (Zigbee) wireless interface for communication with the smart-building system. Experimental results have confirmed that complete energy sustainability can be achieved also with very low-power loads.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131548","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133587
F. E. Murphy, M. Magno, P. Whelan, Emanuel Popo Vici
In recent years, various United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator insect for a wide range of crops. It is clear that protecting the population of honey bees worldwide, as well as enabling them to maximise their productivity, is an important concern. The work described in this paper utilised heterogeneous wireless sensor networks technologies to gather data unobtrusively from a beehive, describing the conditions and activity of the honey bee colony. A wide range of sensors were deployed for monitoring the multidimensional conditions within a living beehive (including oxygen, carbon dioxide, pollutant levels, temperature, and humidity). Meteorological and environmental conditions outside the hive were also monitored throughout the deployment. The data were then analysed from a biological perspective to provide insights into honey bee behaviour and health. This led to the development of an algorithm for automatically determining the status of the bee colony. Analysis was also undertaken from a meteorological perspective, which led to the development of an algorithm for predicting short term external weather conditions (rain) based on the conditions observed within the hive. The meteorological conditions were seen to have an impact on the data provided by biological sensors (bees) and physical sensors. This can be exploited to improve the accuracy of local weather prediction. Applications of this algorithm include agricultural and environmental monitoring for accurate short term forecasts for the area local to the beehive.
{"title":"b+WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring — Preliminary results and analysis","authors":"F. E. Murphy, M. Magno, P. Whelan, Emanuel Popo Vici","doi":"10.1109/SAS.2015.7133587","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133587","url":null,"abstract":"In recent years, various United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator insect for a wide range of crops. It is clear that protecting the population of honey bees worldwide, as well as enabling them to maximise their productivity, is an important concern. The work described in this paper utilised heterogeneous wireless sensor networks technologies to gather data unobtrusively from a beehive, describing the conditions and activity of the honey bee colony. A wide range of sensors were deployed for monitoring the multidimensional conditions within a living beehive (including oxygen, carbon dioxide, pollutant levels, temperature, and humidity). Meteorological and environmental conditions outside the hive were also monitored throughout the deployment. The data were then analysed from a biological perspective to provide insights into honey bee behaviour and health. This led to the development of an algorithm for automatically determining the status of the bee colony. Analysis was also undertaken from a meteorological perspective, which led to the development of an algorithm for predicting short term external weather conditions (rain) based on the conditions observed within the hive. The meteorological conditions were seen to have an impact on the data provided by biological sensors (bees) and physical sensors. This can be exploited to improve the accuracy of local weather prediction. Applications of this algorithm include agricultural and environmental monitoring for accurate short term forecasts for the area local to the beehive.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130427862","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133602
Mangesh Gurav, S. Sarik, M. Baghini
Determination and monitoring of moisture content in the soil is a requirement in many agricultural and civil engineering applications. Time domain reflectometry (TDR) is a well-known technique for its accuracy and applicability in the field. A typical TDR system consists of three units: A signal generation unit, a signal acquisition and encoding/decoding unit and a signal processing unit. The signal generation unit sends the electromagnetic waves in the form of pulses on a transmission line (probe), inserted in the soil. Based on the traveling time of the wave along the probe and characteristics of the reflected waves the dielectric constant of the soil is derived. This moisture content of soil is related to the dielectric constant using Topp's equation. Several TDR waveform interpretation methods have been reported. Though, many reported methods process the entire cycle of the TDR signal, the useful information is only available in 10-20% of the signal period, called the region of interest (ROI). Some of the methods extract this ROI manually. Some existing method describe the results by analyzing a single pulse of the TDR signal. This can lead to erroneous results as the single pulse may have been corrupted by either internal or external noise or by the jitter of the sampling clock. This paper presents a TDR waveform interpretation method. In this method, the ROI of 20 cycles is calculated automatically and averaged with the proper averaging technique. To study the effect of non-linearities added by the system on the TDR signal we have modeled the signal acquisition and encoding/decoding unit. We have also presented an error detection technique to detect the corrupted regions of a captured signal. The error detection technique is able to detect error level as low as 0.2% in the signal. The model has been tested with real TDR signals transmitted through air and water and then captured on a sampling oscilloscope, with different jitter levels and different number of bits in DAC. The TDR waveform interpretation method has been tested successfully with 5 different materials.
