Gait Analysis and Identification

The goal of the project is to create a scalable end to end solution to identify and analyze an individual’s gait using inertial sensors found in modern smartphones. Inertial sensors such as accelerometer and gyroscope are often used to capture gait dynamics. Nowadays, these inertial sensors have commonly been integrated in smartphones and widely used by the average person, which makes it very convenient and inexpensive to collect gait data. Unlike traditional methods that makes use of motion capture and often requires the person to walk along a specified path in a controlled environment, the proposed method collects inertial gait time-series data without explicitly knowing when, where, and how the user walks. Due to the nature and size of walking data i.e. of high sink rate and big size, it will be processed on a distributed cluster of machines.

The project aims to provide a distributed computing solution for accumulating large amounts of time-series walking data and then applying machine learning algorithms for identification and analysis.

Source code →