Personalized Health Monitoring Systems: Integrating Wearable and AI

The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

Applying Neural Networks for Tire Pressure Monitoring Systems

A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire.A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data.Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks(RNN-LSTM)and a convolutional neural network(CNN)developed in Python with Tensorflow.Bayesian Optimization via SigOpt was used to optimize training and model parameters.The predictive accuracy and training speed of the two models with various parameters are compared.Finally,future work and improvements are discussed.

STM32-based Health Monitoring System for Infants and Toddlers

In order to allow the guardians to monitor the physiological parameters of the infant more intuitively and to be able to respond to sudden irregularities in the pulse rate,abnormal blood oxygen,high or low body temperature and other conditions,and to facilitate communication with the medical staff or to request assistance in treatment,an STM32 microcontroller-based infant health monitoring system is designed.The digital signal acquisition module for pulse,blood oxygen and body temperature acquire the raw data,and the microcontroller performs algorithmic processing to display the physiological parameters such as pulse,blood oxygen and body temperature of the infant,and configures the threshold alarms for the physiological parameters by means of a keypad module.Finally,the test results are compared and tested against the standard physiological parameters of infants and children to verify that the system meets the requirements of medical precision and accuracy.

针刺联合Monitored Rehab Systems下肢智能运动训练系统对卒中偏瘫患者平衡及步行功能的影响

脑卒中是常见脑血管疾病,具有发病率高、致残率高等特点[1-2]。随着社会进步,医疗水平提升,脑卒中患者存活率增加,但大约有80%脑卒中患者会伴随运动功能障碍,其中以偏瘫为主,伴随步行能力下降、平衡协调功能障碍、运动能力下降等,严重影响患者日常生活,增加患者家庭及社会负担[3-4]。Monitored Rehab Systems为闭链训练方式,可训练患者肢体控制、肌肉协同及关节匹配度,常用于下肢功能训练。针刺是中医外治疗法。本研究旨在观察观察针刺联合Monitored Rehab Systems下肢智能运动训练系统对卒中偏瘫患者平衡及步行功能影响,报道如下。

Gas-dynamic phenomena caused by rock mass tremors and rock bursts

Similar to coal, rock and gas ejections, rock mass tremors and rock bursts are among the most serious natural hazards accompanying the underground extraction of coal. Gas-dynamic phenomena caused by rock mass tremors and rock bursts observed as transient states of air parameters in mining headings,are usually generated as a result of a change in the geometry of headings and the release of considerable amounts of gases. Particular significance is attributed to transient states caused by disasters, which are often accompanied by rapid incidents, presenting threats to the life and health of the underground crew.In Polish mining there are known examples of transient states of air parameters recorded during gasdynamic phenomena, e.g. tremors and rock bursts. The paper presents the case studies of rapid seismic incidents to show how records in mine monitoring systems broaden the knowledge about the transient states of air parameters in mining headings generated because of them.

Defect detection in freight railcar tapered-roller bearings using vibration techniques

Currently,there are two types of defect detection systems used to monitor the health of freight railcar bearings in service:wayside hot-box detection systems and trackside acoustic detection systems.These systems have proven to be inefficient in accurately determining bearing health,especially in the early stages of defect development.To that end,a prototype onboard bearing condition monitoring system has been developed and validated through extensive laboratory testing and a designated field test in 2015 at the Transportation Technology Center,Inc.in Pueblo,CO.The devised system can accurately and reliably characterize the health of bearings based on developed vibration thresholds and can identify defective taperedroller bearing components with defect areas smaller than 12.9 cm2 while in service.

Features of long-term health monitored strains of a bridge with wavelet analysis

This paper analyses the five years’ monitored strains collected from a long-term health monitoring system installed on a bridge with wavelet transform.In the analysis,the monitored strains are pre-processed,features of the monitored data are summarized briefly.The influences of the base functions on the results of wavelet analysis are studied simultaneously.The results show that the db wavelet is a good mother wavelet function in the analysis,and the order N should be larger than 20,but less than 46 in decomposing the monitored strains of the bridge.According to the strain variation features of concrete bridge,the proper decomposition level is 4 in the wavelet multi-resolution analysis.With the present method,the strains caused by random loads and daily sunlight can be accurately extracted from the monitored strains.The decomposed components of the monitored strains show that the amplitudes of the strains caused by random loads,daily sunlight,and annual temperature effect,are about 5 με,25 με,and 50 με respectively.The structural response under random load is smaller than the other parts.

Performance Modeling for Data Monitoring Services in Smart Grid: A Network Calculus Based Approach

This paper focuses on solving the modeling issues of monitoring system service performance based on the network calculus theory.First,we formulate the service model of the smart grid monitoring system.Then,we derive the flow arrival curve based on the incremental process related functions.Next,we develop flow arrival curves for the case of the incremental process being a fractional Gaussian process,and then we obtain the generalized Cauchy process.Three technical theorems related to network calculus are presented as our main results.Mathematically,the variance of arrival flow for the continuous time case is derived.Assuming that the incremental process of network flow is a Gaussian stationary process,and given the auto-correlation function of the incremental process with violation probability,the formula of the arrival curve is derived.In addition,the overall flow variance under the discrete time case is explicitly derived.The theoretical results are evaluated in smart grid applications.Simulations indicate that the generalized Cauchy process outperforms the fractional Gaussian process for our considered problem.