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10页

A multi-person respiration monitoring system using COTS wifi devices 使用COTS wifi设备的多人呼吸监测系统

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: 10.1145/3410530.3414325

Youwei Zeng, Zhaopeng Liu, Dan Wu, Jinyi Liu, Jie Zhang, Daqing Zhang

{"title":"A multi-person respiration monitoring system using COTS wifi devices","authors":"Youwei Zeng, Zhaopeng Liu, Dan Wu, Jinyi Liu, Jie Zhang, Daqing Zhang","doi":"10.1145/3410530.3414325","DOIUrl":"https://doi.org/10.1145/3410530.3414325","url":null,"abstract":"In recent years, we have seen efforts made to simultaneously monitor the respiration of multiple persons based on the channel state information (CSI) retrieved from commodity WiFi devices. However, existing approaches only work when multiple persons exhibit dramatically different respiration rates and the performance degrades significantly when the targeted subjects have similar rates. What's more, they can only obtain the average respiration rate over a period of time and fail to capture the detailed rate change over time. These two constraints greatly limit the application of the proposed approaches in real life. Different from the existing approaches that apply spectral analysis to the CSI amplitude (or phase difference) to obtain respiration rate information, we leverage the multiple antennas provided by the commodity WiFi hardware and model the multi-person respiration sensing as a blind source separation (BSS) problem. Then, we solve it using independent component analysis (ICA) to obtain the reparation information of each person. In this demo, we will demonstrate MultiSense - a multi-person respiration monitoring system using COTS WiFi devices.","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":"24 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91505931","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

Improving activity data collection with on-device personalization using fine-tuning 使用微调改进设备上个性化的活动数据收集

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: 10.1145/3410530.3414370

Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue

{"title":"Improving activity data collection with on-device personalization using fine-tuning","authors":"Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue","doi":"10.1145/3410530.3414370","DOIUrl":"https://doi.org/10.1145/3410530.3414370","url":null,"abstract":"One of the biggest challenges of activity data collection is the unavoidability of relying on users and keep them engaged to provide labels consistently. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. In this study, we propose on-device personalization using fine-tuning convolutional neural networks as a mechanism in optimizing human effort in data labeling. First, we transfer the knowledge gained by on-cloud pre-training based on crowdsourced data to mobile devices. Second, we incrementally fine-tune a personalized model on every individual device using its locally accumulated input. Then, we utilize estimated activities customized according to the on-device model inference as feedback to motivate participants to improve data labeling. We conducted a verification study and gathered activity labels with smartphone sensors. Our preliminary evaluation results indicate that the proposed method outperformed the baseline method by approximately 8% regarding accuracy recognition.","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":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88509283","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

Feature based random forest nurse care activity recognition using accelerometer data 基于特征的随机森林护理活动识别，利用加速度计数据

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: 10.1145/3410530.3414340

Carolin Lübbe, Björn Friedrich, Sebastian J. F. Fudickar, S. Hellmers, A. Hein

引用次数: 4

Deep learning for cognitive load monitoring: a comparative evaluation 认知负荷监测的深度学习:比较评价

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: 10.1145/3410530.3414433

Andrea Salfinger

引用次数: 4

A physical knowledge-based extreme learning machine approach to fault diagnosis of rolling element bearing from small datasets 基于物理知识的极限学习机小数据集滚动轴承故障诊断方法

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: 10.1145/3410530.3414592

Tianyun Liu, Li Kou, Le Yang, Wenhui Fan, Cheng Wu

{"title":"A physical knowledge-based extreme learning machine approach to fault diagnosis of rolling element bearing from small datasets","authors":"Tianyun Liu, Li Kou, Le Yang, Wenhui Fan, Cheng Wu","doi":"10.1145/3410530.3414592","DOIUrl":"https://doi.org/10.1145/3410530.3414592","url":null,"abstract":"The learning-based methods have been widely applied to design a fault diagnosis model for rolling element bearing. However, the mainstream methods can only deal with the large training dataset, which is always violated in practical application. In this paper, we propose a physical knowledge-based hierarchical extreme learning machine(H-ELM) approach to adapt the problem of fault diagnosis for bearing with the small and imbalanced dataset. First, the proposed method uses the simple feature extraction algorithm to build a knowledge base for sample selection from the historical database, and the given training dataset is augmented with knowledge base. Second, a modified H-ELM algorithm is developed to identify fault location and recognize fault severity ranking based on the augmented dataset. Third, we design a self-optimizing module to optimize the sample selection and improve the performance of the H-ELM network. To evaluate the effectiveness of the proposed approach, the H-ELM without knowledge base and data augmentation-based support vector machine(SVM), back propagation neuron networks(BPNN) and deep belief networks(DBN) are tested in the numerical experiments to present a comprehensive comparison. The experimental results demonstrate that our approach outperforms in accuracy than other counterparts when dealing with the small and imbalanced 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":"2873 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86511062","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

