Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers最新文献
Tzu-Chieh Lin, Yu-Shao Su, Emily Yang, Yun Han Chen, Hao-Ping Lee, Yung-Ju Chang
Research shows that smartphone users often attend to phone notifications that are in the middle of the notification list. This suggests a mismatch between the display order and the users' attendance order on the notifications. Yet, we know little about how users would like their notifications to be sorted and presented. This paper presents the preliminary results of a mixed-methods study of the difference between smartphone users' attendance order and their desired display order of smartphone notifications. Our preliminary results show that a mismatch between attendance order and desired display order existed in nearly half of cases. Specifically, many users desired certain categories of notifications to be placed higher in their notification drawers than their actual notification-attendance behaviors would tend to suggest. Additionally, while our participants felt that some notifications have low-attractiveness senders or content, such as shopping-related ones, they would want the system to give them a higher priority.
{"title":"A preliminary investigation of the mismatch between attendance order and desired display order of smartphone notifications","authors":"Tzu-Chieh Lin, Yu-Shao Su, Emily Yang, Yun Han Chen, Hao-Ping Lee, Yung-Ju Chang","doi":"10.1145/3410530.3414384","DOIUrl":"https://doi.org/10.1145/3410530.3414384","url":null,"abstract":"Research shows that smartphone users often attend to phone notifications that are in the middle of the notification list. This suggests a mismatch between the display order and the users' attendance order on the notifications. Yet, we know little about how users would like their notifications to be sorted and presented. This paper presents the preliminary results of a mixed-methods study of the difference between smartphone users' attendance order and their desired display order of smartphone notifications. Our preliminary results show that a mismatch between attendance order and desired display order existed in nearly half of cases. Specifically, many users desired certain categories of notifications to be placed higher in their notification drawers than their actual notification-attendance behaviors would tend to suggest. Additionally, while our participants felt that some notifications have low-attractiveness senders or content, such as shopping-related ones, they would want the system to give them a higher priority.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90855013","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}
With the booming of online mobile games (OMGs), game operators need to provide high-quality game service for users. Using relay has become the de factor approach for game streaming today, because it is easy to use (e.g., game sessions can be redirected via CDN servers) and has good scalability. Today, it has become the norm rather than the exception for game operators to hire CDN servers for their game services in a pay-per-use manner to serve massive users. Given the limited resource, selecting game sessions which are relayed has become a critical decision that can significantly affect users' quality of experience (QoE). Conventional strategies are generally rule-based, e.g., assigning game sessions to relay paths according to their past network performance, but cannot guarantee any particular QoE level because network performance dynamically changes. In this paper, we propose using data-driven approach to study network performance of game sessions in temporal and spatial patterns. Our findings indicate that there is obvious regularity for network performance of game sessions in temporal and spatial patterns. We design a machine learning-based predictive model to capture the quality of a game session given particular network performance metrics. Based on that, we strategically assign game sessions to relay paths to maximize the overall QoE. Trace-driven experiments are used to demonstrate the effectiveness and efficiency of our design.
{"title":"Relay strategy in online mobile games: a data-driven approach","authors":"Guowei Zhu, Kan Lv, Ge Ma, Weixi Gu","doi":"10.1145/3410530.3414595","DOIUrl":"https://doi.org/10.1145/3410530.3414595","url":null,"abstract":"With the booming of online mobile games (OMGs), game operators need to provide high-quality game service for users. Using relay has become the de factor approach for game streaming today, because it is easy to use (e.g., game sessions can be redirected via CDN servers) and has good scalability. Today, it has become the norm rather than the exception for game operators to hire CDN servers for their game services in a pay-per-use manner to serve massive users. Given the limited resource, selecting game sessions which are relayed has become a critical decision that can significantly affect users' quality of experience (QoE). Conventional strategies are generally rule-based, e.g., assigning game sessions to relay paths according to their past network performance, but cannot guarantee any particular QoE level because network performance dynamically changes. In this paper, we propose using data-driven approach to study network performance of game sessions in temporal and spatial patterns. Our findings indicate that there is obvious regularity for network performance of game sessions in temporal and spatial patterns. We design a machine learning-based predictive model to capture the quality of a game session given particular network performance metrics. Based on that, we strategically assign game sessions to relay paths to maximize the overall QoE. Trace-driven experiments are used to demonstrate the effectiveness and efficiency of our design.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88498500","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}
Fatema Sultana Chowdhury, Lauri Lovén, Marta Cortés, Eija Halkola, T. Seppänen, S. Pirttikangas
Well-being in smart environments refers to the mental, physiological and emotional states of people passing through environments where sensors, actuators and computers are intertwined with everyday tasks. In that context, well-being must be measurable and, to some extent, susceptible to external influence within the short time-spans that people spend in those environments. Continuing our previous studies, we evaluate an experiment for well-being measurement and control, introducing EEG observations in the experiment. EEG, as an immediate and objective proxy of one's mental, physiological and emotional state, provides ground truth for comparisons between sensors in the smart environment. We concentrate on the test subject's emotional state, observed by way of comparing changes in the alpha frequency power levels in the left and right frontal cortical areas, respectively corresponding to positive and negative emotions. The results show that our experimental set-up induces significant changes in the test subject's emotional state, paving the way for further studies on influencing personal well-being.
