Pub Date : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730783
Juan Ye
Sensor-based human activity recognition is to recognise users' current activities from a collection of sensor data in real time. This ability presents an unprecedented opportunity to many applications, and ambient assisted living (AAL) for elderly care is one of the most exciting examples. For example, from the meal preparation activities, we can derive the user's diet routine and detect any anomaly or decline in physical or cognitive condition, leading to immediate, appropriate change in their care plan. With the rapidly increasing ageing population and overstretched strains on our healthcare system, there is a rapidly growing need for industry in AAL. However, the complexity in real-world deployment is significantly challenging current sensor-based human activity recognition, including the inherent imperfect nature of sensing technologies, constant change in activity routines, and unpredictability of situations or events occurring in an environment. Such complexity can result in decreased accuracies in recognising activities over time and further a degradation of the performance of an AAL system. The state-of-the-art methodology in studying human activity recognition is cultivated from short-term lab or testbed experimentation, i.e., relying on well-annotated sensor data and assuming no change in activity models, which is no longer suitable for long-term, large-scale, real-world deployment. This creates a need for an activity recognition system capable of embedding the means of automatic adaptation to changes, i.e., lifelong learning. This talk will discuss new challenges and opportunities in lifelong learning in human activity recognition, with particular focus on transfer learning on activity labels across heterogeneous datasets.
{"title":"Lifelong Learning in Sensor-Based Human Activity Recognition","authors":"Juan Ye","doi":"10.1109/PERCOMW.2019.8730783","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730783","url":null,"abstract":"Sensor-based human activity recognition is to recognise users' current activities from a collection of sensor data in real time. This ability presents an unprecedented opportunity to many applications, and ambient assisted living (AAL) for elderly care is one of the most exciting examples. For example, from the meal preparation activities, we can derive the user's diet routine and detect any anomaly or decline in physical or cognitive condition, leading to immediate, appropriate change in their care plan. With the rapidly increasing ageing population and overstretched strains on our healthcare system, there is a rapidly growing need for industry in AAL. However, the complexity in real-world deployment is significantly challenging current sensor-based human activity recognition, including the inherent imperfect nature of sensing technologies, constant change in activity routines, and unpredictability of situations or events occurring in an environment. Such complexity can result in decreased accuracies in recognising activities over time and further a degradation of the performance of an AAL system. The state-of-the-art methodology in studying human activity recognition is cultivated from short-term lab or testbed experimentation, i.e., relying on well-annotated sensor data and assuming no change in activity models, which is no longer suitable for long-term, large-scale, real-world deployment. This creates a need for an activity recognition system capable of embedding the means of automatic adaptation to changes, i.e., lifelong learning. This talk will discuss new challenges and opportunities in lifelong learning in human activity recognition, with particular focus on transfer learning on activity labels across heterogeneous datasets.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130238808","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 : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730799
Güven Aşçı, M. A. Güvensan
The capability of mobile phones are increasing with the development of hardware and software technology. Especially sensors on smartphones enable to collect environmental and personal information. Thus, with the help of smartphones, human activity recognition and transport mode detection (TMD) become the main research areas in the last decade. This study aims to introduce a novel input set for daily activities mainly for transportation modes in order to increase the detection rate. In this study, the frame-based novel input set consisting of time-domain and frequency-domain features is fed to LSTM network. Thus, the classification ratio on HTC public dataset for 10 different transportation modes is climbed up to 97% which is 2% more than the state-of-the-art method in the literature.