{"title":"Time extraction method for time domain reflectometry measurements","authors":"Mangesh Gurav, S. Sarik, M. Baghini","doi":"10.1109/SAS.2015.7133602","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133602","url":null,"abstract":"Determination and monitoring of moisture content in the soil is a requirement in many agricultural and civil engineering applications. Time domain reflectometry (TDR) is a well-known technique for its accuracy and applicability in the field. A typical TDR system consists of three units: A signal generation unit, a signal acquisition and encoding/decoding unit and a signal processing unit. The signal generation unit sends the electromagnetic waves in the form of pulses on a transmission line (probe), inserted in the soil. Based on the traveling time of the wave along the probe and characteristics of the reflected waves the dielectric constant of the soil is derived. This moisture content of soil is related to the dielectric constant using Topp's equation. Several TDR waveform interpretation methods have been reported. Though, many reported methods process the entire cycle of the TDR signal, the useful information is only available in 10-20% of the signal period, called the region of interest (ROI). Some of the methods extract this ROI manually. Some existing method describe the results by analyzing a single pulse of the TDR signal. This can lead to erroneous results as the single pulse may have been corrupted by either internal or external noise or by the jitter of the sampling clock. This paper presents a TDR waveform interpretation method. In this method, the ROI of 20 cycles is calculated automatically and averaged with the proper averaging technique. To study the effect of non-linearities added by the system on the TDR signal we have modeled the signal acquisition and encoding/decoding unit. We have also presented an error detection technique to detect the corrupted regions of a captured signal. The error detection technique is able to detect error level as low as 0.2% in the signal. The model has been tested with real TDR signals transmitted through air and water and then captured on a sampling oscilloscope, with different jitter levels and different number of bits in DAC. The TDR waveform interpretation method has been tested successfully with 5 different materials.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124209132","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133635
Matthew Pugh
The goal of sensor fusion is to combine the information obtained by various sensors to make better decisions. By better, it is meant that the sensor fusion algorithm provides, for example, better detectability or lower false alarm rates compared to decisions based upon a single sensor. This work is motivated by combining the data gathered by multiple passive infrared (PIR) sensors to detect intrusions into a room. Optimal decision theoretic approaches typically include statistical models for both the background (non-event) data, and intrusion (event) data. Concurrent work by the author has shown that by appropriately processing multiple PIR data streams, a statistic can be computed which has a known distribution on the background data. If the distribution of the statistic during an event is known, optimal decision procedures could be derived to perform sensor fusion. It is shown, however, that it is difficult to statistically model the event data. This paper thus focuses on using minimax theory to derive the worst-case event distribution for minimizing Bayes risk. Because of this, using the minimax distribution as a surrogate for the unknown true distribution of the event data provides a lower bound on risk performance. The minimax formulation is very general and will be used to consider loss functions, the probability of intrusions events and consider non-binary decisions.