{"title":"Emotional well-being in smart environments: an experiment with EEG","authors":"Fatema Sultana Chowdhury, Lauri Lovén, Marta Cortés, Eija Halkola, T. Seppänen, S. Pirttikangas","doi":"10.1145/3410530.3414437","DOIUrl":"https://doi.org/10.1145/3410530.3414437","url":null,"abstract":"Well-being in smart environments refers to the mental, physiological and emotional states of people passing through environments where sensors, actuators and computers are intertwined with everyday tasks. In that context, well-being must be measurable and, to some extent, susceptible to external influence within the short time-spans that people spend in those environments. Continuing our previous studies, we evaluate an experiment for well-being measurement and control, introducing EEG observations in the experiment. EEG, as an immediate and objective proxy of one's mental, physiological and emotional state, provides ground truth for comparisons between sensors in the smart environment. We concentrate on the test subject's emotional state, observed by way of comparing changes in the alpha frequency power levels in the left and right frontal cortical areas, respectively corresponding to positive and negative emotions. The results show that our experimental set-up induces significant changes in the test subject's emotional state, paving the way for further studies on influencing personal well-being.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"397 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85496367","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}
Y. Tseng, Hsien-Ting Lin, Yi-Hao Lin, Jyh-cheng Chen
In this paper, our team, SensingGO, presents a hierarchical classifier for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge. We first separate the original data into motorized activities and non-motorized activities in the first layer of the classifier by using accelerometer data. For the non-motorized activities, we calculate auto-correlation values with accelerometer data as input features. For the motorized activities, we take magnetometer and barometer with mean, maximum, standard deviation values as input features. Finally, we integrate the recognition results of each layer of the classifier, and the average F1-score is 50% to the validation data.
{"title":"Hierarchical classification using ML/DL for sussex-huawei locomotion-transportation (SHL) recognition challenge","authors":"Y. Tseng, Hsien-Ting Lin, Yi-Hao Lin, Jyh-cheng Chen","doi":"10.1145/3410530.3414347","DOIUrl":"https://doi.org/10.1145/3410530.3414347","url":null,"abstract":"In this paper, our team, SensingGO, presents a hierarchical classifier for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge. We first separate the original data into motorized activities and non-motorized activities in the first layer of the classifier by using accelerometer data. For the non-motorized activities, we calculate auto-correlation values with accelerometer data as input features. For the motorized activities, we take magnetometer and barometer with mean, maximum, standard deviation values as input features. Finally, we integrate the recognition results of each layer of the classifier, and the average F1-score is 50% to the validation data.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81542599","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}
Deep neural networks consisting of a combination of convolutional feature extractor layers and Long Short Term Memory (LSTM) recurrent layers are widely used models for activity recognition from wearable sensors ---referred to as DeepConvLSTM architectures hereafter. However, the subtleties of training these models on sequential time series data is not often discussed in the literature. Continuous sensor data must be segmented into temporal 'windows', and fed through the network to produce a loss which is used to update the parameters of the network. If trained naively using batches of randomly selected data as commonly reported, then the temporal horizon (the maximum delay at which input samples can effect the output of the model) of the network is limited to the length of the window. An alternative approach, which we will call CausalBatch training, is to construct batches deliberately such that each consecutive batch contains windows which are contiguous in time with the windows of the previous batch, with only the first batch in the CausalBatch consisting of randomly selected windows. After a given number of consecutive batches (referred to as the CausalBatch duration τ), the LSTM states are reset, new random starting points are chosen from the dataset and a new CausalBatch is started. This approach allows us to increase the temporal horizon of the network without increasing the window size, which enables networks to learn data dependencies on a longer timescale without increasing computational complexity. We evaluate these two approaches on the Opportunity dataset. We find that using the CausalBatch method we can reduce the training time of DeepConvLSTM by up to 90%, while increasing the user-independent accuracy by up to 6.3% and the class weighted F1 score by up to 5.9% compared to the same model trained by random batch training with the best performing choice of window size for the latter. Compared to the same model trained using the same window length, and therefore the same computational complexity and almost identical training time, we observe an 8.4% increase in accuracy and 14.3% increase in weighted F1 score. We provide the source code for all experiments as well as a Pytorch reference implementation of DeepConvLSTM in a public github repository.
