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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

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DenseNetX and GRU for the sussex-huawei locomotion-transportation recognition challenge DenseNetX和GRU为sussexhuawei移动运输识别挑战
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414349
Yida Zhu, Haiyong Luo, Runze Chen, Fang Zhao, Li Su
{"title":"DenseNetX and GRU for the sussex-huawei locomotion-transportation recognition challenge","authors":"Yida Zhu, Haiyong Luo, Runze Chen, Fang Zhao, Li Su","doi":"10.1145/3410530.3414349","DOIUrl":"https://doi.org/10.1145/3410530.3414349","url":null,"abstract":"The Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge organized at the HASCA Workshop of UbiComp 2020 presents a large and realistic dataset with different activities and transportation. The goal of this human activity recognition challenge is to recognize eight modes of locomotion and transportation from 5-second frames of sensor data of a smartphone carried in the unknown position. In this paper, our team (We can fly) summarize our submission to the competition. We proposed a one-dimensional (1D) DenseNetX model, a deep learning method for transportation mode classification. We first convert sensor readings from the phone coordinate system to the navigation coordinate system. Then, we normalized each sensor using different maximums and minimums and construct multi-channel sensor input. Finally, 1D DenseNetX with the Gated Recurrent Unit (GRU) model output the predictions. In the experiment, we utilized four internal datasets for training our model and achieved averaged F1 score of 0.7848 on four valid datasets.","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":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77009143","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}
引用次数: 19
Identifying label noise in time-series datasets 识别时间序列数据集中的标签噪声
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414366
G. Atkinson, V. Metsis
{"title":"Identifying label noise in time-series datasets","authors":"G. Atkinson, V. Metsis","doi":"10.1145/3410530.3414366","DOIUrl":"https://doi.org/10.1145/3410530.3414366","url":null,"abstract":"Reliably labeled datasets are crucial to the performance of supervised learning methods. Time-series data pose additional challenges. Data points lying on borders between classes can be mislabeled due to perception limitations of human labelers. Sensor measurements may not be directly interpretable by humans. Thus label noise cannot be manually removed. As a result, time-series datasets often contain a significant amount of label noise that can degrade the performance of machine learning models. This work focuses on label noise identification and removal by extending previous methods developed for static instances to the domain of time-series data. We use a combination of deep learning and visualization algorithms to facilitate automatic noise removal. We show that our approach can identify mislabeled instances, which results in improved classification accuracy on four synthetic and two real publicly available human activity datasets.","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":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78862112","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}
引用次数: 8
Summary of the 2nd nurse care activity recognition challenge using lab and field data 利用实验室和现场数据总结第二次护士护理活动识别挑战
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414611
S. S. Alia, P. Lago, Kohei Adachi, Tahera Hossain, H. Goto, Tsuyoshi Okita, Sozo Inoue
{"title":"Summary of the 2nd nurse care activity recognition challenge using lab and field data","authors":"S. S. Alia, P. Lago, Kohei Adachi, Tahera Hossain, H. Goto, Tsuyoshi Okita, Sozo Inoue","doi":"10.1145/3410530.3414611","DOIUrl":"https://doi.org/10.1145/3410530.3414611","url":null,"abstract":"2nd Nurse Care Activity Recognition Challenge using Lab and Field Data is organized as a part of HASCA workshop and is continuation of Nurse Care Activity Recognition Challenge [7]. We give the description of the dataset and summarize the approaches used by the teams in this Challenge. In this challenge, data collected in both lab and real-world setting is provided to the challenge participants with an aim to bridge the gap between lab and practical field to reduce the workload of the nurses. The challenge was started on May 1, 2020 and continued until July 9, 2020. Accuracy is used as performance metric to evaluate the submissions. The winning team used k-NN classifier and achieved about 22.35% accuracy.","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":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74502661","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}
引用次数: 17
Exploring chatbot user interfaces for mood measurement: a study of validity and user experience 探索聊天机器人用户界面的情绪测量:有效性和用户体验的研究
