Pub Date : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133988
Feichen Shen, Yugyung Lee
Mobile devices have become ubiquitous, with their adoption being driven by their immediacy and sensing capabilities. Applications or apps that run on a portable computing device have recently surged in popularity. An increasing number of mobile apps and their diverse users make it difficult to select the correct app to respond to evolving situations. To address this issue, we have developed a semantic framework for mobile apps named the Situation Aware Mobile Apps Framework (SAMAF) that can achieve the goal of dynamic and automated adaptive apps for software systems responding to the mobile users' context and environmental changes. In this paper, we have implemented the SAMAF system. An assessment of the prototype of the SAMAF system has been conducted from the perspective of performance and adaptability.
{"title":"SAMAF: Situation aware mobile apps framework","authors":"Feichen Shen, Yugyung Lee","doi":"10.1109/PERCOMW.2015.7133988","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133988","url":null,"abstract":"Mobile devices have become ubiquitous, with their adoption being driven by their immediacy and sensing capabilities. Applications or apps that run on a portable computing device have recently surged in popularity. An increasing number of mobile apps and their diverse users make it difficult to select the correct app to respond to evolving situations. To address this issue, we have developed a semantic framework for mobile apps named the Situation Aware Mobile Apps Framework (SAMAF) that can achieve the goal of dynamic and automated adaptive apps for software systems responding to the mobile users' context and environmental changes. In this paper, we have implemented the SAMAF system. An assessment of the prototype of the SAMAF system has been conducted from the perspective of performance and adaptability.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"17 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":"125623322","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134061
H. Kalantarian, N. Alshurafa, Tuan Le, M. Sarrafzadeh
Studies have revealed that non-adherence to prescribed medication can lead to hospital readmissions, clinical complications, and a host of other negative patient outcomes. Though many techniques have been proposed to improve patient adherence rates, they suffer from clear drawbacks such as high complexity, user burden, and low accuracy. In this paper, we propose a two step system for detecting user adherence to medication. First, force-sensitive resistors are used to determine when the pill bottle has been opened. Subsequently, medication ingestion is detected using a smart necklace equipped with a piezoelectric sensor. Evaluations confirm high accuracy of the proposed technique.
{"title":"Non-invasive detection of medication adherence using a digital smart necklace","authors":"H. Kalantarian, N. Alshurafa, Tuan Le, M. Sarrafzadeh","doi":"10.1109/PERCOMW.2015.7134061","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134061","url":null,"abstract":"Studies have revealed that non-adherence to prescribed medication can lead to hospital readmissions, clinical complications, and a host of other negative patient outcomes. Though many techniques have been proposed to improve patient adherence rates, they suffer from clear drawbacks such as high complexity, user burden, and low accuracy. In this paper, we propose a two step system for detecting user adherence to medication. First, force-sensitive resistors are used to determine when the pill bottle has been opened. Subsequently, medication ingestion is detected using a smart necklace equipped with a piezoelectric sensor. Evaluations confirm high accuracy of the proposed technique.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"2 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":"121857188","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134081
Michael Mitchell, Ratnesh Patidar, Manik Saini, Parteek Singh, An-I Wang, P. Reiher
Privacy is an important concern for mobile computing. Users might not understand the privacy implications of their actions and therefore not alter their behavior depending on where they move, when they do so, and who is in their surroundings. Since empirical data about the privacy behavior of users in mobile environments is limited, we conducted a survey study of ~600 users recruited from Florida State University and Craigslist. Major findings include: (1) People often exercise little caution preserving privacy in mobile computing environments; they perform similar computing tasks in public and private. (2) Privacy is orthogonal to trust; people tend to change their computing behavior more around people they know than strangers. (3) People underestimate the privacy threats of mobile apps, and comply with permission requests from apps more often than operating systems. (4) Users' understanding of privacy is different from that of the security community, suggesting opportunities for additional privacy studies.