Complex nurse care activity recognition using statistical features 基于统计特征的复杂护理活动识别

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: 10.1145/3410530.3414338

Promit Basak, Shahamat Mustavi Tasin, Malisha Islam Tapotee, Md. Mamun Sheikh, A. Sakib, Sriman Bidhan Baray, M. Ahad

{"title":"Complex nurse care activity recognition using statistical features","authors":"Promit Basak, Shahamat Mustavi Tasin, Malisha Islam Tapotee, Md. Mamun Sheikh, A. Sakib, Sriman Bidhan Baray, M. Ahad","doi":"10.1145/3410530.3414338","DOIUrl":"https://doi.org/10.1145/3410530.3414338","url":null,"abstract":"Human activity recognition has important applications in healthcare, human-computer interactions and other arenas. The direct interaction between the nurse and patient can play a pivotal role in healthcare. Recognizing various activities of nurses can improve healthcare in many ways. However, it is a very daunting task due to the complexities of the activities. \"The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data'' provides sensor-based accelerometer data to predict 12 activities conducted by the nurses in both the lab and real-life settings. The main difficulty of this dataset is to process the raw data because of a high imbalance among different classes. Besides, all activities have not been performed by all subjects. Our team, 'Team Apophis' has processed the data by filtering noise, applying windowing technique on time and frequency domain to extract various features from lab and field data distinctly. After merging lab and field data, the 10-fold cross-validation technique has been applied to find out the model of best performance. We have obtained a promising accuracy of 65% with an F1 score of 40% on this challenging dataset by using the Random Forest classifier.","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":"85157413","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: 6

Appliance fingerprinting using sound from power supply 利用电源发出的声音进行电器指纹识别

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: 10.1145/3410530.3414385

Lanqing Yang, Honglu Li, Zhaoxi Chen, Xiaoyu Ji, Yi-Chao Chen, Guangtao Xue, Chuang-Wen You

{"title":"Appliance fingerprinting using sound from power supply","authors":"Lanqing Yang, Honglu Li, Zhaoxi Chen, Xiaoyu Ji, Yi-Chao Chen, Guangtao Xue, Chuang-Wen You","doi":"10.1145/3410530.3414385","DOIUrl":"https://doi.org/10.1145/3410530.3414385","url":null,"abstract":"Recognizing the working appliances is of great importance for smart environment to provide services including energy conservation, user activity recognition, fire hazard prevention, etc. There have been many methods proposed to recognize appliances by analyzing the power voltage, current, electromagnetic emissions, vibration, light, and sound from appliances. Among these methods, measuring the power voltage and current requires installing intrusive sensors to each appliance. Measuring the electromagnetic emissions and vibration requires sensors to be attached or close (e.g., < 15cm) to the appliances. Methods relying on light are not universally applicable since only part of appliances generate light. Similarly, methods using sound relying on the sound from motor vibration or mechanical collision so are not applicable for many appliances. As a result, existing methods for appliance fingerprinting are intrusive, have high deployment cost, or only work for part of appliances. In this work, we proposed to use the inaudible high-frequency sound generated by the switching-mode power supply (SMPS) of the appliances as fingerprints to recognize appliances. Since SMPS is widely adopted in home appliances, the proposed method can work for most appliances. Our preliminary experiments on 18 household appliances (where 10 are of the same models) showed that the recognition accuracy achieves 97.6%.","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":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87639396","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

Facial expression based satisfaction index for empathic buildings 基于面部表情的共情建筑满意度指数

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: 10.1145/3410530.3414443

Fahad Sohrab, Jenni Raitoharju, M. Gabbouj

引用次数: 1

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