Register now After registration you will be able to apply for this opportunity online.
Machine Learning for Assessment of Walking Patterns in the SCI population
Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living. In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment.
Your work will guide future rehabilitation methods in general clinical practice, through applied classification and dimensionality reduction in Biomechanics of walking.
Goal: Develop an unsupervised clustering pipeline for a large dataset of gait patterns from spinal cord injured individuals for class similarity evaluation
Keywords: Medical and health science, computing and computational science, engineering and technology, information, machine learning, data science, data engineering
Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living.
In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment.
Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living. In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment.
• Analyse data quantity and quality through visualisation and graphical mapping of features.
• Develop a pipeline for unsupervised clustering through custom metrics.
• Evaluate unsupervised classification vs previously known classes.
• Prepare a results for explaining to a clinical team.
• Writing a report with an optional publication of the results.
• Analyse data quantity and quality through visualisation and graphical mapping of features. • Develop a pipeline for unsupervised clustering through custom metrics. • Evaluate unsupervised classification vs previously known classes. • Prepare a results for explaining to a clinical team. • Writing a report with an optional publication of the results.
- Gain unique access and first-hand experience in one of the leading institutions on long-term health management - At the Swiss Paraplegic Center at Nottwil.
- Develop a highly impactful project with direct application to clinical practice.
- Learn and practice unsupervised & supervised learning methods from time-series data.
- Gain unique access and first-hand experience in one of the leading institutions on long-term health management - At the Swiss Paraplegic Center at Nottwil. - Develop a highly impactful project with direct application to clinical practice. - Learn and practice unsupervised & supervised learning methods from time-series data.
- A fundamental understanding of machine learning methods.
- Strong understanding of statistics, clustering and unsupervised classification.
- Proven records on some of the following: longitudinal data analysis, sparse feature selection, otr deep learning (preferred).
- Knowledge of virtual environments (conda / docker)
- Strong experience with Python (preferred)
- Structured and reliable working style
- Ability to work independently on a challenging topic
- A fundamental understanding of machine learning methods. - Strong understanding of statistics, clustering and unsupervised classification. - Proven records on some of the following: longitudinal data analysis, sparse feature selection, otr deep learning (preferred). - Knowledge of virtual environments (conda / docker) - Strong experience with Python (preferred) - Structured and reliable working style - Ability to work independently on a challenging topic
Host: Dr. Diego Paez (SCAI Lab)
Prof. Robert Riener (SMS Lab)
Dr. Med. Inge Eriks (SPZ)
Please send your CV and latest transcript of records from my studies to Dr Diego Paez (diego.paez _at_ hest.ethz.ch)
Host: Dr. Diego Paez (SCAI Lab) Prof. Robert Riener (SMS Lab) Dr. Med. Inge Eriks (SPZ)
Please send your CV and latest transcript of records from my studies to Dr Diego Paez (diego.paez _at_ hest.ethz.ch)
