PerCom Workshops最新文献

{"title":"Smart sensing and analytics for cognitive health care","authors":"Sajal K. Das","doi":"10.1109/PERCOMW.2016.7457164","DOIUrl":"https://doi.org/10.1109/PERCOMW.2016.7457164","url":null,"abstract":"The rapidly growing elderly population in many parts of the world has started to impact private and public interests — emotionally, socially and economically. Specifically, current demographics, medical and social trends in the U.S. indicate a large population of older adults being at risk of early cognitive impairment and dementia. This motivates us to design innovative technology solutions for cognitive health assessment, care and well-being in elderly people in their own environments of daily life. Among many significant challenges involved in such technology assisted solutions, an important one is multi-modal sensing and complex sensor data analytics on resource constrained wearable and regularly used daily objects, particularly in the presence of uncertainty and noisy environments. An additional critical challenge is how to transfer the extracted knowledge into actionable information to be effectively used by family members, doctors and caregivers. This keynote talk will present our ongoing research on elderly cognitive care with the help of complex at-home activities recognition with smart wearable, and user emotional behavior signatures detection with smart chair. For activity recognition, our innovative methodology is the first of its kind that detects 21 fine-grained and complex at-home activities by wearable devices, as compared to typical 6–12 at-home activities recognized by relevant works in the existing literature. The smart chair based novel framework can accurately detect user functional and emotional activities, in addition to static and movement based sedentary postures that are more common in current works. In collaboration with Phelps County Regional Medical Center, the proposed solutions are being validated with clinical data and patient studies.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133632043","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}

{"title":"CoSDEO 2016: Contact-free ambient sensing - Welcome and committees: Welcome message from the CoSDEO 2016 workshop co-chairs","authors":"Flora D. Salim, S. Sigg, Chenren Xu","doi":"10.1109/PERCOMW.2016.7457041","DOIUrl":"https://doi.org/10.1109/PERCOMW.2016.7457041","url":null,"abstract":"It is our great pleasure to welcome you to the Fifth CoSDEO Workshop in conjunction with the IEEE Intemational Conference on Pe1vasive Computing and Communications (PerCom 2016). This is the first time that the CoSDEO comes to Australia and the first time that it is co-located with PerCom. This year's edition focuses on contact-free ambient sensing, localisation and tracking, extending the successful theme of the fourth CoSDEO workshop on Device-Free Radio-Based Recognition. This years program has selected 6 exciting papers embracing a number of diverse perspectives across the field. Regarding the submission and review process for CoSDEO 2016, each paper was reviewed by at least two to five technical program committee (TPC) members. This year, 6 excellent papers were finally selected for inclusion at the workshop. The topics of the papers reflect the wide spectrum research around mobile computing, se1vices, and applications, ranging from activity recognition, sentiment sensing, advances towards accurate Radio-Tomographic Imaging, Deep-Leaming approaches to RFbased recognition, Audio-based mechanisms, implementations utilising depth-sensors as well as multivariate ambient sensing.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123219793","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}

{"title":"Tracking your every move - Today and tomorrow","authors":"Jakob Eriksson","doi":"10.1109/PERCOMW.2015.7134072","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134072","url":null,"abstract":"Not too long ago, tracking the movements of individuals was an obscure activity largely reserved for detective novels and the occasional creepy stalker. Lately, however, massive-scale continuous location surveillance has quietly become a fact of life, pursued by organizations as diverse as Google, Amazon, the Drug Enforcement Agency, and the Department of Transportation, not to mention cyber-criminals, jealous spouses and helicopter parents. An equally wide range of technologies is used for this virtual stakeout job, including spyware on your laptop and mobile devices, roadside radio receivers (Wi-Fi, Bluetooth and more), license plate reading devices, face-recognizing surveillance cameras, RFID tags and readers, and more. In this keynote lecture, we will review some of the more pervasive people-tracking methods in use today, together with some of their more (or less) well-known uses. We'll then put on a pair of decidedly rose-colored glasses, and try to see what good our Orwellian future may bring, and what challenges lie ahead, beyond the quaint notion of protecting your location privacy.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"455 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122186424","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}

