Non-contact real-time heartbeats and respiration monitoring using Artificial Light Texture (ALT).

Benchmark code for optimizers of bearing fault diagnosis. This code provides moduled features of data download, preprocessing, training, and logging.

"BRACU CSE360: Computer Interfacing" Laboratory Project - Fall 2020

Programmable smart device for smart homes, IoT applications, wearable devices, robotics and more...

IoT based patient health monitoring system using Arduino and generic ESP8266. The proposed project can collect and send patient’s health data.

An attempt to make the patient monitoring system easier given the doctor to patient ratio and unavailability of enough nursing staff

Utilize unsupervised learning to analyze health data, identify patient clusters, and provide personalized health insights for improved monitoring and management.

Patient Health Monitoring System Using ESP32

Visualize your raw data from .avro files for the EmbracePlus device from Empatica

HealthWatchful is a collection of C# libraries for comprehensive health checks and monitoring in .NET projects.

Health Monitoring System using ESP32 and MAX30100 sensor to measure the Heartrate and SpO2 levels in a person

🤖 AI-Powered Diet & Nutrition Assistant with OpenAI integration | Modern, responsive, and user-friendly health tracking system

Im Rahmen der Mental Health Surveillance (MHS) am Robert Koch-Institut (RKI) werden für eine Auswahl an Indikatoren der psychischen Gesundheit von Erwachsenen basierend auf Surveydaten Zeitreihen bestehend aus gleitenden Drei-Monats-Schätzern und Glättungskurven berechnet. Dadurch sollen Entwicklungen in der psychischen Gesundheit der erwachsene...

This project demonstrates a fault detection mechanism on a state machine using a server-client architecture .

Bringing Fitness tracker Analysis and Classification to action - A Case Study for identifying useful features responsible for healthy life. Using RStudio to create a RMarkdown

Retrieves FITBIT Data

HealthVitals is a Raspberry Pi-based system designed to monitor and analyze health vitals, such as heart rate and temperature. It detects abnormal readings, triggers alerts, and stores data for further analysis, ensuring users can maintain optimal health.

Ready to Use Advanced Industry level Spring Boot Tempelate that contains all the advanced configuration setup like Swagger, API caching, Correlation, Token based Autharization and Authentication, Various Filters, AOT based logging, etc

The repository is about sound based health monitoring of the industrial machines using machine learning.