DHEEPAK29/Project-ML-IoT
Objective : To help Doctors (or) Supervisors remotely monitor the Condition of the ambiance, Health condition, and the proximity from the set location of a Patient (or) Person under observation. This project imbibes the concept of the Internet of Things (IoT) and so the data is accessible seamlessly even if the supervisor is remote. Using range detection techniques and health parameters, Manipulations are done in the backend such that the alerts are notified based on set conditions to the respective person in case of emergency, we can conclusively predict the condition of a Patient (or) Person under observation remotely and accurately. Further, the data received from a patient is integrated into the database for analytics in Machine Learning (ML) to predict the reaction of another patient who suffers from the same disease or condition in the future. In addition, the product is feasible to be designed as a Handy and User-Friendly prototype, Cost-Efficient model, Less power-consuming mechanism, and Alterable Design.
This project helps doctors or supervisors remotely monitor patients, children, or elderly individuals who need constant supervision. It takes continuous health data (like heart rate, SpO2, temperature) and proximity information from wearable devices. It provides real-time alerts for critical health changes or if the person leaves a designated safe area, and can predict future patient reactions based on collected data. This is for medical professionals, caregivers, or parents monitoring vulnerable individuals.
No commits in the last 6 months.
Use this if you need a system to remotely track the health status and location of patients, children, or elderly individuals and receive immediate alerts for emergencies.
Not ideal if you require an FDA-approved medical device for life support or complex diagnostic analysis, as this project focuses on a monitoring and alert prototype.
Stars
9
Forks
1
Language
C++
License
—
Category
Last pushed
Apr 30, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/DHEEPAK29/Project-ML-IoT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DeepPSP/torch_ecg
Deep learning ECG models implemented using PyTorch
im-ethz/flirt
Are you ready to FLIRT with your wearable data?
Edoar-do/HuBERT-ECG
A self-supervised foundation ECG model for broad and scalable cardiac applications
bowang-lab/ecg-fm
An electrocardiogram analysis foundation model.
antonior92/automatic-ecg-diagnosis
Scripts and modules for training and testing neural network for ECG automatic classification....