{"title":"Mobile usage patterns and privacy implications","authors":"Michael Mitchell, Ratnesh Patidar, Manik Saini, Parteek Singh, An-I Wang, P. Reiher","doi":"10.1109/PERCOMW.2015.7134081","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134081","url":null,"abstract":"Privacy is an important concern for mobile computing. Users might not understand the privacy implications of their actions and therefore not alter their behavior depending on where they move, when they do so, and who is in their surroundings. Since empirical data about the privacy behavior of users in mobile environments is limited, we conducted a survey study of ~600 users recruited from Florida State University and Craigslist. Major findings include: (1) People often exercise little caution preserving privacy in mobile computing environments; they perform similar computing tasks in public and private. (2) Privacy is orthogonal to trust; people tend to change their computing behavior more around people they know than strangers. (3) People underestimate the privacy threats of mobile apps, and comply with permission requests from apps more often than operating systems. (4) Users' understanding of privacy is different from that of the security community, suggesting opportunities for additional privacy studies.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"40 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":"133787042","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134069
Sunao Hara, M. Abe, N. Sonehara
This paper presents a sound collection system to visualize environmental sounds that are collected using a crowd-sourcing approach. An analysis of physical features is generally used to analyze sound properties; however, human beings not only analyze but also emotionally connect to sounds. If we want to visualize the sounds according to the characteristics of the listener, we need to collect not only the raw sound, but also the subjective feelings associated with them. For this purpose, we developed a sound collection system using a crowdsourcing approach to collect physical sounds, their statistics, and subjective evaluations simultaneously. We then conducted a sound collection experiment using the developed system on ten participants. We collected 6,257 samples of equivalent loudness levels and their locations, and 516 samples of sounds and their locations. Subjective evaluations by the participants are also included in the data. Next, we tried to visualize the sound on a map. The loudness levels are visualized as a color map and the sounds are visualized as icons which indicate the sound type. Finally, we conducted a discrimination experiment on the sound to implement a function of automatic conversion from sounds to appropriate icons. The classifier is trained on the basis of the GMM-UBM (Gaussian Mixture Model and Universal Background Model) method. Experimental results show that the F-measure is 0.52 and the AUC is 0.79.
{"title":"Sound collection and visualization system enabled participatory and opportunistic sensing approaches","authors":"Sunao Hara, M. Abe, N. Sonehara","doi":"10.1109/PERCOMW.2015.7134069","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134069","url":null,"abstract":"This paper presents a sound collection system to visualize environmental sounds that are collected using a crowd-sourcing approach. An analysis of physical features is generally used to analyze sound properties; however, human beings not only analyze but also emotionally connect to sounds. If we want to visualize the sounds according to the characteristics of the listener, we need to collect not only the raw sound, but also the subjective feelings associated with them. For this purpose, we developed a sound collection system using a crowdsourcing approach to collect physical sounds, their statistics, and subjective evaluations simultaneously. We then conducted a sound collection experiment using the developed system on ten participants. We collected 6,257 samples of equivalent loudness levels and their locations, and 516 samples of sounds and their locations. Subjective evaluations by the participants are also included in the data. Next, we tried to visualize the sound on a map. The loudness levels are visualized as a color map and the sounds are visualized as icons which indicate the sound type. Finally, we conducted a discrimination experiment on the sound to implement a function of automatic conversion from sounds to appropriate icons. The classifier is trained on the basis of the GMM-UBM (Gaussian Mixture Model and Universal Background Model) method. Experimental results show that the F-measure is 0.52 and the AUC is 0.79.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"56 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":"133726345","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134075
Alan Ferrari, Dario Gallucci, D. Puccinelli, S. Giordano
This paper presents the design and implementation of a Portable Open Source Energy Monitor (POEM) to enable developers to automatically test and measure the energy consumption of every single application component down to the control flow level. Based on existing portable power meter designs, POEM extends the state of the art of application analysxis with the energy annotation of the control flow down to the basic blocks, the call graph, and the Android API calls, allowing developers to locate energy leaks in their applications with high accuracy. Because the power consumption is tied to the system status, energy annotation is also coupled with system activities.
{"title":"Detecting energy leaks in Android app with POEM","authors":"Alan Ferrari, Dario Gallucci, D. Puccinelli, S. Giordano","doi":"10.1109/PERCOMW.2015.7134075","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134075","url":null,"abstract":"This paper presents the design and implementation of a Portable Open Source Energy Monitor (POEM) to enable developers to automatically test and measure the energy consumption of every single application component down to the control flow level. Based on existing portable power meter designs, POEM extends the state of the art of application analysxis with the energy annotation of the control flow down to the basic blocks, the call graph, and the Android API calls, allowing developers to locate energy leaks in their applications with high accuracy. Because the power consumption is tied to the system status, energy annotation is also coupled with system activities.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"45 1 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":"122118562","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133997
P. Giridhar, T. Abdelzaher, Jemin George, Lance M. Kaplan
Social networks, such as Twitter, carry important information on ongoing events and as such can be viewed as networks of sensors that monitor and report events in the physical world. In this paper, we concern ourselves with the challenge of event localization from Twitter feeds. We explore the quality of information that can be derived either directly or indirectly from microblog entries regarding locations of ongoing events. Contrary to prior work that used Twitter to map regions of large-footprint events, or derived coarse-grained location information, in this paper, we are interested in point-events, such as building fires or car accidents, and aim to pin-point them down to a street address. An algorithm is presented that identifies distinct event signatures in the blogosphere, clusters microblogs based on events they describe, and analyzes the resulting clusters for fine-grained location indicators. An exact event location is then derived by fusing these indicators. To evaluate the quality of derived location information, we use road-traffic-related Twitter feeds from 3 major cities in California and compare automatic event localization within our service to manually obtained ground truth data. Results show a great correspondence between our automatically determined locations and ground-truth.
