scholarly journals Cattle Diseases Prediction using IOT and ML - A Review

2020 ◽  
Vol 9 (10) ◽  
pp. 169-173
Keyword(s):  
Health Monitoring ◽  
Learning Algorithm ◽  
Farm Animals ◽  
Monitoring Systems ◽  
Machine Learning Algorithm ◽  
Health Monitoring System ◽  
Cattle Diseases ◽  
Cattle Health ◽  
Current Monitoring ◽  
Livestock Diseases

Due to the everchanging environment, the cattle’s are in risk of getting affected by diseases and this in turn affects the economy. There will low productivity, less yield . It is still hard to forestall farm animals sicknesses using current monitoring systems that tune cattle activity and consequently the environmental situations of cattle .In this paper, we design a cattle health monitoring system using IOT and ML to a prevent livestock diseases, like anthrax disease, using dedicated sensors. We collect information using various. With the assistance of machine learning algorithm, we will predict the disease and send notification to the respective cattle owner and also the doctor in charge.

scholarly journals Non-Invasive Sensor Technology for the Development of a Dairy Cattle Health Monitoring System

Computers ◽  
2016 ◽  
Vol 5 (4) ◽  
pp. 23 ◽  
Author(s):  
Amruta Awasthi ◽  
Anshul Awasthi ◽  
Daniel Riordan ◽  
Joseph Walsh
Keyword(s):  
Dairy Cattle ◽  
Monitoring System ◽  
Health Monitoring ◽  
Sensor Technology ◽  
Health Monitoring System ◽  
Non Invasive ◽  
Cattle Health

scholarly journals Design of a Portable Health Monitoring System Based on Node MCU

2019 ◽  
Vol 9 (2) ◽  
pp. 957-960
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Public Hospital ◽  
Low Cost ◽  
Monitoring Systems ◽  
Electronic Components ◽  
Oxygen Saturation Level ◽  
Health Monitoring System ◽  
Health Monitoring Systems ◽  
Oled Display

In the present work, we have designed a health monitoring system based on Node MCU to monitor temperature, heart rate and oxygen saturation level (SpO2) signals, sensed by respective sensors. The necessary signal conditioning circuits have been designed in our laboratory using off-the shelf electronic components. A Data acquisition system has been designed using ESP 32 Node MCU. The designed system is a low-cost alternative to the commercially available USB controller based health monitoring systems. Firmware has been developed and deployed into the Node MCU using arduino IDE. The acquired data has been displayed on OLED display. The result shows maximum errors in the measured parameters within 2%. The designed system helps to achieve portability, high functionality and low cost which makes it an easy accessible tool for public, hospital, sports healthcare and other medical purposes.

A New Health Monitoring System for Cable-Stayed Bridges II: Applications

2011 ◽  
Vol 378-379 ◽  
pp. 328-331
Author(s):  
Ling Luo ◽  
Hong Luo ◽  
Bai Song Du
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Reference Value ◽  
Companion Paper ◽  
Monitoring Systems ◽  
Health Monitoring System ◽  
Bridge Health Monitoring ◽  
Cable Stayed Bridge ◽  
Four Levels ◽  
Cable Stayed Bridges

In the companion paper, a new health monitoring system with five sub-systems is proposed for cable-stayed bridges. In this paper, for a health monitoring system of the cable-stayed bridge, it is classified four levels as excellent, good, fair, and poor base on the function of the system at the first time. The monitoring systems of the second Wujiang Bridge, a cable-stayed bridge with a low tower and a high tower, and the Shibangou Yangtze River Bridge, a cable-stayed bridge with regular double towers, are employed as two examples to narrate the determination of the monitoring parameters, monitoring contents and methods, the principles of the positions of measuring points, and evaluation of the system grade. The health monitoring system for the cable-stayed bridge sets up a good example for other types of bridges and has a reference value for the development of the bridge health monitoring systems.