{"title":"A Novel Input Set for LSTM-Based Transport Mode Detection","authors":"Güven Aşçı, M. A. Güvensan","doi":"10.1109/PERCOMW.2019.8730799","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730799","url":null,"abstract":"The capability of mobile phones are increasing with the development of hardware and software technology. Especially sensors on smartphones enable to collect environmental and personal information. Thus, with the help of smartphones, human activity recognition and transport mode detection (TMD) become the main research areas in the last decade. This study aims to introduce a novel input set for daily activities mainly for transportation modes in order to increase the detection rate. In this study, the frame-based novel input set consisting of time-domain and frequency-domain features is fed to LSTM network. Thus, the classification ratio on HTC public dataset for 10 different transportation modes is climbed up to 97% which is 2% more than the state-of-the-art method in the literature.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129564733","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 : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730719
Gabriele Civitarese
With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of cognitive decline. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This talk presents our latest research efforts on these topics. In particular, the talk will cover: a) novel unobtrusive sensing solutions, b) hybrid ADLs recognition methods and c) techniques to detect abnormal behaviors at a fine granularity. We will discuss those challenges reporting our experience and identifying critical aspects which still need to be investigated.
{"title":"Human Activity Recognition in Smart-Home Environments for Health-Care Applications","authors":"Gabriele Civitarese","doi":"10.1109/PERCOMW.2019.8730719","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730719","url":null,"abstract":"With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of cognitive decline. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This talk presents our latest research efforts on these topics. In particular, the talk will cover: a) novel unobtrusive sensing solutions, b) hybrid ADLs recognition methods and c) techniques to detect abnormal behaviors at a fine granularity. We will discuss those challenges reporting our experience and identifying critical aspects which still need to be investigated.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128489239","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 : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730682
M. Garcia-Constantino, A. Konios, Idongesit Ekerete, S. Christopoulos, Colin Shewell, C. Nugent, Gareth Morrison
This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from sensor data collected from 30 participants. The ADLs considered are: (i) preparing and drinking tea, and (ii) preparing and drinking coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal aspect of the sequences of actions that are part of each ADL and that vary between participants. The average and standard deviation for the durations of each action were calculated to define an average time and a range in which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) was used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity. The data analysis show that CDF can provide accurate and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute. Finally, this approach could be used to train machine learning algorithms for the abnormal behaviour detection.
{"title":"Probabilistic Analysis of Abnormal Behaviour Detection in Activities of Daily Living","authors":"M. Garcia-Constantino, A. Konios, Idongesit Ekerete, S. Christopoulos, Colin Shewell, C. Nugent, Gareth Morrison","doi":"10.1109/PERCOMW.2019.8730682","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730682","url":null,"abstract":"This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from sensor data collected from 30 participants. The ADLs considered are: (i) preparing and drinking tea, and (ii) preparing and drinking coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal aspect of the sequences of actions that are part of each ADL and that vary between participants. The average and standard deviation for the durations of each action were calculated to define an average time and a range in which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) was used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity. The data analysis show that CDF can provide accurate and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute. Finally, this approach could be used to train machine learning algorithms for the abnormal behaviour detection.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124536112","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 : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730669
Lewis Tseng, Liwen Wong
It is difficult to overstate how large a role Intelligent Transportation Systems (ITS) technology has played in advancing safety, mobility, and productivity in our daily lives. ITS encompasses a broad range of technologies, including information and communication technologies, transportation and communication infrastructures, connected vehicles, and emerging technologies such as Internet-of-Things (IoT). It has been studied extensively in many different disciplines, including transportation, communication, database and management communities. Unfortunately, there are still many unsolved challenges that hinder the large deployment of advanced ITS systems. Recent studies have proposed using Blockchain, an emerging technology that enables decentralized coordination, to address inherent challenges in ITS such as security and scalability. However, these studies did not address a key question: how can we achieve a sustainable ITS ecosystem? This paper presents our preliminary study where we first point out the limitations of prior Blockchain-based ITS systems and then outline an architecture to support a sustainable ITS ecosystem. Our main goal is to stimulate further effort and cross-disciplinary collaboration by providing guidance and reference for future studies.