{"title":"On quality of event localization from social network feeds","authors":"P. Giridhar, T. Abdelzaher, Jemin George, Lance M. Kaplan","doi":"10.1109/PERCOMW.2015.7133997","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133997","url":null,"abstract":"Social networks, such as Twitter, carry important information on ongoing events and as such can be viewed as networks of sensors that monitor and report events in the physical world. In this paper, we concern ourselves with the challenge of event localization from Twitter feeds. We explore the quality of information that can be derived either directly or indirectly from microblog entries regarding locations of ongoing events. Contrary to prior work that used Twitter to map regions of large-footprint events, or derived coarse-grained location information, in this paper, we are interested in point-events, such as building fires or car accidents, and aim to pin-point them down to a street address. An algorithm is presented that identifies distinct event signatures in the blogosphere, clusters microblogs based on events they describe, and analyzes the resulting clusters for fine-grained location indicators. An exact event location is then derived by fusing these indicators. To evaluate the quality of derived location information, we use road-traffic-related Twitter feeds from 3 major cities in California and compare automatic event localization within our service to manually obtained ground truth data. Results show a great correspondence between our automatically determined locations and ground-truth.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"133 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":"117085543","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134010
C. Ferraz, D. V. D. Silva, Jancleidsson S. da Silva
This paper presents an application model for augmenting TV viewing experience. The augmentation consists of additional media resources, which are linked to the Web according to the user profile and to TV metadata. Instead of asking the user for such data, a distributed software system captures them non-intrusively, processes the user and TV program context, and automatically searches for and delivers the context-aware resources to the viewer either on the TV screen or on a second screen. The use of TV metadata as context data is a remarkable feature in this work. Such metadata are carried in the MPEG-2 Transport Stream, which is part of the major digital TV systems in the world. This work deals with problems such as inconsistency of TV metadata, and ineffectiveness of Web search, which could frustrate the viewer's enriched experience. The research indicates that context-aware applications in the television domain should strongly take into account TV metadata captured opportunistically from broadcast streams, in addition to traditional context data, such as location, temperature, device capabilities, among others. The solutions presented in this paper point to a minimum-effort by the TV user, enabling a more useful, easier and more attractive Integrated TV-Internet viewing experience.
{"title":"A collaborative TV-Internet application model to enrich TV viewing experience in a pervasive way","authors":"C. Ferraz, D. V. D. Silva, Jancleidsson S. da Silva","doi":"10.1109/PERCOMW.2015.7134010","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134010","url":null,"abstract":"This paper presents an application model for augmenting TV viewing experience. The augmentation consists of additional media resources, which are linked to the Web according to the user profile and to TV metadata. Instead of asking the user for such data, a distributed software system captures them non-intrusively, processes the user and TV program context, and automatically searches for and delivers the context-aware resources to the viewer either on the TV screen or on a second screen. The use of TV metadata as context data is a remarkable feature in this work. Such metadata are carried in the MPEG-2 Transport Stream, which is part of the major digital TV systems in the world. This work deals with problems such as inconsistency of TV metadata, and ineffectiveness of Web search, which could frustrate the viewer's enriched experience. The research indicates that context-aware applications in the television domain should strongly take into account TV metadata captured opportunistically from broadcast streams, in addition to traditional context data, such as location, temperature, device capabilities, among others. The solutions presented in this paper point to a minimum-effort by the TV user, enabling a more useful, easier and more attractive Integrated TV-Internet viewing experience.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"1 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":"129491896","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134105
L. Malott, Pratool Bharti, Nicholas Hilbert, G. Gopalakrishna, S. Chellappan
In the United States, there are more than 35, 000 reported suicides with approximately 1, 800 of them being psychiatric inpatients. Staff perform intermittent or continuous observations in order to prevent such tragedies, but a study of 98 articles over time showed that 20% to 62% of suicides happened while inpatients were on an observation schedule. Reducing the instances of suicides of inpatients is a problem of critical importance to both patients and healthcare providers. In this paper, we introduce SHARE - A Self-Harm Activity Recognition Engine, which attempts to infer self-harming activities from sensing accelerometer data using smart devices worn on a subject's wrist. Preliminary classification accuracy of 80% was achieved using data acquired from 4 subjects performing a series of activities (both self-harming and not). The results, application, and proposed technology platform are discussed in-depth.
