2022 18th International Conference on Intelligent Environments (IE)最新文献

Individual Convolution of Ankle, Hip, and Wrist Data for Activities-of-Daily-Living Classification 日常生活活动分类中踝关节、髋关节和腕部数据的个体卷积

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826781

Lee B. Hinkle, V. Metsis

{"title":"Individual Convolution of Ankle, Hip, and Wrist Data for Activities-of-Daily-Living Classification","authors":"Lee B. Hinkle, V. Metsis","doi":"10.1109/ie54923.2022.9826781","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826781","url":null,"abstract":"The Activities of Daily Living (ADL) include activities such as brushing teeth, sweeping, and walking that are critical to on-going health, especially in older adults. Activities may be determined using recorded video and 2D-CNNs, however video recordings present privacy and coverage challenges in personal spaces. Smartphones and newer wristworn devices that record motion data can also be used for activity recognition tasks. Ankle or shoe-based devices such as the retired Nike+ sensor are less common, however ear-based devices which may record head movement are gaining popularity. In this work we use accelerometer data from a recently released dataset using devices placed on the ankle, hip, and wrist. First, we evaluate a simple 1D-CNNs ability to classify the 17 included activities in subject-dependent and subject-independent analysis. Then we process the accelerometer data from the three sensors individually to evaluate each location’s ability to predict activities. Finally, we develop a functional model which independently executes a 1D-CNN for each sensor’s data and combines the results using Global Average Pooling. The functional model achieves a subject-independent accuracy of 70.7%.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125580080","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

BoboLink: Low Latency and Low Power Communication for Intelligent Environments BoboLink:智能环境下的低延迟和低功耗通信

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826769

Mengyao Liu, J. Oostvogels, Sam Michiels, W. Joosen, D. Hughes

引用次数: 0

PowerJoular and JoularJX: Multi-Platform Software Power Monitoring Tools PowerJoular和JoularJX:多平台软件电源监控工具

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826760

Adel Noureddine

引用次数: 5

Vehicle Detection and Classification using Vibration Sensor and Machine Learning 基于振动传感器和机器学习的车辆检测与分类

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826783

Tomoki Okuro, Yumiko Nakayama, Yoshitada Takeshima, Yusuke Kondo, Nobuya Tachimori, M. Yoshida, Hiromu Yoshihara, H. Suwa, K. Yasumoto

{"title":"Vehicle Detection and Classification using Vibration Sensor and Machine Learning","authors":"Tomoki Okuro, Yumiko Nakayama, Yoshitada Takeshima, Yusuke Kondo, Nobuya Tachimori, M. Yoshida, Hiromu Yoshihara, H. Suwa, K. Yasumoto","doi":"10.1109/ie54923.2022.9826783","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826783","url":null,"abstract":"Road traffic censuses have been carried out manually for many years since measurements by machines were not widely spread due to the difficulty of installation. To solve installation difficulty, the size issues of the necessary equipment, and privacy issues of the existing traffic counter, we are conducting research and development of portable traffic counters using a vibration sensor and machine learning. However, vehicle type classification was not realized in the previous work, hence it was not possible to survey traffic volume by vehicle types. In addition, to the best of our knowledge, there is no existing study that can detect and classify vehicles based on road vibrations with a single sensor. In this paper, we propose a method of vehicle type classification that is capable of binary classification of small and large vehicles by machine learning combined with Support Vector Machine and Random Forest for vibrations of passing vehicles. We evaluated the proposed method by conducting measurements for up to 12 hours at two actual road locations. We tested over 5 hours of data and confirmed that small vehicles classified with the F-measure of 0.96 and large vehicles with the F-measure of 0.83.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116333140","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

On-the-Fly Spatio-Temporal Human Segmentation of 3D Point Cloud Data By Micro-Size LiDAR 基于微尺度激光雷达的三维点云数据的实时时空人体分割