{"title":"CausalBatch","authors":"Lloyd Pellatt, D. Roggen","doi":"10.1145/3410530.3414365","DOIUrl":"https://doi.org/10.1145/3410530.3414365","url":null,"abstract":"Deep neural networks consisting of a combination of convolutional feature extractor layers and Long Short Term Memory (LSTM) recurrent layers are widely used models for activity recognition from wearable sensors ---referred to as DeepConvLSTM architectures hereafter. However, the subtleties of training these models on sequential time series data is not often discussed in the literature. Continuous sensor data must be segmented into temporal 'windows', and fed through the network to produce a loss which is used to update the parameters of the network. If trained naively using batches of randomly selected data as commonly reported, then the temporal horizon (the maximum delay at which input samples can effect the output of the model) of the network is limited to the length of the window. An alternative approach, which we will call CausalBatch training, is to construct batches deliberately such that each consecutive batch contains windows which are contiguous in time with the windows of the previous batch, with only the first batch in the CausalBatch consisting of randomly selected windows. After a given number of consecutive batches (referred to as the CausalBatch duration τ), the LSTM states are reset, new random starting points are chosen from the dataset and a new CausalBatch is started. This approach allows us to increase the temporal horizon of the network without increasing the window size, which enables networks to learn data dependencies on a longer timescale without increasing computational complexity. We evaluate these two approaches on the Opportunity dataset. We find that using the CausalBatch method we can reduce the training time of DeepConvLSTM by up to 90%, while increasing the user-independent accuracy by up to 6.3% and the class weighted F1 score by up to 5.9% compared to the same model trained by random batch training with the best performing choice of window size for the latter. Compared to the same model trained using the same window length, and therefore the same computational complexity and almost identical training time, we observe an 8.4% increase in accuracy and 14.3% increase in weighted F1 score. We provide the source code for all experiments as well as a Pytorch reference implementation of DeepConvLSTM in a public github repository.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84174665","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}
In this thesis, we plan to introduce a new IoT app development framework named Peekaboo, which aims to make it much easier for developers to get the granularity of data they actually need rather than always requesting raw data, while also offering architecture support for building privacy features across all the apps. Peekaboo's architectural design philosophy is to factor out repetitive data pre-processing tasks (e.g., face detection, frequency spectrum extraction) from the cloud side onto a user-controlled hub, and support them as a fixed set of open source, reusable, and chainable operators. These operators pre-process raw data to remove unneeded sensitive user information before the data flow to the cloud (and out of the users' control), thus reducing data egress and many potential privacy risks for users. Further, all the IoT apps built with Peekaboo share a common structure of the chainable operators, making it possible to build consistent privacy features beyond individual apps.
