Quality of Life (QoL) assessment uses of wareble data

Project Code :TCMAPY1762

Objective

This project develops a Quality of Life (QoL) assessment system using wearable sensor data and machine learning to classify activities and infer well-being states. The system processes data from 12 sensor inputs (accelerometer, gyroscope, gravity) using models like KNN, Random Forest, LightGBM, XGBoost, CNN, and LSTM. Built with Python (Flask) for the backend and HTML/CSS/JavaScript for the frontend, the application predicts activities and provides QoL insights from user-inputted sensor values, offering real-time, personalized health monitoring.

Abstract

This project presents a Quality of Life (QoL) assessment system leveraging wearable sensor data and machine learning models to classify human activities and infer potential well-being states. Utilizing the publicly available mobile health dataset from Kaggle, the system extracts features from 12 sensor inputs (accelerometer, gyroscope, and gravity in x, y, z axes). We trained and evaluated multiple models including K-Nearest Neighbors (KNN), Random Forest (RF), LightGBM (LGM), XGBoost (XGB), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to identify patterns linked to daily activities. The backend is powered by Python (Flask), while the frontend is built using HTML, CSS, and JavaScript. The user inputs sensor values via a simple web form, and the system outputs the predicted activity along with QoL interpretation. This lightweight, locally hosted application serves as a real-time monitoring tool for personalized health insights using wearable data.

Keywords:
Quality of Life (QoL), Wearable Sensors, Activity Recognition, Machine Learning, CNN, LSTM, Flask Web App, Mobile Health Dataset, Human Activity Classification, Real-time Monitoring.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.