Pub Date : 2017-07-01DOI: 10.1109/EESMS.2017.8052697
A. Cardellicchio, R. Dario, V. Di Lecce, C. Guaragnella, A. Lombardi, L. Mongelli, Alessandro Quarto, D. Soldo
Population ageing, i.e. the increase of the median age in the population, has become a pressing matter, due to a significant rise in health-care expenses, which, if not controlled, may destabilize welfare mechanisms. To face this problem, various approaches have already been presented in literature: some of these methods acquire pathology-relevant data using ad-hoc systems (i.e. sensor arrays), while others employs commercial devices, like wearable or smartphones; however, there are several issues that such systems must address, i.e. the meaningfulness of sensed data, or the wellness of the patients in the follow-up phase. The aim of this work is to propose a medical decision support system, specifically oriented to the acquisition of data related to neurological pathologies, whose final aim is to build an extended medical knowledge base, which will be used by skilled professionals in the diagnosis phase of the pathology; to improve the comfort of the patients, a smartphone medical app has been developed and tested, therefore evaluating the relevance of the data acquired by sensors embedded in an Android smartphone, and assessing the overall feasibility of such a decision support system.
{"title":"Evaluation of smartphone usage in neurological pathologies diagnosis","authors":"A. Cardellicchio, R. Dario, V. Di Lecce, C. Guaragnella, A. Lombardi, L. Mongelli, Alessandro Quarto, D. Soldo","doi":"10.1109/EESMS.2017.8052697","DOIUrl":"https://doi.org/10.1109/EESMS.2017.8052697","url":null,"abstract":"Population ageing, i.e. the increase of the median age in the population, has become a pressing matter, due to a significant rise in health-care expenses, which, if not controlled, may destabilize welfare mechanisms. To face this problem, various approaches have already been presented in literature: some of these methods acquire pathology-relevant data using ad-hoc systems (i.e. sensor arrays), while others employs commercial devices, like wearable or smartphones; however, there are several issues that such systems must address, i.e. the meaningfulness of sensed data, or the wellness of the patients in the follow-up phase. The aim of this work is to propose a medical decision support system, specifically oriented to the acquisition of data related to neurological pathologies, whose final aim is to build an extended medical knowledge base, which will be used by skilled professionals in the diagnosis phase of the pathology; to improve the comfort of the patients, a smartphone medical app has been developed and tested, therefore evaluating the relevance of the data acquired by sensors embedded in an Android smartphone, and assessing the overall feasibility of such a decision support system.","PeriodicalId":285890,"journal":{"name":"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116129402","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 : 2017-07-01DOI: 10.1109/EESMS.2017.8052678
M. Tesfaye, M. Nardello, D. Brunelli
Demand and cost of electricity is expected to grow in the next years. This has raised interest in monitoring energy usage to reduce losses, and to provide real-time feedback about the cost of the electrical power consumed. This paper focuses on the implementation of a stand-alone system capable of real-time tracking of the power used and that provides power consumption estimation for each device from a single point of measurement. The learning activity is done by detecting the possible state of the electrical devices using a clustering algorithm, which involves k-means technique to analyze and detect the state of an appliance.