{"title":"A minimax approach to sensor fusion for intrusion detection","authors":"Matthew Pugh","doi":"10.1109/SAS.2015.7133635","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133635","url":null,"abstract":"The goal of sensor fusion is to combine the information obtained by various sensors to make better decisions. By better, it is meant that the sensor fusion algorithm provides, for example, better detectability or lower false alarm rates compared to decisions based upon a single sensor. This work is motivated by combining the data gathered by multiple passive infrared (PIR) sensors to detect intrusions into a room. Optimal decision theoretic approaches typically include statistical models for both the background (non-event) data, and intrusion (event) data. Concurrent work by the author has shown that by appropriately processing multiple PIR data streams, a statistic can be computed which has a known distribution on the background data. If the distribution of the statistic during an event is known, optimal decision procedures could be derived to perform sensor fusion. It is shown, however, that it is difficult to statistically model the event data. This paper thus focuses on using minimax theory to derive the worst-case event distribution for minimizing Bayes risk. Because of this, using the minimax distribution as a surrogate for the unknown true distribution of the event data provides a lower bound on risk performance. The minimax formulation is very general and will be used to consider loss functions, the probability of intrusions events and consider non-binary decisions.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122953319","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133619
G. Horvat, D. Zagar, Jelena Vlaovic
Emerging new applications of Wireless Sensor Networks (WSNs) place new and complex requirements on existing systems and continuously propel new research topics. With the proliferation of WSNs in domains such as process control, automation and related domains, the need to establish reliable communication with low latency is becoming intensely expressed. Taking into account WSNs as low power, low cost and low reliability infrastructures, the process of establishing Quality of Service (QoS) support for WSNs is a non-trivial task. Furthermore, the majority of the scientific work focuses on simulations so there is a need to perform experimental measurements on contemporary hardware solutions used in WSNs for comparison. This paper evaluates WSN application for access control system where sensor nodes are query driven and the network follows star topology configuration. Considering specific network configurations, this paper investigates reliability and delay for round-trip communication - RTT. The emphasis is placed on the analysis of network performance from the sensor node point of view. Analysis is performed through simulation and experimental validation of a WSN deployed within the proposed testbed. The investigation of influence of network parameters on the proposed metrics for critical network conditions using simulation results in higher losses, reduced reliability and high values of RTT that needs to be avoided for real network deployment. Finally, comparison of simulated and experimental results yields a conclusion that the simulation does not accurately represent experimental data, and more accurate simulation model of WSN nodes should be proposed in order to mitigate the observed discrepancies.
{"title":"On the topic of RTT and delivery ratio in query driven Wireless Sensor Networks","authors":"G. Horvat, D. Zagar, Jelena Vlaovic","doi":"10.1109/SAS.2015.7133619","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133619","url":null,"abstract":"Emerging new applications of Wireless Sensor Networks (WSNs) place new and complex requirements on existing systems and continuously propel new research topics. With the proliferation of WSNs in domains such as process control, automation and related domains, the need to establish reliable communication with low latency is becoming intensely expressed. Taking into account WSNs as low power, low cost and low reliability infrastructures, the process of establishing Quality of Service (QoS) support for WSNs is a non-trivial task. Furthermore, the majority of the scientific work focuses on simulations so there is a need to perform experimental measurements on contemporary hardware solutions used in WSNs for comparison. This paper evaluates WSN application for access control system where sensor nodes are query driven and the network follows star topology configuration. Considering specific network configurations, this paper investigates reliability and delay for round-trip communication - RTT. The emphasis is placed on the analysis of network performance from the sensor node point of view. Analysis is performed through simulation and experimental validation of a WSN deployed within the proposed testbed. The investigation of influence of network parameters on the proposed metrics for critical network conditions using simulation results in higher losses, reduced reliability and high values of RTT that needs to be avoided for real network deployment. Finally, comparison of simulated and experimental results yields a conclusion that the simulation does not accurately represent experimental data, and more accurate simulation model of WSN nodes should be proposed in order to mitigate the observed discrepancies.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115840982","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133567
Josip Grlica, Toni Martinovic, H. Džapo
Respiration monitoring of patients with chronic diseases, children, elderly, or sportsmen can be a useful tool in health condition assessment, early diagnosis of various diseases, and real-time prediction of possibly dangerous health conditions. In this paper we present a low-cost solution of respiration monitoring system, based on a custom designed capacitive sensor, which comprises of two moveable electrodes, mounted on a rigid belt attached around the person's chest. One electrode is fixed while the other one moves in a rhythm of breathing, with restriction of movement in one axis only. The electrode geometry was optimized by numeric electromagnetic simulations to provide linearity and measurable level in change of capacitance, even for case of shallow breathing. Input measuring chain is based on capacitance-to-digital (CDC) integrated circuit and it is able to capture the changes of up to several hundreds of femtofarads in full scale, with enough resolution to enable breathing rate detection, and discrimination of cases of deep, shallow, and no breathing by signal processing algorithms. The prototype measurement system was designed and tested in laboratory on several test subjects. Preliminary experiments showed that the proposed measurement system for respiration monitoring can be used for low-cost and low-power integrated solution for continuous monitoring of patient's respiration.