{"title":"Clinical translation: Smart technologies for health assessment and intervention","authors":"M. Schmitter-Edgecombe","doi":"10.1109/PERCOMW.2015.7134057","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134057","url":null,"abstract":"Smart technologies that can adapt, sense, infer, learn, anticipate and intervene offer possibilities for improving health care delivery. Continuous data collection afforded by smart technologies has the potential to enrich patients' clinical pictures by augmenting self- and informant-report data and testing data collected during an office visit. Smart technologies also offer possibilities for assisting in real-time with rehabilitation and proactive health interventions. This keynote will discuss issues of importance in creating technologies that can be translated into clinical practice. For example, improving clinical translation of smart technologies for use by health-care professionals will require demonstrating that developed health-related algorithms are reliable and valid, easily visualized and of value in clinical decision-making. The clinical translation of prompting interventions will require answers to questions regarding the best timing, content and delivery of prompts in addition to issues related to motivation and the changing clinical needs of patients. These points and others will be illustrated with examples from our research using sensor data and machine learning techniques to develop algorithms that recognize activities, provide insights on functional status and automate interventions.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131820475","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}

{"title":"Smart health vital signs from the smart home","authors":"M. Skubic","doi":"10.1109/PERCOMW.2015.7134064","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134064","url":null,"abstract":"Dr. Skubic will describe interdisciplinary research in the Center for Eldercare and Rehabilitation Technology at the University of Missouri. Longitudinal research studies with inhome sensing systems have shown that the sensors offer new vital signs for detecting early signs of illness and functional decline. This talk will focus on in-home gait and balance as vital signs for monitoring health status. Dr. Skubic will discuss the significance of gait in tracking cognitive and physical health problems and will illustrate the capture of gait through different Smart Home sensor modalities (vision, radar, and depth images). Gait and fall alerts will be included, with case studies shown from senior homes. The talk will also include a discussion of the strategies used in this work to address research challenges such as collecting data, validating algorithms, and correlating gait parameters to health status. Finally, Dr. Skubic will discuss one approach to Closing the Loop in the form of an interactive interface to facilitate remote physical therapy.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124722831","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}

{"title":"Keynote talk at the WristSense 2015 workshop the \"E Pluribus Unum\" agenda for wearables","authors":"Archan Misra","doi":"10.1109/PERCOMW.2015.7134095","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134095","url":null,"abstract":"The talk will expound on the vision where smart-watches (and other wrist-worn devices) are not just isolated sensing platforms, but part of a sensing ecosystem that couples together multiple personal devices. While an individual smartwatch can undoubtedly capture certain gestural activities and health vitals, a far richer set of applications can be enabled by performing coordinated sensing across a distributed set of one or more wrist-worn devices and a smartphone. I will describe ongoing work that involves such coordinated sensing for both (a) newer, gestural-based interactive and immersive interfaces and (b) improved unobtrusive recognition of daily activities, and the associated challenges for three key performance metrics: energy, latency and accuracy. I will provide specific examples on how we are using (i) novel distributed sensor data pipelines to enable real-time, low-latency recognition of gestures, and (ii) smartphone and infrastructural sensing as effective contextual triggers for continuous capture of commonplace urban lifestyle activities.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115953518","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}

{"title":"Crowd assisted approach for pervasive opportunistic sensing","authors":"P. Mendes, W. Junior","doi":"10.1109/PERCOMW.2015.7134068","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134068","url":null,"abstract":"The advances in the areas of microelectronics and telecommunications are helping to materialize the vision of a pervasive computing model through the incorporation of sensors and communication interfaces into objects of everyday life. In recent years, there has been a growing interest in crowd assisted sensing applications, in which people serve as the building block that can be exploited to offer pervasive opportunistic sensing at scale. This paper describes a new crowd assisted pervasive opportunistic sensing framework able of exploiting people's mobility to overcome the coverage limitation of sensors and the diversity of devices, expanding the scale of sensing applications.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116018780","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}

{"title":"Wireless clinical monitoring at scale","authors":"Chenyang Lu","doi":"10.1109/PERCOMW.2015.7134008","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134008","url":null,"abstract":"Early detection and intervention are essential for preventing clinical deterioration in patients. We are developing a two-tiered clinical warning system designed to identify the signs of clinical deterioration and provide early warning of serious clinical events at general hospital units. The first tier of the system automatically identifies patients at risk of clinical deterioration from existing electronic medical record databases. The second tier performs real-time clinical event detection based on vital sign data collected from on-body wireless sensors attached to those high-risk patients. Wireless sensor networks play an important role in clinical warning by collecting real-time vital signs for clinical decision support. This talk presents the architecture of, and our experiences with, a large-scale wireless clinical monitoring system. Our system encompasses portable wireless pulse oximeters, a wireless relay network spanning multiple hospital floors, and integration with electronic medical record databases. We report our experience and lessons learned from a 14-month clinical trial of the system in six hospital wards of Barnes-Jewish Hospital in St. Louis, Missouri. Our experiences show the feasibility of achieving reliable vital sign collection using a wireless sensor network integrated with hospital IT infrastructure and procedures. We highlight technical and non-technical elements that pose challenges in a real-world hospital environment and provide guidelines for successful and efficient deployment of similar systems. The convergence of wireless sensors, mobile computing, data mining and electronic medical record in clinical warning systems will lead to enhanced quality of care for patients in hospitals as well as outpatients in their everyday lives.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090361","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}

