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Activity and fatigue detection using machine learning based on real-world data from smart clothing
The aim of this project is to use machine learning methods to extract useful information such as activity type and fatigue level from real-world data acquired from our textile-based wearable technology during sport activities.
Sport monitoring has many benefits including injury prevention and performance optimization. Current methods for activity monitoring in sports mostly use camera-based motion tracking or the use of inertial measurement unit-based systems that are limited in their measurement space or are obtrusive to the activities of the user. Smart clothing offers a solution that makes monitoring of biomechanics during individual and team sports possible in different conditions.
We have made textile-based wearable technology for unobtrusive monitoring of movement and completed a study in which we acquired real-world data from our technology during sport activities. Using machine learning methods, this project aims to extract useful information from our data such as activity type and fatigue. This project will offer valuable experience in data processing, machine learning, working with real-world data, and cutting-edge wearable technologies.
**Your Profile**
- Background in electrical engineering, computer engineering, mechanical engineering, or related fields
- Prior experience with data analysis (signal processing, machine learning algorithms, or similar)
- Independent worker with critical thinking skills and problem solving skills
Sport monitoring has many benefits including injury prevention and performance optimization. Current methods for activity monitoring in sports mostly use camera-based motion tracking or the use of inertial measurement unit-based systems that are limited in their measurement space or are obtrusive to the activities of the user. Smart clothing offers a solution that makes monitoring of biomechanics during individual and team sports possible in different conditions.
We have made textile-based wearable technology for unobtrusive monitoring of movement and completed a study in which we acquired real-world data from our technology during sport activities. Using machine learning methods, this project aims to extract useful information from our data such as activity type and fatigue. This project will offer valuable experience in data processing, machine learning, working with real-world data, and cutting-edge wearable technologies.
**Your Profile**
- Background in electrical engineering, computer engineering, mechanical engineering, or related fields
- Prior experience with data analysis (signal processing, machine learning algorithms, or similar)
- Independent worker with critical thinking skills and problem solving skills
- Investigate methods to classify different sport-specific activities based on real-world data.
- Develop and test models to assess the level of fatigue during sports.
- Improve the model and compare it with other measurement methods.
- Investigate methods to classify different sport-specific activities based on real-world data. - Develop and test models to assess the level of fatigue during sports. - Improve the model and compare it with other measurement methods.
Prof Dr Carlo Menon and Dr Chakaveh Ahmadizadeh will supervise the student and the research will be performed at the Biomedical and Mobile Health Technology lab (www.bmht.ethz.ch) at ETH Zurich, Switzerland.
To apply, use the button below to tell us why you want to do this project ("motivation") along with the type of project you are applying for (e.g., master project or thesis) and your timeline for the project; attach a mini CV with your current program of study, your grades and any other info you deem relevant--maybe the name and phone number of a postdoc or a professor willing to be your reference; and make any further comments ("additional remarks").
Prof Dr Carlo Menon and Dr Chakaveh Ahmadizadeh will supervise the student and the research will be performed at the Biomedical and Mobile Health Technology lab (www.bmht.ethz.ch) at ETH Zurich, Switzerland.
To apply, use the button below to tell us why you want to do this project ("motivation") along with the type of project you are applying for (e.g., master project or thesis) and your timeline for the project; attach a mini CV with your current program of study, your grades and any other info you deem relevant--maybe the name and phone number of a postdoc or a professor willing to be your reference; and make any further comments ("additional remarks").