{"title":"Capacitive sensor for respiration monitoring","authors":"Josip Grlica, Toni Martinovic, H. Džapo","doi":"10.1109/SAS.2015.7133567","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133567","url":null,"abstract":"Respiration monitoring of patients with chronic diseases, children, elderly, or sportsmen can be a useful tool in health condition assessment, early diagnosis of various diseases, and real-time prediction of possibly dangerous health conditions. In this paper we present a low-cost solution of respiration monitoring system, based on a custom designed capacitive sensor, which comprises of two moveable electrodes, mounted on a rigid belt attached around the person's chest. One electrode is fixed while the other one moves in a rhythm of breathing, with restriction of movement in one axis only. The electrode geometry was optimized by numeric electromagnetic simulations to provide linearity and measurable level in change of capacitance, even for case of shallow breathing. Input measuring chain is based on capacitance-to-digital (CDC) integrated circuit and it is able to capture the changes of up to several hundreds of femtofarads in full scale, with enough resolution to enable breathing rate detection, and discrimination of cases of deep, shallow, and no breathing by signal processing algorithms. The prototype measurement system was designed and tested in laboratory on several test subjects. Preliminary experiments showed that the proposed measurement system for respiration monitoring can be used for low-cost and low-power integrated solution for continuous monitoring of patient's respiration.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129705211","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133574
Stephan Mühlbacher-Karrer, H. Zangl
Robust object detection and low computational effort are two key issues, which have to be addressed when Electrical Capacitance Tomography (ECT) is applied in the field of mobile applications. In this paper we present an object detection technique in combination with an artefact reduced fast reconstruction algorithm. The approach achieves a high object detection rate in the vicinity and center of the sensor front end. The proposed light-weight signal processing chain is the key to integrate this sensing technology on a platform limited in terms of space, energy and computational resources.
{"title":"Object detection based on electrical capacitance tomography","authors":"Stephan Mühlbacher-Karrer, H. Zangl","doi":"10.1109/SAS.2015.7133574","DOIUrl":"https://doi.org/10.1109/SAS.2015.7133574","url":null,"abstract":"Robust object detection and low computational effort are two key issues, which have to be addressed when Electrical Capacitance Tomography (ECT) is applied in the field of mobile applications. In this paper we present an object detection technique in combination with an artefact reduced fast reconstruction algorithm. The approach achieves a high object detection rate in the vicinity and center of the sensor front end. The proposed light-weight signal processing chain is the key to integrate this sensing technology on a platform limited in terms of space, energy and computational resources.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125286137","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 : 2015-04-13DOI: 10.1109/SAS.2015.7133603
D. Vasić, D. Ambruš, V. Bilas
Soil electrical conductivity and magnetic susceptibility are connected to a number of soil properties such as water content, salinity and clay content. Electromagnetic induction (EMI) sensors for geoelectric characterization and mapping of upper soil layers typically consist of a transmitter and several spatially distributed receiver coils. In this paper, we develop a stochastic approach to the inverse problem of determination of electrical conductivity and magnetic susceptibility of two-layered soil, and thickness of its upper layer. As a forward model, we use an analytical truncated-region EMI model with one transmitter and several receiver coils placed horizontally above the soil. For solving the stochastic inversion problem we employ Markov Chain Monte Carlo (MCMC) approach. We illustrate the application of the inversion procedure on a synthetic single-frequency data set obtained from the model of an EMI sensor. Furthermore, we investigate the measurement uncertainty requirements for the sensor. The model and the stochastic inversion approach are suitable for design of EMI sensors and off-line analysis of the EMI data.
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