{"title":"Towards a Sustainable Ecosystem of Intelligent Transportation Systems","authors":"Lewis Tseng, Liwen Wong","doi":"10.1109/PERCOMW.2019.8730669","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730669","url":null,"abstract":"It is difficult to overstate how large a role Intelligent Transportation Systems (ITS) technology has played in advancing safety, mobility, and productivity in our daily lives. ITS encompasses a broad range of technologies, including information and communication technologies, transportation and communication infrastructures, connected vehicles, and emerging technologies such as Internet-of-Things (IoT). It has been studied extensively in many different disciplines, including transportation, communication, database and management communities. Unfortunately, there are still many unsolved challenges that hinder the large deployment of advanced ITS systems. Recent studies have proposed using Blockchain, an emerging technology that enables decentralized coordination, to address inherent challenges in ITS such as security and scalability. However, these studies did not address a key question: how can we achieve a sustainable ITS ecosystem? This paper presents our preliminary study where we first point out the limitations of prior Blockchain-based ITS systems and then outline an architecture to support a sustainable ITS ecosystem. Our main goal is to stimulate further effort and cross-disciplinary collaboration by providing guidance and reference for future studies.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115791500","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 : 2019-03-01DOI: 10.1109/percomw.2019.8730760
{"title":"PerPersuasion'19: PerPersuasion'19 – 1st International Workshop on Pervasive Persuasive System for Behavior Change - Program","authors":"","doi":"10.1109/percomw.2019.8730760","DOIUrl":"https://doi.org/10.1109/percomw.2019.8730760","url":null,"abstract":"","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121360251","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 : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730581
Sizhen Bian, V. F. Rey, Junaid Younas, P. Lukowicz
Given the wide and increasing popularity of smart-watches, the wrist is a compelling location for placing sensors. On the other hand, only specific information such as hand/arm motions and selected physiological signals are readily available at the wrist. In this paper, we explore a novel wrist-worn sensing approach that allows information not typically associated with the wrist or the arm to be acquired by exploring the ubiquitous near-field electric phenomena. We first introduce the design of an ultra-low power near-field electric field sensing prototype, which is able to sense $uV$ level potential variation caused by disturbance or movement of the human body in an environment. Then we demonstrate how our prototype can detect motions of various body parts beyond the wrist, such as touch and proximity between users and objects. Finally, a use case related to a collaborative work by two people is recorded by deploying our prototypes both at surrounding objects and on wrists, presenting the feasibility of collaborative work monitoring by sensing the variation of the near field electric field.
{"title":"Wrist-Worn Capacitive Sensor for Activity and Physical Collaboration Recognition","authors":"Sizhen Bian, V. F. Rey, Junaid Younas, P. Lukowicz","doi":"10.1109/PERCOMW.2019.8730581","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730581","url":null,"abstract":"Given the wide and increasing popularity of smart-watches, the wrist is a compelling location for placing sensors. On the other hand, only specific information such as hand/arm motions and selected physiological signals are readily available at the wrist. In this paper, we explore a novel wrist-worn sensing approach that allows information not typically associated with the wrist or the arm to be acquired by exploring the ubiquitous near-field electric phenomena. We first introduce the design of an ultra-low power near-field electric field sensing prototype, which is able to sense $uV$ level potential variation caused by disturbance or movement of the human body in an environment. Then we demonstrate how our prototype can detect motions of various body parts beyond the wrist, such as touch and proximity between users and objects. Finally, a use case related to a collaborative work by two people is recorded by deploying our prototypes both at surrounding objects and on wrists, presenting the feasibility of collaborative work monitoring by sensing the variation of the near field electric field.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127072396","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 : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730798
Yiannis Gkoufas, S. Braghin
Indoor Positioning Systems are gaining market momentum, mainly due to the significant reduction of sensor cost (on smartphones or standalone) and leveraging standardization of related technology. Among various alternatives for accurate and cost-effective Indoor Positioning System, positioning based on the Magnetic Field has proven popular, as it does not require specialized infrastructure. Related experimental results have demonstrated good positioning accuracy. However, when transitioned to production deployments, these systems exhibit serious drawbacks to make them practical: a) accuracy fluctuates significantly across smartphone models and configurations and b) costly continuous manual fingerprinting of the area is required. The developed Indoor Positioning System Copernicus is a self-learning, adaptive system that is shown to exhibit improved accuracy across different smartphone models. Copernicus leverages a minimal deployment of Bluetooth Low Energy Beacons to infer the trips of users, learn and eventually build tailored Magnetic Maps for every smartphone model for the specific indoor area. In a practical deployment, after each trip execution by the users we can observe an increase in the accuracy of positioning.
