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Fallup: Fall Risk Assessment and Prediction Based on Gait Analysis

Supervised By:
Imran Ihsan
2019 - 2020
Funded By:
Project completed in 100%.
Main Architecture of System
Accelerometer Data Patterns
Machine Learning Results
Wearable and Mobile App

Project description

A Fall Detection and Classification System

Fall-related injuries are the most common cause of accidental death in those over the age of 60, resulting in approximately 41 fall-related deaths per 100,000 people per year. Billions of dollars are cast-off for the cure of these fall injuries. Keeping this in mind fall detection has become a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. An effective fall detection system is needed to lessen this immense price and also the pain and agony the elderly bears due to fall injuries. For this purpose, we are contributing our part by creating a handy and efficient software induced hardware system that indents to give timely services to our subjects. Our project basically is a concept of Smart Environment that aims at providing ease to its users. In this paper, we have designed a Model to predict falls using the Na´ve Bayesian classifier. Three datasets are used for the research Smartfall, Farseeing and Mobile Dataset. Two of these were secondary datasets where as one dateset was collected by our own team member by attaching and android mobile device to the wrist. The data we required for processing falls is accelerometer sensor data, for this a 3-axial accelerometer is required for this research. However, as a cost-effective and a handy system is required so we propose the use of wrist wearable device as a medium to get data from. A protype android application is also created for making it clear how our fall detector is supposed to work in a real-time situation.

Tools and Technologies

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