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826758

Yuma Okochi, Hamada Rizk, H. Yamaguchi

{"title":"On-the-Fly Spatio-Temporal Human Segmentation of 3D Point Cloud Data By Micro-Size LiDAR","authors":"Yuma Okochi, Hamada Rizk, H. Yamaguchi","doi":"10.1109/ie54923.2022.9826758","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826758","url":null,"abstract":"The technology of 3D recognition is evolving rapidly, enabling unprecedented growth of applications towards human-centric intelligent environments. On top of these applications human segmentation is a key technology towards analyzing and understanding human mobility in those environments. However, existing segmentation techniques rely on deep learning models, which are computationally intensive and data-hungry solutions. This hinders their practical deployment on edge devices in realistic environments. In this paper, we introduce a novel micro-size LiDAR device for understanding human mobility in the surrounding environment. The device is supplied with an on-device lightweight human segmentation technique for the captured 3D point cloud data using density-based clustering. The proposed technique significantly reduces the computational complexity of the clustering algorithm by leveraging the Spatiotemporal relation between consecutive frames. We implemented and evaluated the proposed technique in a real-world environment. The results show that the proposed technique obtains a human segmentation accuracy of 99% with a drastic reduction of the processing time by 66%.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126595784","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: 7

Designing Socially Interactive, Robotic Environments through Pattern Languages 通过模式语言设计社会互动的机器人环境

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826761

Yixiao Wang, K. Green

{"title":"Designing Socially Interactive, Robotic Environments through Pattern Languages","authors":"Yixiao Wang, K. Green","doi":"10.1109/ie54923.2022.9826761","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826761","url":null,"abstract":"Architecture has long been conceptualized as “a machine for living in” and more recently as “a robot for living in.” Human-Robot Interaction (HRI) has developed robots as social agents—our friends, companions, and partners. Could robotic environments be perceived and interacted with as socially intelligent agents? If so, how should we design a Socially Interactive, Robotic Environment (SIRE)? To address the first question, we offer the empirical evidence and theoretical support of SIREs. We then address the second question by discussing the “Spatial Design” and “Interaction Design” of SIREs through an explorative, pattern-based approach. For “Spatial Design,” we present a co-design study for a partner-like office, generating new spatial patterns that form pattern languages to convey sociality to individual users. For “Interaction Design,” we employed four “Design Patterns for Sociality in HRI.” Our results show that “Spatial Patterns” and “HRI Patterns” can be integrated as one pattern language for sociality and that such a pattern language can vary from person to person. Through the explorative works of this paper, we wish to introduce SIRE to IE communities and cultivate the conversation about the design and application of SIREs in everyday life.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121937243","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

An Optical Soil Sensor for NPK Nutrient Detection in Smart Cities 用于智慧城市NPK养分检测的光学土壤传感器

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826759

Wei Fan, Kevin A. Kam, Haokai Zhao, P. Culligan, I. Kymissis

引用次数: 3

Distributed Power-Delivery Decision for a USB-PD-based Network 基于usb - pd网络的分布式供电决策

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826768

Yuusuke Kawakita, Kota Tamura, Yoshito Tobe, S. Yokogawa, H. Ichikawa

引用次数: 0

CellStory: Extendable Cellular Signals-Based Floor Estimator Using Deep Learning CellStory:基于深度学习的可扩展蜂窝信号底层估计器

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ie54923.2022.9826773

Asmaa Saeed, Ahmed Wasfey, Hamada Rizk, H. Yamaguchi

{"title":"CellStory: Extendable Cellular Signals-Based Floor Estimator Using Deep Learning","authors":"Asmaa Saeed, Ahmed Wasfey, Hamada Rizk, H. Yamaguchi","doi":"10.1109/ie54923.2022.9826773","DOIUrl":"https://doi.org/10.1109/ie54923.2022.9826773","url":null,"abstract":"As the demand for location-based services increases, several research efforts have aimed for robust and accurate indoor localization, especially 3D localization. Due to the widespread availability of cellular networks and their support by commodity cellphones, cellular-based systems have recently been proposed as a means of achieving this. However, because of the inherent noise and instability of wireless signals, localization accuracy typically degrades and is not robust to the dynamic heterogeneity of mobile devices.In this paper, we present a CellStory, a deep learning-based floor estimation system that achieves a fine-grained and robust accuracy in the presence of noise. CellStory combines stacked denoising autoencoder learning models, and a probabilistic framework to handle noise in the received signal and capture the complex relationship between the signals detected by the mobile phone and its location. Evaluation using different Android phones in a real testbed shows that CellStory can accurately estimate the user’s floor 98.7% of the time and within one floor error 100% of the time. This accuracy demonstrates CellStory’s superiority over state-of-the-art systems as well as its robustness to heterogeneous devices.","PeriodicalId":157754,"journal":{"name":"2022 18th International Conference on Intelligent Environments (IE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127631661","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

Program Commitee 程序委员会

2022 18th International Conference on Intelligent Environments (IE) Pub Date : 2022-06-20 DOI: 10.1109/ser-ip.2017..22

引用次数: 0