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5页

Combining LSTM and CNN for mode of transportation classification from smartphone sensors 结合LSTM和CNN对智能手机传感器的交通方式进行分类

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: 10.1145/3410530.3414350

Björn Friedrich, Carolin Lübbe, A. Hein

引用次数: 10

A pragmatic signal processing approach for nurse care activity recognition using classical machine learning 一个实用的信号处理方法护理活动识别使用经典机器学习

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: 10.1145/3410530.3414337

Md. Ahasan Atick Faisal, Md. Sadman Siraj, Md. Tahmeed Abdullah, Omar Shahid, Farhan Fuad Abir, Md Atiqur Rahman Ahad

{"title":"A pragmatic signal processing approach for nurse care activity recognition using classical machine learning","authors":"Md. Ahasan Atick Faisal, Md. Sadman Siraj, Md. Tahmeed Abdullah, Omar Shahid, Farhan Fuad Abir, Md Atiqur Rahman Ahad","doi":"10.1145/3410530.3414337","DOIUrl":"https://doi.org/10.1145/3410530.3414337","url":null,"abstract":"Nursing activity recognition adds a new dimension to the healthcare automation system. But nursing activity recognition is very challenging than identifying simple human activities like walking, cycling, swimming, etc. due to intra-class variability between activities. Besides, the lack of proper dataset does not allow researchers to develop a generalized method for nursing activity or comparing baseline methods on different datasets. Nurse Care Activity Recognition Challenge 2020 provides a dataset of twelve nursing activities. In this paper, we have described our (Team Hex Code) approach where we have emphasized on developing method, which can cope up with real-world data with noise and uncertainty. In our method, we have resampled our data to deal with a variable sample frequency of dataset and we have also applied feature selection method on the extracted feature to have the best combination of feature set for classification. We have used random forest classifier which is a classical machine learning algorithm. Applying our methodology, we have got 78% validation accuracy on the dataset. We have trained our model on the lab dataset and validate them on the field dataset.","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":"32 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87665612","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: 5

Deep generative cross-modal on-body accelerometer data synthesis from videos 基于视频的深度生成跨模态车身加速度计数据合成

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: 10.1145/3410530.3414329

Shibo Zhang, N. Alshurafa

{"title":"Deep generative cross-modal on-body accelerometer data synthesis from videos","authors":"Shibo Zhang, N. Alshurafa","doi":"10.1145/3410530.3414329","DOIUrl":"https://doi.org/10.1145/3410530.3414329","url":null,"abstract":"Human activity recognition (HAR) based on wearable sensors has brought tremendous benefit to several industries ranging from healthcare to entertainment. However, to build reliable machine-learned models from wearables, labeled on-body sensor datasets obtained from real-world settings are needed. It is often prohibitively expensive to obtain large-scale, labeled on-body sensor datasets from real-world deployments. The lack of labeled datasets is a major obstacle in the wearable sensor-based activity recognition community. To overcome this problem, I aim to develop two deep generative cross-modal architectures to synthesize accelerometer data streams from video data streams. In the proposed approach, a conditional generative adversarial network (cGAN) is first used to generate sensor data conditioned on video data. Then, a conditional variational autoencoder (cVAE)-cGAN is proposed to further improve representation of the data. The effectiveness and efficacy of the proposed methods will be evaluated through two popular applications in HAR: eating recognition and physical activity recognition. Extensive experiments will be conducted on public sensor-based activity recognition datasets by building models with synthetic data and comparing the models against those trained from real sensor data. This work aims to expand labeled on-body sensor data, by generating synthetic on-body sensor data from video, which will equip the community with methods to transfer labels from video to on-body sensors.","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":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87781617","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: 15

5th international workshop on mental health and well-being: sensing and intervention 第五届心理健康和福祉:感知和干预国际讲习班

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: 10.1145/3410530.3414615

Varun Mishra, A. Sano, Saeed Abdullah, J. Bardram, S. Servia, Elizabeth L. Murnane, Tanzeem Choudhury, Mirco Musolesi, G. N. Vilaza, R. Nandakumar, Tauhidur Rahman

