Heart Disease Prediction App
This project will focus on predicting heart disease using neural networks. Based on attributes such as blood pressure, cholestoral levels, heart rate, and other characteristic attributes, patients will be classified according to varying degrees of coronary artery disease. This project will utilize a dataset of 303 patients and distributed by the UCI Deep Learning Repository.
keras Sensors medical heart disease
Epileptic Seizure Recognition App
A sudden rush of electric activity in the brain is called a seizure. Epilepsy is a chronic neurological disorder causing involuntory, recurrent seizures. Deep learning can be used to detect and monitor seizures in patients and IoT provides an ease of integration with the existing health system and patient wearability.
tinyML keras Sensors EEG signal
Human Activity Recognition App
Predict the activity being performed by a human being in real-time
accelerometer gyroscope TensorFlow Sensors CortexM4
Gesture Recognition App Using Muscle Activity
Electromyography is a technique for recording and evaluating electrical acitvity produced in the skeletal muscles. The readings from the muscle activity sensor is then fed to a model that can be trained on gestures, and can be used in various applications like prosthetic arms, game control etc.
tinyML keras Sensors health Electromyography
Mushroom Classification App
detect if the mushroom is edible or poisonous by its specifications like cap shape, cap color, gill color
keras Sensors food enviroment Mushroom
Heart Failure Prediction App
Characterized by the heart’s inability to pump an adequate supply of blood to the body. Without sufficient blood flow, all major body functions are disrupted. Heart failure is a condition or a collection of symptoms that weaken the heart
heart disease prediction Failure Stroke