{"title":"Detecting self-harming activities with wearable devices","authors":"L. Malott, Pratool Bharti, Nicholas Hilbert, G. Gopalakrishna, S. Chellappan","doi":"10.1109/PERCOMW.2015.7134105","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134105","url":null,"abstract":"In the United States, there are more than 35, 000 reported suicides with approximately 1, 800 of them being psychiatric inpatients. Staff perform intermittent or continuous observations in order to prevent such tragedies, but a study of 98 articles over time showed that 20% to 62% of suicides happened while inpatients were on an observation schedule. Reducing the instances of suicides of inpatients is a problem of critical importance to both patients and healthcare providers. In this paper, we introduce SHARE - A Self-Harm Activity Recognition Engine, which attempts to infer self-harming activities from sensing accelerometer data using smart devices worn on a subject's wrist. Preliminary classification accuracy of 80% was achieved using data acquired from 4 subjects performing a series of activities (both self-harming and not). The results, application, and proposed technology platform are discussed in-depth.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"89 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":"127893398","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134058
J. Lundström, W. O. D. Morais, M. Cooney
Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve. In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios.
{"title":"A holistic smart home demonstrator for anomaly detection and response","authors":"J. Lundström, W. O. D. Morais, M. Cooney","doi":"10.1109/PERCOMW.2015.7134058","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134058","url":null,"abstract":"Applying machine learning methods in scenarios involving smart homes is a complex task. The many possible variations of sensors, feature representations, machine learning algorithms, middle-ware architectures, reasoning/decision schemes, and interactive strategies make research and development tasks non-trivial to solve. In this paper, the use of a portable, flexible and holistic smart home demonstrator is proposed to facilitate iterative development and the acquisition of feedback when testing in regard to the above-mentioned issues. Specifically, the focus in this paper is on scenarios involving anomaly detection and response. First a model for anomaly detection is trained with simulated data representing a priori knowledge pertaining to a person living in an apartment. Then a reasoning mechanism uses the trained model to infer and plan a reaction to deviating activities. Reactions are carried out by a mobile interactive robot to investigate if a detected anomaly constitutes a true emergency. The implemented demonstrator was able to detect and respond properly in 18 of 20 trials featuring normal and deviating activity patterns, suggesting the feasibility of the proposed approach for such scenarios.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"12 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":"127979124","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133987
A. Alhamoud, Pei Xu, F. Englert, Philipp Scholl, T. Nguyen, Doreen Böhnstedt, R. Steinmetz
In the recent years, smart home projects started to gain great attention from academic as well as industrial communities. However, an essential challenge that all smart home ideas face is the provision of the ground truth i.e. the labeled training data required to train the machine learning algorithms which achieve the smartness of the smart home. Another challenging task is to evaluate the correctness of the collected ground truth so that we can be sure that we train the system with correct data which represents the reality. In order to build a smart home which is interactive and adaptable to the behavior and preferences of its inhabitants, we need to have comprehensive information about the everyday behavior and preferences of the inhabitants of the smart home. This comprehensive information which needs to be collected represents the ground truth in the context of our smart home research. Many technologies have been utilized in order to collect this information. In this paper, we present our approach for collecting the ground truth in smart homes in a nonintrusive way. More importantly, we present our methodology for evaluating the correctness of the collected ground truth.
{"title":"Evaluation of user feedback in smart home for situational context identification","authors":"A. Alhamoud, Pei Xu, F. Englert, Philipp Scholl, T. Nguyen, Doreen Böhnstedt, R. Steinmetz","doi":"10.1109/PERCOMW.2015.7133987","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133987","url":null,"abstract":"In the recent years, smart home projects started to gain great attention from academic as well as industrial communities. However, an essential challenge that all smart home ideas face is the provision of the ground truth i.e. the labeled training data required to train the machine learning algorithms which achieve the smartness of the smart home. Another challenging task is to evaluate the correctness of the collected ground truth so that we can be sure that we train the system with correct data which represents the reality. In order to build a smart home which is interactive and adaptable to the behavior and preferences of its inhabitants, we need to have comprehensive information about the everyday behavior and preferences of the inhabitants of the smart home. This comprehensive information which needs to be collected represents the ground truth in the context of our smart home research. Many technologies have been utilized in order to collect this information. In this paper, we present our approach for collecting the ground truth in smart homes in a nonintrusive way. More importantly, we present our methodology for evaluating the correctness of the collected ground truth.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"26 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":"126790476","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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