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
Yida Zhu, Haiyong Luo, Runze Chen, Fang Zhao, Li Su
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.
{"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}
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.
{"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}
S. S. Alia, P. Lago, Kohei Adachi, Tahera Hossain, H. Goto, Tsuyoshi Okita, Sozo Inoue
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.
{"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}
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.
随着交互式文本或语音系统(如智能个人助理和聊天机器人)的发展，现在可以通过基于对话的互动而不是传统的问卷调查来轻松测量用户的情绪。然而，目前尚不清楚这种情绪测量是否有效，类似于传统的测量方法，以及用户粘性。使用智能手机，我们在本文中比较了两种最流行的传统情绪测量方法:国际PANAS-Short Form (I-PANAS-SF)和Affect Grid。对于每一种测量方法，我们用一个改进的、基于聊天机器人的用户界面设计来研究情绪测量的有效性。我们的初步结果表明，一些情绪测量方法可能无法适应修改，它们的改变可能导致无效的结果，如果不是无意义的结果。这篇探索性论文随后提出并讨论了四种基于语音的情绪追踪器设计，并总结了用户对这些工具的感知和满意度。
{"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}
P. Widhalm, Philipp Merz, L. Coconu, Norbert Brändle
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%.
{"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}
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.
{"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}
Smriti Rani, A. Chowdhury, Andrew Gigie, T. Chakravarty, A. Pal
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.
{"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}
T. Okoshi, J. Nakazawa, JeongGil Ko, F. Kawsar, S. Pirttikangas
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.
{"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}
Pengfei Zhao, C. Gu, Zhidong Cao, Yue Xiang, Xiaohe Yan, Da Huo
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.
{"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}
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.
{"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}
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：