Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing最新文献

DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning DeepEar:在不受约束的声学环境中使用深度学习实现强大的智能手机音频传感

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2804262

N. Lane, Petko Georgiev, Lorena Qendro

{"title":"DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning","authors":"N. Lane, Petko Georgiev, Lorena Qendro","doi":"10.1145/2750858.2804262","DOIUrl":"https://doi.org/10.1145/2750858.2804262","url":null,"abstract":"Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is highly susceptible to wild fluctuations in accuracy when used in diverse acoustic environments (such as, bedrooms, vehicles, or cafes), that users encounter on a daily basis. Towards addressing this challenge, we turn to the field of deep learning; an area of machine learning that has radically changed related audio modeling domains like speech recognition. In this paper, we present DeepEar -- the first mobile audio sensing framework built from coupled Deep Neural Networks (DNNs) that simultaneously perform common audio sensing tasks. We train DeepEar with a large-scale dataset including unlabeled data from 168 place visits. The resulting learned model, involving 2.3M parameters, enables DeepEar to significantly increase inference robustness to background noise beyond conventional approaches present in mobile devices. Finally, we show DeepEar is feasible for smartphones by building a cloud-free DSP-based prototype that runs continuously, using only 6% of the smartphone's battery daily.","PeriodicalId":167263,"journal":{"name":"Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115279711","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: 323

Drone & me: an exploration into natural human-drone interaction 无人机与我:探索人类与无人机的自然互动

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2805823

Jessica R. Cauchard, J. E, Kevin Y. Zhai, J. Landay

引用次数: 273

Improved activity recognition by using enriched acceleration data 通过使用丰富的加速度数据改进活动识别

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2805844

Isabel Suarez, Andreas Jahn, Christoph Anderson, K. David

{"title":"Improved activity recognition by using enriched acceleration data","authors":"Isabel Suarez, Andreas Jahn, Christoph Anderson, K. David","doi":"10.1145/2750858.2805844","DOIUrl":"https://doi.org/10.1145/2750858.2805844","url":null,"abstract":"Sensors embedded in smartphones are an essential component for activity recognition. Even though the accelerometer is the most widely used sensor, the highest recognition accuracies are obtained when using data collected from multiple sensors. However, the use of multiple sensors has an adverse impact on the energy consumption of power-limited devices such as smartphones. In this paper, we present a new method to improve the recognition accuracy of physical activities by using only the accelerometer. We utilize a low-pass filter to split the acceleration data into a low- and a high-frequency component. These components provide a new set of features, which can be used as a complement to the raw acceleration to reduce the number of sensors needed to recognize physical activities. After evaluating our method for a public dataset, we found that our approach represents an average of up to 16% increase in the recognition accuracy over the raw acceleration data, outperforming even widely used combinations such as the raw acceleration plus the gyroscope. The highest accuracies are obtained when using a cut-off frequency in the interval [0:001--0:05] Hz as well as a combination of the acceleration with its low-frequency component.","PeriodicalId":167263,"journal":{"name":"Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114600232","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: 26

Understanding usage states on mobile devices 了解移动设备的使用状态

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2805837

Chakajkla Jesdabodi, W. Maalej

引用次数: 39

One-button recognizer: exploiting button pressing behavior for user differentiation 一键识别器:利用按下按钮的行为来区分用户

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2804270

Henning Pohl, Markus Krause, M. Rohs

引用次数: 10

Why we use and abandon smart devices 为什么我们使用和放弃智能设备

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2804288

Amanda Lazar, Christian Koehler, Theresa Jean Tanenbaum, David H. Nguyen

引用次数: 344

MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones MyBehavior:根据用户使用智能手机的行为和偏好自动提供个性化的健康反馈

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2805840

Mashfiqui Rabbi, M. Aung, Mi Zhang, Tanzeem Choudhury

引用次数: 216

Vowel shapes: an open-source, interactive tool to assist singers with learning vowels 元音形状:一个开源的交互式工具，帮助歌手学习元音

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2805829

Cynthia Ryan, Katherine Ciesinski, Ehsan Hoque

引用次数: 0

Designing an emergency communication system for human and assistance dog partnerships 为人类和辅助犬的伙伴关系设计一个紧急通信系统

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2805849

Charlotte L. Robinson, C. Mancini, J. Linden, C. Guest, L. Swanson, Helen Marsden, Jose Valencia, Brendan Aengenheister

引用次数: 32

Analyzing visual attention during whole body interaction with public displays 分析全身与公共展示互动时的视觉注意

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2015-09-07 DOI: 10.1145/2750858.2804255

Robert Walter, A. Bulling, David Lindlbauer, M. Schuessler, Jörg Müller

引用次数: 12