IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing

<p><strong>Abstract.</strong> This paper presents an innovative power line corridor inspection approach using UAV LiDAR edge computing and 4G real real-time transmission. First, sample point clouds of power towers are manually classified and decomposed into components according to five mainstream tower types: T type, V type, n type, I type and owl head type. A deep learning AI agent, named “Tovos Age Agent” internally, is trained by supervised deep learning the sample data sets under a 3D CNN framework. Second, laser points of power line corridors are simultaneously classified into Ground, Vegetation, Tower, Cable, and Building types using semantic feature constraints during the UAV-borne LiDAR acquisition process, and then tower types are further recognized by Tovos Agent for strain span separation. Spatial and topological relations between Cable points and other types are analyzed according to industry standards to identify potential risks at the same time. Finally, all potential risks are organized as industry standard reports and transmitted onto central server via 4G data link, so that maintenance personal can be notified the risks as soon as possible. Tests on LiDAR data of 1000&thinsp;KV power line show the promising results of the proposed method.</p>

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5G Edge Cloud Power Real-time Inspection Technology Based on YOLOV4-Tiny

Distributed Generation & Alternative Energy Journal ◽

10.13052/dgaej2156-3306.3643 ◽

2021 ◽

Author(s):

Jiaqi Song ◽

Jing Li ◽

Di Wu ◽

Guangye Li ◽

Jiaxin Zhang ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

High Efficiency ◽

Real Data ◽

Detection Algorithm ◽

Power Line ◽

Edge Computing ◽

Inspection Method ◽

Computing Architecture ◽

Low Efficiency

Power line corridor inspection plays a vital role in power system safe operation, traditional human inspection’s low efficiency makes the novel inspection method requiring high precision and high efficiency. Combined with the current deep learning target detection algorithm based on high accuracy and strong real-time performance, this paper proposes a YOLOV4-Tiny based drone real-time power line inspection method. The 5G and edge computing technology are combined properly forming a complete edge computing architecture. The UAV is treated as an edge device with a YOLOV4-Tiny deep- learning-based object detection model and AI chip on board. Extensive experiments on real data demonstrate the 5G and Edge computing architecture could satisfy the demands of real-time power inspection, and the intelligence of the whole inspection improved significantly.

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Live Demonstration: An IoT Wearable Device for Real-time Blood Glucose Prediction with Edge AI

10.1109/biocas49922.2021.9644975 ◽

2021 ◽

Author(s):

Lei Kuang ◽

Taiyu Zhu ◽

Kezhi Li ◽

John Daniels ◽

Pau Herrero ◽

...

Keyword(s):

Blood Glucose ◽

Real Time ◽

Wearable Device ◽

Live Demonstration ◽

Glucose Prediction

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TinyDL: Edge Computing and Deep Learning Based Real-time Hand Gesture Recognition Using Wearable Sensor

2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM) ◽

10.1109/healthcom49281.2021.9399005 ◽

2021 ◽

Author(s):

Brian Coffen ◽

Md.Shaad Mahmud

Keyword(s):

Deep Learning ◽

Real Time ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Edge Computing ◽

Hand Gesture ◽

Wearable Sensor

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An Edge Computing-Enabled Train Obstacle Detection Method Based on YOLOv3

Wireless Communications and Mobile Computing ◽

10.1155/2021/7670724 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Song Li ◽

Hongli Zhao ◽

Jinmin Ma

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Detection Method ◽

Obstacle Detection ◽

Edge Computing ◽

Traffic Load ◽

Learning Tasks ◽

Onboard Equipment

Rail transit is developing towards intelligence which takes lots of computation resource to perform deep learning tasks. Among these tasks, object detection is the most widely used, like track obstacle detection, catenary wear, and defect detection and looseness detection of train wheel bolts. But the limited computation capability of the train onboard equipment prevents running deep and complex detection networks. The limited computation capability of the train onboard equipment prevents conducting complex deep learning tasks. Cloud computing is widely utilized to make up for the insufficient onboard computation capability. However, the traditional cloud computing architecture will bring in uncertain heavy traffic load and cause high transmission delay, which makes it fail to complete real-time computing intensive tasks. As an extension of cloud computing, edge computing (EC) can reduce the pressure of cloud nodes by offloading workloads to edge nodes. In this paper, we propose an edge computing-based method. The onboard equipment on a fast-moving train is responsible for acquiring real-time images and completing a small part of the inference task. Edge computing is used to help execute the object detection algorithm on the trackside and carry most of the computing power. YOLOv3 is selected as the object detection model, since it can balance between the real-time and accurate performance on object detection compared with two-stage models. To save onboard equipment computation resources and realize the edge-train cooperative interface, we propose a model segmentation method based on the existing YOLOv3 model. We implement the cooperative inference scheme in real experiments and find that the proposed EC-based object detection method can accomplish real-time object detection tasks with little onboard computation resources.

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