{"title":"5th international workshop on mental health and well-being: sensing and intervention","authors":"Varun Mishra, A. Sano, Saeed Abdullah, J. Bardram, S. Servia, Elizabeth L. Murnane, Tanzeem Choudhury, Mirco Musolesi, G. N. Vilaza, R. Nandakumar, Tauhidur Rahman","doi":"10.1145/3410530.3414615","DOIUrl":"https://doi.org/10.1145/3410530.3414615","url":null,"abstract":"Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing and tracking clinically relevant behaviors, contexts, and symptoms. Further, such systems can passively detect relapse onset and enable the opportune delivery of effective intervention strategies. However, despite their clear potential, the uptake of ubiquitous technologies into clinical mental healthcare is slow, and a number of challenges still face the overall efficacy of such technology-based solutions. The goal of this workshop is to bring together researchers interested in identifying, articulating, and addressing such issues and opportunities. Following the success of this workshop for the last four years, we aim to continue facilitating the UbiComp community in developing a holistic approach for sensing and intervention in the context of mental health.","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":"129 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80345507","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

Estimating symptoms caused by CIPN using mobile and wearable devices 使用移动和可穿戴设备估计CIPN引起的症状

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: 10.1145/3410530.3414435

Wataru Sasaki, Ryouya Ozawa, T. Okoshi, J. Nakazawa, K. Yagasaki, H. Komatsu

引用次数: 1

The light: exploring socially improvised movements using wearable sensors in a performative installation 光:在表演装置中使用可穿戴传感器探索社会即兴运动

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: 10.1145/3410530.3414378

Youhong Friendred Peng, Atau Tanaka, Jamie A. Ward

引用次数: 1

Occu-track: occupant presence sensing and trajectory detection using non-intrusive sensors in buildings 占用跟踪:使用建筑物中的非侵入式传感器的占用者存在感测和轨迹检测

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: 10.1145/3410530.3414597

Anooshmita Das, Emil Stubbe Kolvig Raun, Fisayo Caleb Sangogboye, M. Kjærgaard

{"title":"Occu-track: occupant presence sensing and trajectory detection using non-intrusive sensors in buildings","authors":"Anooshmita Das, Emil Stubbe Kolvig Raun, Fisayo Caleb Sangogboye, M. Kjærgaard","doi":"10.1145/3410530.3414597","DOIUrl":"https://doi.org/10.1145/3410530.3414597","url":null,"abstract":"Sensing occupant presence and their trajectories of movement in buildings enable new types of analysis and building operation strategies. However, obtaining such information in a cost-efficient and non-intrusive manner is a challenge. This paper proposes the Occu-track method for how inexpensive battery-powered sensors can be used at scale to estimate occupant presence and movement trajectories. The technique combines graph analysis and advanced clustering to produce accurate estimates. This paper validates the efficiency of Occu-track in two different settings; a music room and a private office. The experimental results from two room-level deployments demonstrate the benefits of the approach obtaining an average Root Mean Squared Error of 1.19 meters for case 1 and 0.88 meters for case 2 for trajectory estimation. The results can contribute to new dimensions of research associated with the generation of metadata from non-intrusive sensors to make informed decisions about efficient space utilization and floor plans, intelligent building operations, crowd management, comfortable indoor environment, or managing personnel.","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":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87895046","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

Scalable selection of EEG features for compression 可扩展的EEG特征压缩选择

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: 10.1145/3410530.3414438

Yuma Tsurugasaki, Koichi Shimoda, Michael Hefenbrock, Akihito Taya, Sejun Song, Y. Tobe

引用次数: 0

SCSV 2

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: 10.1145/3410530.3414586

Lixing He, C. Ruiz, Mostafa Mirshekari, Shijia Pan

{"title":"SCSV\u0000 2","authors":"Lixing He, C. Ruiz, Mostafa Mirshekari, Shijia Pan","doi":"10.1145/3410530.3414586","DOIUrl":"https://doi.org/10.1145/3410530.3414586","url":null,"abstract":"Structural vibration sensing has been explored to acquire indoor human information. This non-intrusive sensing modality enables various smart building applications such as long-term in-home elderly monitoring, ubiquitous gait analysis, etc. However, for applications that utilize multiple sensors to collaboratively infer this information (e.g., localization, activities of daily living recognition), the system configuration requires the location of the anchor sensor, which are usually acquired manually. This labor-intensive manual system configuration limited the scalability of the system. In this paper, we propose SCSV2, a self-configuration scheme to compute these vibration sensor locations utilizing shared context information acquired from complementary sensing modalities - vibration sensor itself and co-located cameras. SCSV2 combines 1) the physics models of wave propagation together with structural element effects and 2) the data-driven model from the multimodal data to infer the vibration sensor's location. We conducted real-world experiments to verify our proposed method and achieved an up to 7cm anchor sensor localization 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":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81715012","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

Ensemble approach for sensor-based human activity recognition 基于传感器的人体活动识别集成方法

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: 10.1145/3410530.3414352

Sunidhi Brajesh, Indraneel Ray

引用次数: 7