... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks最新文献

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A Survey on Algorithmic Problems in Wireless Systems 无线系统中的算法问题综述

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2023-01-01 DOI: 10.5220/0011791200003399

Simon Thelen, Klaus Volbert, D. Nunes

引用次数: 0

Gateway Placement in LoRaWAN Enabled Sensor Networks 启用LoRaWAN的传感器网络中的网关放置

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2023-01-01 DOI: 10.5220/0011777000003399

Batuhan Can, Halit Uyanık, T. Ovatman

: This paper proposes two different approaches to be applied in gateway placement problem in LoRaWAN sensor networks. The ﬁrst approach is based on ﬁnding the minimal set to contain all the coverage intersections of the sensors and the second approach is based on optimization via integer programming over the distance between the gateways and sensors. Our results show that using automated gateway placement provides significantly less number of gateways to be used.

引用次数: 0

Triple Pi Sensing to Limit Spread of Infectious Diseases at Workplace 三Pi感应限制传染病在工作场所的传播

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2023-01-01 DOI: 10.5220/0011747400003399

J. Grabis, R. Pirta-Dreimane, Brigita Dejus, A. Borodinecs, Rolands Zaharovs

引用次数: 2

On the Path Towards Standardisation of a Sensor API for Forensics Investigations 关于法医调查传感器API标准化的道路

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2023-01-01 DOI: 10.5220/0011688300003399

Marco Manso, B. Guerra, Fernando Freire, R. Chirico, N. Liberatore, Renea Linder, Ulrike Schröder, Yusuf Yilmaz

引用次数: 0

SLAE6: Secure and Lightweight Authenticated Encryption Scheme for 6LoWPAN Networks SLAE6: 6LoWPAN网络的安全轻量级认证加密方案

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2023-01-01 DOI: 10.5220/0011632200003399

Fatma Foad Ashrif, Elankovan Sundarajan, Rami Ahmed, M Zahid Hasan

引用次数: 5

A Low-Cost Sensors Study Measuring Exposure to Particulate Matter in Mobility Situations 低成本传感器在移动环境中测量暴露于颗粒物的研究

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2023-01-01 DOI: 10.5220/0011747600003399

Marie-Laure Aix, Mélaine Claitte, D. Bicout

引用次数: 0

TSCH Slotframe Optimization Using Differential Evolution Algorithm for Heterogeneous Sensor Networks 基于差分进化算法的异构传感器网络TSCH槽帧优化

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2023-01-01 DOI: 10.5220/0011623400003399

Aida Vatankhah, R. Liscano, Tarana Ara

引用次数: 0

SmartAct: Energy Efficient and Real-Time Hand-to-Mouth Gesture Detection Using Wearable RGB-T. SmartAct:使用可穿戴RGB-T的节能实时手对嘴手势检测。

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2022-09-01 DOI: 10.1109/bsn56160.2022.9928492

Soroush Shahi, Mahdi Pedram, Glenn Fernandes, Nabil Alshurafa

Researchers have been leveraging wearable cameras to both visually confirm and automatically detect individuals' eating habits. However, energy-intensive tasks such as continuously collecting and storing RGB images in memory, or running algorithms in real-time to automate detection of eating, greatly impacts battery life. Since eating moments are spread sparsely throughout the day, battery life can be mitigated by recording and processing data only when there is a high likelihood of eating. We present a framework comprising a golf-ball sized wearable device using a low-powered thermal sensor array and real-time activation algorithm that activates high-energy tasks when a hand-to-mouth gesture is confirmed by the thermal sensor array. The high-energy tasks tested are turning on the RGB camera (Trigger RGB mode) and running inference on an on-device machine learning model (Trigger ML mode). Our experimental setup involved the design of a wearable camera, 6 participants collecting 18 hours of data with and without eating, the implementation of a feeding gesture detection algorithm on-device, and measures of power saving using our activation method. Our activation algorithm demonstrates an average of at-least 31.5% increase in battery life time, with minimal drop of recall (5%) and without impacting the accuracy of detecting eating (a slight 4.1% increase in F1-Score).

研究人员一直在利用可穿戴摄像头从视觉上确认和自动检测个人的饮食习惯。然而，诸如在内存中持续收集和存储RGB图像，或实时运行算法以自动检测进食等高能耗任务，会极大地影响电池寿命。由于一天中吃饭的时间很少，所以只有在很可能吃东西的时候才记录和处理数据，从而缩短电池寿命。我们提出了一个框架，包括一个高尔夫球大小的可穿戴设备，使用低功率热传感器阵列和实时激活算法，当热传感器阵列确认手对嘴的手势时，激活高能任务。测试的高能任务是打开RGB相机(触发RGB模式)和在设备上的机器学习模型(触发ML模式)上运行推理。我们的实验设置包括设计一个可穿戴相机，6名参与者在进食和不进食的情况下收集18小时的数据，在设备上实现进食手势检测算法，以及使用我们的激活方法节省电力的措施。我们的激活算法显示电池寿命平均至少增加31.5%，召回率最小(5%)，并且不影响检测饮食的准确性(F1-Score略有增加4.1%)。

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引用次数: 2

IoT Application for Monitoring and Storage of Temperature History in Electric Motors 物联网应用于电机温度历史的监测和存储

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2022-01-01 DOI: 10.5220/0010818300003118

Jairovan Denis de Paiva, Carlos Roberto Silveira Junior, Arquimedes Lopes da Silva

引用次数: 0

Semantic Segmentation of Retinal Blood Vessels from Fundus Images by using CNN and the Random Forest Algorithm 基于CNN和随机森林算法的眼底图像视网膜血管语义分割

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks

Pub Date : 2022-01-01 DOI: 10.5220/0010911800003118

Ayoub Skouta, Abdelali Elmoufidi, Said Jai-Andaloussi, O. Ouchetto

Abstract: In this paper, we present a new study to improve the automated segmentation of blood vessels in diabetic retinopathy images. Pre-processing is necessary due to the contrast between the blood vessels and the background, as well as the uneven illumination of the retinal images, in order to produce better quality data to be used in further processing. We use data augmentation techniques to increase the amount of accessible data in the dataset to overcome the data sparsity problem that deep learning requires. We then use the CNN VGG16 architecture to extract the feature from the preprocessed background images. The Random Forest method will then use the extracted attributes as input parameters. We used part of the augmented dataset to train the model (1764 images, representing the training set); the rest of the dataset will be used to test the model (196 images, representing the test set). Regarding the model validation phase, we used the dedicated part for testing the DRIVE dataset. Promising results compared to the state of the art were obtained. The method achieved an accuracy of 98.7%, a sensitivity of 97.4% and specificity of 99.5%. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

摘要:本文提出了一种改进糖尿病视网膜病变图像血管自动分割的新方法。由于血管和背景之间的对比度，以及视网膜图像的光照不均匀，预处理是必要的，以便产生更好质量的数据，用于进一步处理。我们使用数据增强技术来增加数据集中可访问数据的数量，以克服深度学习所需的数据稀疏性问题。然后，我们使用CNN VGG16架构从预处理的背景图像中提取特征。然后随机森林方法将使用提取的属性作为输入参数。我们使用增强数据集的一部分来训练模型(1764张图像，代表训练集);数据集的其余部分将用于测试模型(196张图像，代表测试集)。关于模型验证阶段，我们使用专用部分来测试DRIVE数据集。与目前的技术水平相比，取得了令人满意的结果。准确度为98.7%，灵敏度为97.4%，特异度为99.5%。与文献中最近的一些先前工作的比较显示了我们的建议的重大进步。

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引用次数: 4

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