{"title":"Residential electrical consumption disaggregation on a single low-cost meter","authors":"M. Tesfaye, M. Nardello, D. Brunelli","doi":"10.1109/EESMS.2017.8052678","DOIUrl":"https://doi.org/10.1109/EESMS.2017.8052678","url":null,"abstract":"Demand and cost of electricity is expected to grow in the next years. This has raised interest in monitoring energy usage to reduce losses, and to provide real-time feedback about the cost of the electrical power consumed. This paper focuses on the implementation of a stand-alone system capable of real-time tracking of the power used and that provides power consumption estimation for each device from a single point of measurement. The learning activity is done by detecting the possible state of the electrical devices using a clustering algorithm, which involves k-means technique to analyze and detect the state of an appliance.","PeriodicalId":285890,"journal":{"name":"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128808373","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 : 2017-07-01DOI: 10.1109/EESMS.2017.8052693
Leone Pasquato, Nicola Bonotto, Pietro Tosato, D. Brunelli
We present the design optimization of an energy harvesting device based on the aeroelastic flutter effect, developed for converting wind energy in electrical energy. Due to the aeroelastic mechanical principle, the energy harvester can be equipped with a system capable to follow the Maximum Power Point of the wind generator and then to sustain the energy demand of a sensor system used for pollution monitoring. The aeroelastic harvester consists of a tensioned ribbon coupled with an electromagnetic transducer and a power conditioning unit to guarantee the power supply for remote sensors deployed in hard-to-reach areas. This paper presents the characterization of the wind flutter generator and the design of a Maximum Power Point Tracking (MPPT) logic that controls the tension of the belt for the maximum energy extraction.
{"title":"An optimized wind energy harvester for remote pollution monitoring","authors":"Leone Pasquato, Nicola Bonotto, Pietro Tosato, D. Brunelli","doi":"10.1109/EESMS.2017.8052693","DOIUrl":"https://doi.org/10.1109/EESMS.2017.8052693","url":null,"abstract":"We present the design optimization of an energy harvesting device based on the aeroelastic flutter effect, developed for converting wind energy in electrical energy. Due to the aeroelastic mechanical principle, the energy harvester can be equipped with a system capable to follow the Maximum Power Point of the wind generator and then to sustain the energy demand of a sensor system used for pollution monitoring. The aeroelastic harvester consists of a tensioned ribbon coupled with an electromagnetic transducer and a power conditioning unit to guarantee the power supply for remote sensors deployed in hard-to-reach areas. This paper presents the characterization of the wind flutter generator and the design of a Maximum Power Point Tracking (MPPT) logic that controls the tension of the belt for the maximum energy extraction.","PeriodicalId":285890,"journal":{"name":"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130528431","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 : 1900-01-01DOI: 10.1109/EESMS.2017.8052686
Alberto Girolami, D. Brunelli, L. Benini
In this paper we present a low-cost distributed embedded system for Structural Health Monitoring (SHM) that uses very cost-effective MEMS accelerometers, instead of more expensive piezoelectric analog transducers. The proposed platform provides online filtering and fusion of the collected data directly on-board. Data are transmitted after processing using a WiFi transceiver. Low-cost and synchronized devices permit to have more fine-grained measurements and a comprehensive assessment of the whole building, by evaluating their response to vibrations. The challenge addressed in this paper is to execute a quite computationally-demanding digital filtering on a low-cost microcontroller STM32, and to reduce the signal-to-noise ratio typical of MEMS devices with a spatial redundancy of the sensors. Our work poses the basis for low-cost methods for elaborating complex modal analysis of buildings and structures.
{"title":"Low-cost and distributed health monitoring system for critical buildings","authors":"Alberto Girolami, D. Brunelli, L. Benini","doi":"10.1109/EESMS.2017.8052686","DOIUrl":"https://doi.org/10.1109/EESMS.2017.8052686","url":null,"abstract":"In this paper we present a low-cost distributed embedded system for Structural Health Monitoring (SHM) that uses very cost-effective MEMS accelerometers, instead of more expensive piezoelectric analog transducers. The proposed platform provides online filtering and fusion of the collected data directly on-board. Data are transmitted after processing using a WiFi transceiver. Low-cost and synchronized devices permit to have more fine-grained measurements and a comprehensive assessment of the whole building, by evaluating their response to vibrations. The challenge addressed in this paper is to execute a quite computationally-demanding digital filtering on a low-cost microcontroller STM32, and to reduce the signal-to-noise ratio typical of MEMS devices with a spatial redundancy of the sensors. Our work poses the basis for low-cost methods for elaborating complex modal analysis of buildings and structures.","PeriodicalId":285890,"journal":{"name":"2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS)","volume":"375 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123317328","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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