Master of Science in Engineering (MSE)
Electrical and Computer Engineering
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Si Jung Kim
Fifth Committee Member
Number of Pages
Human gait classification and analysis become very important when a person has been diagnosed with a neurological disorder or has suffered an injury which has affected their ability to walk correctly. Gait strides are an important parameter to be studied as it helps the doctor to diagnose any underlying gait condition and evaluate what type of treatment suits the best for the patient’s recovery. Studying gait strides also helps athletes to improve their performance.In today’s world, machine learning has emerged as one of the most widely used technology for classification and analysis of gait characteristics. TinyML is a field of study in Machine Learning and Embedded Systems that uses the power of machine learning (ML) and runs these models on small, low-powered devices like microcontrollers. In this study, we delve into the world of TinyML and explore how the gait analysis can be carried out using this technology. The objective of this thesis is accomplished by using multiple sensors to collect gait stride patterns through a series of trials. We will be using STM’s SensorTile for collecting data. Three different types of stride motions are recorded for classification of short, long, and normal strides. We will use various machine learning/deep learning models to train the data and classify the stride lengths. Next, we’ll improve the model performance by using methods such as feature selection and tuning of various model parameters. On obtaining an optimized model, the model is translated to an embedded TinyML model for real-time evaluation. The classification and regression TinyML model are evaluations with ground truth values observed using a depth sense camera with skeleton tracking algorithms.
Gait in humans; Walking;
Computer Engineering | Electrical and Computer Engineering
University of Nevada, Las Vegas
Rajendra, Priyanka, "TinyML for Gait Stride Classification" (2021). UNLV Theses, Dissertations, Professional Papers, and Capstones. 4311.
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