This repository contains the code and documentation for a research project on fall risk assessment using human gait analysis and deep learning.
Falls are a significant health concern, especially among older adults, leading to severe injuries and reduced quality of life. This project presents a novel approach for fall risk assessment based on human gait analysis using deep learning. We explore the use of an autoencoder network trained on inertial measurement unit (IMU) sensor data to detect abnormal gait patterns indicative of fall risk.
Collecting real-world gait data from older adults can be challenging due to ethical considerations, privacy concerns, and physical limitations. To address this, we propose the use of open-source datasets for model development and validation.
The project involves the following steps:
- Data Preprocessing: Filtering, segmentation, transformation, and augmentation of raw sensor data.
- Deep Learning Model: Training an autoencoder network on normal gait data to learn essential features.
- Model Optimization: Quantization and pruning to reduce model size and computational complexity for deployment on edge devices.
The trained autoencoder model achieved an accuracy of 0.86 in distinguishing between normal and abnormal gait patterns. The optimized model can be deployed on wearable sensors for real-time fall risk monitoring and timely interventions.
This project demonstrates the feasibility of using deep learning for fall risk assessment based on gait analysis. Future work will focus on collecting real-world data from older adults, refining the model using transfer learning, and integrating it into wearable devices for practical applications.
- code: Python scripts for data preprocessing, model training, and optimization.
- Datasets: Links to open-source datasets used in the project.
- Documentation: Detailed documentation of the methodology and results.