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414395
Helma Torkamaan, J. Ziegler
{"title":"Exploring chatbot user interfaces for mood measurement: a study of validity and user experience","authors":"Helma Torkamaan, J. Ziegler","doi":"10.1145/3410530.3414395","DOIUrl":"https://doi.org/10.1145/3410530.3414395","url":null,"abstract":"With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user's mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools.","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":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79470998","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}
引用次数: 3
Tackling the SHL recognition challenge with phone position detection and nearest neighbour smoothing 利用手机位置检测和最近邻平滑来解决SHL识别难题
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414344
P. Widhalm, Philipp Merz, L. Coconu, Norbert Brändle
{"title":"Tackling the SHL recognition challenge with phone position detection and nearest neighbour smoothing","authors":"P. Widhalm, Philipp Merz, L. Coconu, Norbert Brändle","doi":"10.1145/3410530.3414344","DOIUrl":"https://doi.org/10.1145/3410530.3414344","url":null,"abstract":"We present the solution of team MDCA to the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2020. The task is to recognize the mode of transportation from 5-second frames of smartphone sensor data from two users, who wore the phone in a constant but unknown position. The training data were collected by a different user with four phones simultaneously worn at four different positions. Only a small labelled dataset from the two \"target\" users was provided. Our solution consists of three steps: 1) detecting the phone wearing position, 2) selecting training data to create a user and position-specific classification model, and 3) \"smoothing\" the predictions by identifying groups of similar data frames in the test set, which probably belong to the same class. We demonstrate the effectiveness of the processing pipeline by comparison to baseline models. Using 4-fold cross-validation our approach achieves an average F1 score of 75.3%.","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":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80865936","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}
引用次数: 4
Towards a wearable system for assessing couples' dyadic interactions in daily life 一个可穿戴系统，用于评估夫妻在日常生活中的二元互动
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414331
George Boateng
{"title":"Towards a wearable system for assessing couples' dyadic interactions in daily life","authors":"George Boateng","doi":"10.1145/3410530.3414331","DOIUrl":"https://doi.org/10.1145/3410530.3414331","url":null,"abstract":"Researchers are interested in understanding the dyadic interactions of couples as they relate to relationship quality and chronic disease management. Currently, ambulatory assessment of couples' interactions entail collecting data at random times in the day. There is no ubiquitous system that leverages the dyadic nature of couples' interactions (eg. collecting data when partners are interacting) and also performs real-time inference relevant for relationship quality and chronic disease management. In this work, we seek to develop a smartwatch system that can collect data about couples' dyadic interactions, and infer and track indicators of relationship quality and chronic disease management. We plan to collect data from couples in the field and use the data to develop methods to detect the indicators. Then, we plan to implement these methods as a smartwatch system and evaluate its performance in real-time and everyday life through another field study. Such a system can be used by social psychology researchers to understand the social dynamics of couples in everyday life and their impact on relationship quality, and also by health psychology researchers for developing and delivering behavioral interventions for couples who are managing chronic diseases.","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":"80887541","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}
引用次数: 1
Action recognition using spatially distributed radar setup through microdoppler signature 利用微多普勒特征的空间分布雷达装置进行动作识别
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414362
Smriti Rani, A. Chowdhury, Andrew Gigie, T. Chakravarty, A. Pal