{"title":"Green building energy analytics: Challenges and opportunities","authors":"Nirmalya Roy","doi":"10.1109/PERCOMW.2015.7134050","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134050","url":null,"abstract":"Green building applications need efficient and finegrained determination of power consumption pattern of a wide variety of consumer-grade appliances through non-intrusive load monitoring (NILM) techniques. Fine-grained monitoring of everyday appliances can provide better feedback to the consumers and motivate them to change behavior in order to reduce their energy usage. It also helps to detect abnormal power consumption events, long-term appliance malfunctions and potential safety concerns. Commercially available plug meters can be used for individual appliance monitoring but for an entire house, each such individual plug meters are expensive and tedious to setup. In practice, deploying smart plug based NILM and acquiring the low-level power measures of a large number of devices is often difficult or impossible due to the deployment complexity and varying characteristics of devices and thus must instead be employed at the circuit or house-level and inferred through the incorporation of novel usage-based measurement and probabilistic level-based disaggregation algorithm. But the challenges in deploying non-intrusive load monitoring algorithm involve disaggregating individual devices consumption from the aggregate power measurement, as well as modeling and incorporating the usage based prediction. In this talk, I will discuss on advanced machine learning and data analytics algorithms that capture the measurement based approach and circuit level NILM with the autonomous profiling and prediction logic, and the significant practical impact of intelligent use of such profiling techniques for green building applications. Our approach help improve the performance of energy disaggregation algorithms and provide critical insights on appliance longevity, abnormal power consumption, consumer behavior and their everyday lifestyle activities. The performance of our proposed algorithms on real data traces will be presented. I will conclude this talk with our ongoing research projects in this area and future research directions.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126748368","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}

{"title":"Keynote: Context, big data, and digital prejudices","authors":"D. Nicklas","doi":"10.1109/PERCOMW.2015.7133983","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133983","url":null,"abstract":"In pervasive computing research and literature, context has mostly been seen as an information source for applications that adapt their behavior according to the current situation of their user or their (often physical) environment. This adaptation could be the change of the user interface, the performance of actions (like sending messages or triggering actuators), or the change of used resources (like network bandwidth or processing power). To determine relevant situations, many heterogeneous data sources could be used, ranging from sensor data over mined patterns in files to explicit user input. Since most sensors are not perfect, context quality has to be considered. And since many context-aware applications are mobile, the set of data sources may change during runtime. According to the widely used definition by Anind Dey, context can be “any information that can be used to characterize the situation of an entity”. In the past years, we have seen a significant increase in the so-called “big data” domain, in research, technology, and industrial usage. The desire to analyze, gain knowledge and use more and more data it in new ways is rising in a way that resemble a gold rush. Data is the new oil. Beside applications like predictive maintenance of machines or optimization of industrial processes, a main target for big data analyses are humans — in their roles as travelers, current or potential clients, or application users. We could say that big data is “any information that can be used to characterize the situation of a user”, and relate these approaches to what have been done in context modelling and reasoning. This gets even clearer when these analyses leave the virtual world (e.g., client behavior in web shops) and enter the real world (e.g., client behavior in retail). In addition to the ambiguities of the analysis itself that only leads to predictions with a limited probability, sensor data quality becomes an issue: the sensor data might be inaccurate, outdated or conflicting with other observations or physical laws; in addition, sensor data processing algorithms like object classification or tracking might lead to ambiguous results. In this talk, we will shortly review these two domains and derive what could be learned for context-aware applications. A special focus will be given on quality of context on all semantic levels, and how the improper consideration of quality issues can lead to dangerous digital prejudices.","PeriodicalId":448199,"journal":{"name":"PerCom Workshops","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114513748","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}