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_第9页

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

A multi-view architecture for the SHL challenge 针对SHL挑战的多视图体系结构

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: 10.1145/3410530.3414351

Massinissa Hamidi, A. Osmani, Pegah Alizadeh

引用次数: 6

Respiratory events screening using consumer smartwatches 使用消费者智能手表进行呼吸事件筛查

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: 10.1145/3410530.3414399

Illia Fedorin, Kostyantyn Slyusarenko, Margaryta Nastenko

引用次数: 4

Human activity recognition using multi-input CNN model with FFT spectrograms 基于FFT谱图的多输入CNN模型的人体活动识别

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: 10.1145/3410530.3414342

Keiichi Yaguchi, Kazukiyo Ikarigawa, R. Kawasaki, Wataru Miyazaki, Yuki Morikawa, Chihiro Ito, M. Shuzo, Eisaku Maeda

{"title":"Human activity recognition using multi-input CNN model with FFT spectrograms","authors":"Keiichi Yaguchi, Kazukiyo Ikarigawa, R. Kawasaki, Wataru Miyazaki, Yuki Morikawa, Chihiro Ito, M. Shuzo, Eisaku Maeda","doi":"10.1145/3410530.3414342","DOIUrl":"https://doi.org/10.1145/3410530.3414342","url":null,"abstract":"An activity recognition method developed by Team DSML-TDU for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge was descrived. Since the 2018 challenge, our team has been developing human activity recognition models based on a convolutional neural network (CNN) using Fast Fourier Transform (FFT) spectrograms from mobile sensors. In the 2020 challenge, we developed our model to fit various users equipped with sensors in specific positions. Nine modalities of FFT spectrograms generated from the three axes of the linear accelerometer, gyroscope, and magnetic sensor data were used as input data for our model. First, we created a CNN model to estimate four retention positions (Bag, Hand, Hips, and Torso) from the training data and validation data. The provided test data was expected to from Hips. Next, we created another (pre-trained) CNN model to estimate eight activities from a large amount of user 1 training data (Hips). Then, this model was fine-tuned for different users by using the small amount of validation data for users 2 and 3 (Hips). Finally, an F-measure of 96.7% was obtained as a result of 5-fold-cross validation.","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":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77467663","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: 11

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

引用次数: 2

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

引用次数: 8

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

引用次数: 3

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

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

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

引用次数: 4