Titlebook: Wireless Mobile Communication and Healthcare; 10th EAI Internation Xinbo Gao,Abbas Jamalipour,Lei Guo Conference proceedings 2022 ICST Inst

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A CNN-Based Computer Vision Interface for Prosthetics’ Controldaily life object in order to classify and recognize them. Such a classification provides useful information for the configuration of prosthetic and robotic hand: following the training, in fact, a low cost embedded computer combined with a low cost camera on the device (i.e. a prosthetic or robotic

Deduct

A Deep Learning-Based Dessert Recognition System for Automated Dietary Assessment Monitoring the food intake through self-report in diet control applications has been proven both time-consuming and non-practical and can be easily sidelined especially by children. In this paper, we propose the design and development of a novel system, which will assist obese or diabetic patients.

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Edge-Computing System Based on Smart Mat for Sleep Posture Recognition in IoMTstem based on a smart mat for sleep posture recognition in IoMT is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. To meet the requirements of embedded system in IoMT, a light-weight algorithm that includes pre-processing, EdgeNet pre-training, model qua

microscopic

Retinal Vessel Segmentation Using Multi-scale Generative Adversarial Network with Class Activation Mels in retinal images and generate some false-positive vessels. To alleviate this issue, we propose a multi-scale generative adversarial network with class activation mapping to achieve efficient segmentation. For the problem of small amount of data, we introduce a novel data augmentation method, wh

弹药

Robust Intent Classification Using Bayesian LSTM for Clinical Conversational Agents (CAs). CAs are currently being utilised for diverse clinical applications such as symptom checking, health monitoring, medical triage and diagnosis. Intent classification (IC) is an essential task of understanding user utterance in CAs which makes use of modern deep learning (DL) methods. Because of the

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Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depressionrted mental health symptom severity assessments. The effectiveness of predictive techniques to monitor depression is limited, given the idiosyncratic nature of depression symptoms and the limited availability of objectively labelled depression sensor-driven behaviour. In this paper, we investigate t