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--- 2025-26:fall [2025/08/20 13:28] – sallin
+++ 2025-26:fall [2025/09/05 02:45] (current) – grau
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 ** Instructions:** Reach out to security faculty to see if they have the capacity to supervise this term.  For questions about eligible security projects, contact the CSec Coordinator (Yan Shvartzshnaider).
+----
+==== Analysis of gait data for health applications using machine learning ====
+** [added 2025-09-04] **
+** Course:** {EECS4080 | EECS4088 | EECS4090}
+**Supervisors:** Gerd Grau, Garrett Melenka
+**Supervisor's email address:** grau@yorku.ca, gmelenka@yorku.ca
+**Project Description:**
+//Project Motivation//
+Understanding human gait is crucial for diagnosing and managing conditions such as arthritis, where mobility is often impaired and subtle changes may indicate disease progression. With objective measurement and analysis of walking patterns, clinicians can tailor interventions and monitor the effectiveness of treatments more precisely. This project seeks to uncover relationships between gait data and restriction levels, paving the way for advanced health applications using modern machine learning techniques.
+//Project Overview//
+A comprehensive dataset of human gait has been collected from healthy subjects in various walking scenarios, with restricted braces simulating different levels of disease-related mobility impairment. The primary objective is to extract meaningful insights about gait characteristics and restriction levels from this dataset. The student will employ machine learning methods such as Long Short-Term Memory (LSTM) networks for time-domain analysis and Principal Component Analysis (PCA) for frequency-domain exploration.
+//Key Tasks and Responsibilities//
+• Preprocess and clean gait data collected under controlled walking conditions, including different restriction levels modelled by braces.
+• Apply time-domain and frequency-domain analysis techniques to identify distinctive features and trends related to restriction and gait quality.
+• Develop, train, and validate machine learning models (e.g., LSTM, PCA) to classify restriction levels and extract clinically relevant patterns.
+• Interpret model results and visualize findings to support health-related insights and recommendations for mobility assessment.
+• Document all steps of the analysis to ensure reproducibility and clarity for future research and clinical use.
+**Required skills or prerequisites:**
+•	Excellent programming skills (preferably in Python)
+•	Some knowledge of machine learning concepts and algorithms
+•	Interest in health applications and biomedical data analysis
+**Instructions:**
+Interested students should email Gerd Grau (grau@yorku.ca) and Garrett Melenka (gmelenka@yorku.ca) with:
+- CV
+- Latest transcript
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