{"title":"Anatomy and Deployment of Robust AI-Centric Indoor Positioning System","authors":"Yiannis Gkoufas, S. Braghin","doi":"10.1109/PERCOMW.2019.8730798","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730798","url":null,"abstract":"Indoor Positioning Systems are gaining market momentum, mainly due to the significant reduction of sensor cost (on smartphones or standalone) and leveraging standardization of related technology. Among various alternatives for accurate and cost-effective Indoor Positioning System, positioning based on the Magnetic Field has proven popular, as it does not require specialized infrastructure. Related experimental results have demonstrated good positioning accuracy. However, when transitioned to production deployments, these systems exhibit serious drawbacks to make them practical: a) accuracy fluctuates significantly across smartphone models and configurations and b) costly continuous manual fingerprinting of the area is required. The developed Indoor Positioning System Copernicus is a self-learning, adaptive system that is shown to exhibit improved accuracy across different smartphone models. Copernicus leverages a minimal deployment of Bluetooth Low Energy Beacons to infer the trips of users, learn and eventually build tailored Magnetic Maps for every smartphone model for the specific indoor area. In a practical deployment, after each trip execution by the users we can observe an increase in the accuracy of positioning.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125444492","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 : 2019-03-01DOI: 10.1109/PERCOMW.2019.8730809
M. Shimosaka, Qiyang Zhang, Kazunari Takeichi
Automated activity assessment using IoT/smartphone sensors becomes great popular in ubiquitous computing research community recent year thanks to the enhancement of mobility and IoT sensing. In these researches, owing to the great success of statistical machine learning technique called Lasso, the work offers the interpretability of the model. However, in some sparse feature condition, Lasso as a $l_{1}$ regression method could not give a satisfying result for prediction precision and feature selection. In this paper, we propose a new prediction scheme using greedy feature selection method which is expected to be effective under large scale feature in limited number of dataset. With the help of the new scheme, we could solve the overfitting problem when using $l_{1}$ regression as well as giving satisfying prediction result. Experimental results using longitudinal pyro-sensor dataset of health score of elderly people show that our new scheme offers better interpretability as well as achieves better prediction accuracy compared with Lasso
{"title":"Robust Health Score Prediction from Pyro-Sensor Activity Data based on Greedy Feature Selection","authors":"M. Shimosaka, Qiyang Zhang, Kazunari Takeichi","doi":"10.1109/PERCOMW.2019.8730809","DOIUrl":"https://doi.org/10.1109/PERCOMW.2019.8730809","url":null,"abstract":"Automated activity assessment using IoT/smartphone sensors becomes great popular in ubiquitous computing research community recent year thanks to the enhancement of mobility and IoT sensing. In these researches, owing to the great success of statistical machine learning technique called Lasso, the work offers the interpretability of the model. However, in some sparse feature condition, Lasso as a $l_{1}$ regression method could not give a satisfying result for prediction precision and feature selection. In this paper, we propose a new prediction scheme using greedy feature selection method which is expected to be effective under large scale feature in limited number of dataset. With the help of the new scheme, we could solve the overfitting problem when using $l_{1}$ regression as well as giving satisfying prediction result. Experimental results using longitudinal pyro-sensor dataset of health score of elderly people show that our new scheme offers better interpretability as well as achieves better prediction accuracy compared with Lasso","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126203215","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 : 2019-03-01DOI: 10.1109/percomw.2019.8730748
A. Bernardos, Jesús García, Hideo Saito, P. Marti
{"title":"UNAGI'19 - Workshop on UNmanned aerial vehicle Applications in the Smart City: from Guidance technology to enhanced system Interaction - Welcome and Committees","authors":"A. Bernardos, Jesús García, Hideo Saito, P. Marti","doi":"10.1109/percomw.2019.8730748","DOIUrl":"https://doi.org/10.1109/percomw.2019.8730748","url":null,"abstract":"","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127989867","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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