{"title":"Providing architectural support for building privacy-sensitive smart home applications","authors":"Haojian Jin, Swarun Kumar, Jason I. Hong","doi":"10.1145/3410530.3414328","DOIUrl":"https://doi.org/10.1145/3410530.3414328","url":null,"abstract":"In this thesis, we plan to introduce a new IoT app development framework named Peekaboo, which aims to make it much easier for developers to get the granularity of data they actually need rather than always requesting raw data, while also offering architecture support for building privacy features across all the apps. Peekaboo's architectural design philosophy is to factor out repetitive data pre-processing tasks (e.g., face detection, frequency spectrum extraction) from the cloud side onto a user-controlled hub, and support them as a fixed set of open source, reusable, and chainable operators. These operators pre-process raw data to remove unneeded sensitive user information before the data flow to the cloud (and out of the users' control), thus reducing data egress and many potential privacy risks for users. Further, all the IoT apps built with Peekaboo share a common structure of the chainable operators, making it possible to build consistent privacy features beyond individual apps.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91104785","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}
Xin Zhu, Shuai Wang, Baoshen Guo, Taiwei Ling, Ziyi Zhou, L. Tu, T. He
With a rapid growth of vehicles in modern cities, searching for a parking space becomes difficult for drivers especially in rush hours. To alleviate parking difficulties and make the most of urban parking resources, contract parking sharing services allow drivers to pay for parking under the consent of owners, reaching a win-win situation. Contract parking sharing services, however, have not yet been prevailingly adopted due to the dynamic parking time which leads to uncertainties for sharing. Thanks to the Internet of things technique, most of modern parking lots record vehicles' fine-grained parking data including entry and exit timestamps for billing purposes. Leveraging the parking data, we analyze and exploit available vacant contract parking spaces. We propose SParking, a shared contract parking system with a win-win data-driven scheduling. SParking consists of (i) a parking time prediction model to exploit reliable periods of free parking spaces and (ii) an optimal scheduling model to allocate free parking spaces to drivers. To verify the effectiveness of SParking, we evaluate our design on seven-month real-world parking data involved with 368 parking lots and 14,704 parking spaces in Wuhan, China. The experimental results show that SParking achieves more than 90% of accuracy in parking time prediction and the average utilization rate of contract parking spaces is improved by 35%.
{"title":"SParking: a win-win data-driven contract parking sharing system","authors":"Xin Zhu, Shuai Wang, Baoshen Guo, Taiwei Ling, Ziyi Zhou, L. Tu, T. He","doi":"10.1145/3410530.3414588","DOIUrl":"https://doi.org/10.1145/3410530.3414588","url":null,"abstract":"With a rapid growth of vehicles in modern cities, searching for a parking space becomes difficult for drivers especially in rush hours. To alleviate parking difficulties and make the most of urban parking resources, contract parking sharing services allow drivers to pay for parking under the consent of owners, reaching a win-win situation. Contract parking sharing services, however, have not yet been prevailingly adopted due to the dynamic parking time which leads to uncertainties for sharing. Thanks to the Internet of things technique, most of modern parking lots record vehicles' fine-grained parking data including entry and exit timestamps for billing purposes. Leveraging the parking data, we analyze and exploit available vacant contract parking spaces. We propose SParking, a shared contract parking system with a win-win data-driven scheduling. SParking consists of (i) a parking time prediction model to exploit reliable periods of free parking spaces and (ii) an optimal scheduling model to allocate free parking spaces to drivers. To verify the effectiveness of SParking, we evaluate our design on seven-month real-world parking data involved with 368 parking lots and 14,704 parking spaces in Wuhan, China. The experimental results show that SParking achieves more than 90% of accuracy in parking time prediction and the average utilization rate of contract parking spaces is improved by 35%.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73323250","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}
91% of the world's population lives in areas where air pollution exceeds safety limits1. Research has focused on monitoring ambient air pollution, but individual exposure to air pollution is not equal to ambient and is thus important to measure. Our work (in progress) measures individual exposures of different categories of people on an academic campus. We highlight some anecdotal findings and surprising insights from monitoring, such as a) Indoor CO2 concentration of 1.8 times higher than the permissible limit. Over 10 times the WHO limit of PM2.5 exposure during b) construction-related activities, and c) cooking (despite the use of exhaust). We also found that during transit, the PM2.5 exposure is at least two times higher than indoor. Our current work though in progress, already shows important findings affecting different people associated with an academic campus. In the future, we plan to do a more exhaustive study and reduce the form factor and energy needs for our sensors to scale the study.