Direct Arrhythmia Classification from Compressive ECG Signals in Wearable Health Monitoring System

2018 ◽  
Vol 27 (06) ◽  
pp. 1850088 ◽  
Author(s):  
Jing Hua ◽  
Hua Zhang ◽  
Jizhong Liu ◽  
Yilu Xu ◽  
Fumin Guo
Keyword(s):  
Compressive Sensing ◽  
Monitoring System ◽  
Health Monitoring ◽  
Learning Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Health Monitoring System ◽  
Ecg Signals ◽  
Average Accuracy ◽  
Processing Signal

Due to the capacity of processing signal with low energy consumption, compressive sensing (CS) has been widely used in wearable health monitoring system for arrhythmia classification of electrocardiogram (ECG) signals. However, most existing works focus on compressive sensing reconstruction, in other words, the ECG signals must be reconstructed before use. Hence, these methods have high computational complexity. In this paper, the authors propose a cardiac arrhythmia classification scheme that performs classification task directly in the compressed domain, skipping the reconstruction stage. The proposed scheme first employs the Pan–Tompkins algorithm to preprocess the ECG signals, including denoising and QRS detection, and then compresses the ECG signals by CS to obtain the compressive measurements. The features are extracted directly from these measurements based on principal component analysis (PCA), and are used to classify the ECG signals into different types by the proposed semi-supervised learning algorithm based on support vector machine (SVM). Extensive simulations have been performed to validate the effectiveness of the proposed scheme. Experimental results have shown that the proposed scheme achieves an average accuracy of [Formula: see text] at a sensing rate of 0.7, compared to an accuracy of [Formula: see text] for noncompressive ECG data.

An Integrated Cattle Health Monitoring System

2006 ◽  
Author(s):  
Kevin Smith ◽  
Angel Martinez ◽  
Roland Craddolph ◽  
Howard Erickson ◽  
Daniel Andresen ◽  
...  
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Health Monitoring System ◽  
Cattle Health

scholarly journals Large Dynamic Current Monitoring for UAV Steering Motor Using Twin Nonlinear Shunt

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1240
Author(s):  
Bo Li ◽  
Shengbing Zhang ◽  
Hanlu Zhang ◽  
Shiyu Wang ◽  
Feng He
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Dynamic Range ◽  
Measurement Range ◽  
Industrial Control ◽  
Sampling Device ◽  
Health Monitoring System ◽  
Working Principle ◽  
Current Sampling ◽  
Current Monitoring

Steering motor is of vital importance in UAV’s health-monitoring system, to which its supply current is the most critical characteristic representing health statue of UAV. In order to conduct continuous measuring on the steering motor’s current of large dynamic range, in this paper, a current measurement method is therefore proposed on the basis of twin nonlinear shunt. The proposed method adopts the twin diode as the current sampling device, which not only realizes measurement range and relative constant resolution, but also ensures continuity of the measurement due to the eliminated operation of range switching. The associated diode is used to compensate the temperature of core temperature of the shunt diode, and to make the nonlinear-shunt more adaptive for the case of junction being heated under larger current. The working principle, real-time compensation method and circuit implementation of our method are discussed in detail. Experimental test results suggest that the measurement error of the proposed method is less than 4.5% when the measurement current varies between 10 mA to 10 A, maintaining the relative resolution at an almost constant level, while preventing the conventional method of frequent range switching from generating glitches. In addition to the ensured continuity, information-rich details of the current are sustained, contributing to the UAV’s health-monitoring system. The proposal can also be applied to other applications concerning large dynamic current detection, including, but not limited to, industrial control, motor control, etc.

An Integrated Cattle Health Monitoring System

2006 ◽  
Author(s):  
Kevin Smith ◽  
Angel Martinez ◽  
Roland Craddolph ◽  
Howard Erickson ◽  
Daniel Andresen ◽  
...  
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Health Monitoring System ◽  
Cattle Health
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