scholarly journals Ubiquitous Health Management System with Watch-Type Monitoring Device for Dementia Patients

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Dongmin Shin ◽  
Dongil Shin ◽  
Dongkyoo Shin
Keyword(s):  
Data Analysis ◽  
Health Information ◽  
Health Management ◽  
Sensor Data ◽  
Analysis Algorithm ◽  
Smart Watch ◽  
Dementia Patients ◽  
Type Device ◽  
Fast Pace ◽  
Server System

For patients who have a senile mental disorder such as dementia, the quantity of exercise and amount of sunlight are an important clue for doses and treatment. Therefore, monitoring daily health information is necessary for patients’ safety and health. A portable and wearable sensor device and server configuration for monitoring data are needed to provide these services for patients. A watch-type device (smart watch) that patients wear and a server system are developed in this paper. The smart watch developed includes a GPS, accelerometer, and illumination sensor, and can obtain real time health information by measuring the position of patients, quantity of exercise, and amount of sunlight. The server system includes the sensor data analysis algorithm and web server used by the doctor and protector to monitor the sensor data acquired from the smart watch. The proposed data analysis algorithm acquires the exercise information and detects the step count in patients’ motion acquired from the acceleration sensor and verifies the three cases of fast pace, slow pace, and walking pace, showing 96% of the experimental results. If developed and the u-Healthcare System for dementia patients is applied, higher quality medical services can be provided to patients.

scholarly journals Student Physical Health Information Management Model under Big Data Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Wang ◽  
Ning Wang ◽  
MeiJie Li ◽  
Simeng Mi ◽  
YaYa Shi
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Physical Health ◽  
Health Information ◽  
Information Management ◽  
Health Management ◽  
Health Information Management ◽  
Management Model ◽  
Student Health ◽  
Management Service

Health is considered an important foundation for students’ success. However, with the accelerated pace of life, rising pressure from various parties, weak health awareness, lack of exercise time, and other reasons, students’ physical quality is generally declining, the incidence of health diseases is increasing, and the onset age tends to be younger. With the development of the concept of “health first,” health management continues to expand and extend and students’ health management has attracted more attention from many aspects. Due to the late and low starting point of health management research and the lack of professional theoretical support, a complete, mature, and effective health management service system has not been established to deal with the students’ health. In order to make student health management more scientific, normative, and effective, this article has proposed big data technology to build the student health information management model. The first step of the approach is to store and analyze the data of students’ physical health. It is necessary to combine the data collection, supervision, data analysis, and data application of students’ physical health and gradually improve the national monitoring and evaluation system of students’ physical health. Student health check-up management platform is mainly used in realizing the school student information management and student health information relationship between system, science, standardization, and automation, and its main task is to use a computer to perform daily management of all previous medical information of students, such as query, modify, add, delete, and enhance the physical health of students information management ability given the large data analysis of useful information. In addition, we have built a doctor recommendation model based on online questions and answers to give specific health recommendations for students of different physiques.

Exploring Internet and Media-mediated Health Information within the Context of Personal Health Management

2013 ◽  
Author(s):  
Shelagh K. Genuis
Keyword(s):  
Health Information ◽  
Information Literacy ◽  
Information Seeking ◽  
Health Management ◽  
The Internet ◽  
Personal Health ◽  
Étude Qualitative ◽  
Informal Information ◽  
Etude Qualitative

This qualitative paper explores how health information mediated by the internet and media is used and made valuable within the life of consumers managing non-crisis health challenges, and how informal information seeking and gathering influences self-positioning within patient-clinician relationships. Findings have implications for health information literacy and collaborative, patient-centred care.Cette étude qualitative explore comment l’information sur la santé relayée par Internet et les médias est utilisée et rendue utile dans le contexte de consommateurs gérant des problèmes médicaux non urgents, et comment la recherche et la collecte d’information informelles influencent l’auto-positionnement dans la relation patient clinicien. Les résultats ont des applications dans la maîtrise de l’information médicale et les soins collaboratifs centrés sur le patient.

Defect analysis of the same batch of substation equipment based on big data analysis algorithm

2021 ◽  
Vol 651 (2) ◽  
pp. 022093
Author(s):  
Qiang Gao ◽  
Chuan Zhong ◽  
Yong Wang ◽  
Peng Wang ◽  
Zaiming Yu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Defect Analysis ◽  
Analysis Algorithm ◽  
Substation Equipment

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

scholarly journals Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2884 ◽  
Author(s):  
Xiaobo Chen ◽  
Cheng Chen ◽  
Yingfeng Cai ◽  
Hai Wang ◽  
Qiaolin Ye
Keyword(s):  
Data Analysis ◽  
Sparse Representation ◽  
Intelligent Transportation Systems ◽  
Missing Values ◽  
Road Traffic ◽  
Linear Representation ◽  
Transportation Systems ◽  
Sensor Data ◽  
L1 Norm ◽  
L2 Norm

The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation of subsequent data analysis tasks since most analysis algorithms need complete data as input. In this work, a novel MVs imputation approach termed as kernel sparse representation with elastic net regularization (KSR-EN) is developed for reconstructing MVs to facilitate analysis with traffic sensor data. The idea is to represent each sample as a linear combination of other samples due to inherent spatiotemporal correlation, as well as periodicity of daily traffic flow. To discover few yet correlated samples and make full use of the valuable information, a combination of l1-norm and l2-norm is employed to penalize the combination coefficients. Moreover, the linear representation among samples is extended to nonlinear representation by mapping input data space into high-dimensional feature space, which further enhances the recovery performance of our proposed approach. An efficient iterative algorithm is developed for solving KSR-EN model. The proposed method is verified on both an artificially simulated dataset and a public road network traffic sensor data. The results demonstrate the effectiveness of the proposed approach in terms of MVs imputation.

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