{"title":"Do we breathe the same air?","authors":"Rishiraj Adhikary, Nipun Batra","doi":"10.1145/3410530.3414414","DOIUrl":"https://doi.org/10.1145/3410530.3414414","url":null,"abstract":"91% of the world's population lives in areas where air pollution exceeds safety limits1. Research has focused on monitoring ambient air pollution, but individual exposure to air pollution is not equal to ambient and is thus important to measure. Our work (in progress) measures individual exposures of different categories of people on an academic campus. We highlight some anecdotal findings and surprising insights from monitoring, such as a) Indoor CO2 concentration of 1.8 times higher than the permissible limit. Over 10 times the WHO limit of PM2.5 exposure during b) construction-related activities, and c) cooking (despite the use of exhaust). We also found that during transit, the PM2.5 exposure is at least two times higher than indoor. Our current work though in progress, already shows important findings affecting different people associated with an academic campus. In the future, we plan to do a more exhaustive study and reduce the form factor and energy needs for our sensors to scale the study.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"142 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88972103","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}
Wearable health devices have the potential to incentivize individuals in health-promoting behaviors and to assist in the monitoring of health conditions. Wearable epilepsy seizure monitoring devices are now evolving that can support individuals and their caregivers via the automated sensing, reporting and logging of epileptic seizures. This work contributes a novel reflection on the interface requirements of wearer users and non-wearer stakeholder users. We evaluate the "guessability" of the light pattern interface of the Empatica Embrace wrist-worn epileptic seizure monitor and provide box plot results for eight interface indications. We also report summarised feedback from a heuristic analysis with fourteen participant evaluators. The results indicate some satisfaction with the minimal aesthetic of a simple light pattern interface as well as some concerns about confusion between different indications, accessibility and reliance on recall.
{"title":"Wearable epilepsy seizure monitor user interface evaluation: an evaluation of the empatica 'embrace' interface","authors":"Tendai Rukasha, Sandra I. Woolley, Tim Collins","doi":"10.1145/3410530.3414382","DOIUrl":"https://doi.org/10.1145/3410530.3414382","url":null,"abstract":"Wearable health devices have the potential to incentivize individuals in health-promoting behaviors and to assist in the monitoring of health conditions. Wearable epilepsy seizure monitoring devices are now evolving that can support individuals and their caregivers via the automated sensing, reporting and logging of epileptic seizures. This work contributes a novel reflection on the interface requirements of wearer users and non-wearer stakeholder users. We evaluate the \"guessability\" of the light pattern interface of the Empatica Embrace wrist-worn epileptic seizure monitor and provide box plot results for eight interface indications. We also report summarised feedback from a heuristic analysis with fourteen participant evaluators. The results indicate some satisfaction with the minimal aesthetic of a simple light pattern interface as well as some concerns about confusion between different indications, accessibility and reliance on recall.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84461564","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}
Mariusz Mazurek, T. Rymarczyk, Konrad Kania, G. Kłosowski
The article presents a cyber-physical system for acquiring, processing and reconstructing images from measurement data. The technology is based on process tomography, intelligent sensors, machine learning, Big Data, Cloud Computing, as well as Internet of Things as a solution for industry 4.0. Industrial tomography allows observation of physical and chemical phenomena without the need for internal penetration, in a non-destructive way and allows monitoring of manufacturing processes in real time. The application contains a dedicated algorithm based on discrete cosine transformation to solve the inverse problem and a specialized intelligent system for tomographic measurements.
{"title":"Dedicated algorithm based on discrete cosine transform for the analysis of industrial processes using ultrasound tomography","authors":"Mariusz Mazurek, T. Rymarczyk, Konrad Kania, G. Kłosowski","doi":"10.1145/3410530.3414381","DOIUrl":"https://doi.org/10.1145/3410530.3414381","url":null,"abstract":"The article presents a cyber-physical system for acquiring, processing and reconstructing images from measurement data. The technology is based on process tomography, intelligent sensors, machine learning, Big Data, Cloud Computing, as well as Internet of Things as a solution for industry 4.0. Industrial tomography allows observation of physical and chemical phenomena without the need for internal penetration, in a non-destructive way and allows monitoring of manufacturing processes in real time. The application contains a dedicated algorithm based on discrete cosine transformation to solve the inverse problem and a specialized intelligent system for tomographic measurements.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75187555","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}
Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：