{"title":"Action recognition using spatially distributed radar setup through microdoppler signature","authors":"Smriti Rani, A. Chowdhury, Andrew Gigie, T. Chakravarty, A. Pal","doi":"10.1145/3410530.3414362","DOIUrl":"https://doi.org/10.1145/3410530.3414362","url":null,"abstract":"Small form factor off-the shelf radar sensor nodes are being investigated for various privacy preserving non-contact sensing applications. This paper, presents a novel method, based on a system of spatially distributed radar setup(panel radar), for real time action recognition. Proposed method uses spatially distributed two single channel Continuous Wave (CW) radars to classify actions. For classification, a unique two layered classifier, is employed on novel features. Layer I performs coarse limb level classification followed by finer action detection in Layer II. For validation of the proposed system, 7 actions were targeted and data was collected for 20 people. Accuracy of 88.6 % was obtained, with a precision and recall of 0.9 and 0.89 respectively, hence proving the efficacy of this novel approach.","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":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80011979","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}
引用次数: 2
WellComp 2020: third international workshop on computing for well-being WellComp 2020:第三届健康计算国际研讨会
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414614
T. Okoshi, J. Nakazawa, JeongGil Ko, F. Kawsar, S. Pirttikangas
{"title":"WellComp 2020: third international workshop on computing for well-being","authors":"T. Okoshi, J. Nakazawa, JeongGil Ko, F. Kawsar, S. Pirttikangas","doi":"10.1145/3410530.3414614","DOIUrl":"https://doi.org/10.1145/3410530.3414614","url":null,"abstract":"With the advancements in ubiquitous computing, ubicomp technology has deeply spread into our daily lives, including office work, home and house-keeping, health management, transportation, or even urban living environments. Furthermore, beyond the initial metric of computing, such as \"efficiency\" and \"productivity\", the benefits that people (users) benefit on a well-being perspective based on such ubiquitous technology has been greatly paid attention in the recent years. In our third \"WellComp\" (Computing for Well-being) workshop, we intensively discuss about the contribution of ubiquitous computing towards users' well-being that covers physical, mental, and social wellness (and their combinations), from the viewpoints of various different layers of computing. After big success of two previous workshops WellComp 2018 and 2019, with strong international organization members in various ubicomp research domains, WellComp 2020 will bring together researchers and practitioners from the academia and industry to explore versatile topics related to well-being and ubiquitous computing.","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":"81577319","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}
引用次数: 0
A two-stage data-driven multi-energy management considering demand response 考虑需求响应的两阶段数据驱动多能源管理
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414587
Pengfei Zhao, C. Gu, Zhidong Cao, Yue Xiang, Xiaohe Yan, Da Huo
{"title":"A two-stage data-driven multi-energy management considering demand response","authors":"Pengfei Zhao, C. Gu, Zhidong Cao, Yue Xiang, Xiaohe Yan, Da Huo","doi":"10.1145/3410530.3414587","DOIUrl":"https://doi.org/10.1145/3410530.3414587","url":null,"abstract":"This paper proposes an innovative two-stage data-driven optimization framework for a multi-energy system. Enormous energy conversion technologies are incorporated in the system to enhance the overall energy utilization efficiency, i.e., combined heat and power, power-to-gas, gas furnace, and ground source heat pump. Furthermore, a demand response program is adopted for stimulating the load shift of customers. Accordingly, both the economic performance and system reliability can be improved. The endogenous solar generation brings about high uncertainty and variability, which affects the decision making of the system operator. Therefore, a two-stage data-driven distributionally robust optimization (TSDRO) method is utilized to capture the uncertainty. A tractable semidefinite programming reformulation is obtained based on the duality theory. Case studies are implemented to demonstrate the effectiveness of applying the TSDRO on energy management.","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":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89239406","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}
引用次数: 1
Where are you?: human activity recognition with smartphone sensor data 你在哪里?:利用智能手机传感器数据进行人体活动识别
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 Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414354
Gulustan Dogan, Iremnaz Cay, Sinem Sena Ertas, Seref Recep Keskin, Nouran Alotaibi, Elif Sahin
{"title":"Where are you?: human activity recognition with smartphone sensor data","authors":"Gulustan Dogan, Iremnaz Cay, Sinem Sena Ertas, Seref Recep Keskin, Nouran Alotaibi, Elif Sahin","doi":"10.1145/3410530.3414354","DOIUrl":"https://doi.org/10.1145/3410530.3414354","url":null,"abstract":"This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge.","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":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89758029","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}
引用次数: 7
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