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Analysing force-time profiles of manual therapy interventions
We aim to develop a graphical user interface with an integrated machine learning model to analyze forces applied during manual therapy interventions (spinal manipulation). Data is collected with a flexible sensor matrix between the practitioner and the patient.
Keywords: Time series analysis, force-time profile analysis, data science, and medical technologies
The purpose of this study is to develop a graphical user interface with an integrated machine learning algorithm or model that can use force data from a sensor to identify and analyse the performed manual intervention. These algorithms could be of use for health clinical studies to help identify potential mechanisms of treatment effects and adverse events of manual interventions for back pain. The data has already been collected.
The purpose of this study is to develop a graphical user interface with an integrated machine learning algorithm or model that can use force data from a sensor to identify and analyse the performed manual intervention. These algorithms could be of use for health clinical studies to help identify potential mechanisms of treatment effects and adverse events of manual interventions for back pain. The data has already been collected.
- Literature review (10%)
- Data processing and analysis (loading and filtering data, extracting features, and basic statistical analysis) (30%)
- Design and implement a basic machine learning algorithm (20%)
- Testing and evaluation (10%)
- Design graphical user interface (20%)
- Report and present results (10%)
**Your profile**
- Background in Electrical Engineering, Biomedical Engineering, Computer Science, Biostatistics, or related fields
- Prior experience with programming (Matlab or Python)
- Able to work independently, pay attention to detail, and deliver results remotely
- Can visualize data effectively using different charts such as boxplots and scatter plots
- Background in statistics, time series analysis, and machine learning is needed.
- Literature review (10%) - Data processing and analysis (loading and filtering data, extracting features, and basic statistical analysis) (30%) - Design and implement a basic machine learning algorithm (20%) - Testing and evaluation (10%) - Design graphical user interface (20%) - Report and present results (10%)
**Your profile**
- Background in Electrical Engineering, Biomedical Engineering, Computer Science, Biostatistics, or related fields - Prior experience with programming (Matlab or Python) - Able to work independently, pay attention to detail, and deliver results remotely - Can visualize data effectively using different charts such as boxplots and scatter plots - Background in statistics, time series analysis, and machine learning is needed.
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch), Biomedical and Mobile Health Technology Lab, ETH Zurich’s D-HEST Department of Health Sciences and Technology, will supervise the student during this project in collaboration with Prof. Petra Schweinhardt (Head of Chiropractic Research).
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch), Biomedical and Mobile Health Technology Lab, ETH Zurich’s D-HEST Department of Health Sciences and Technology, will supervise the student during this project in collaboration with Prof. Petra Schweinhardt (Head of Chiropractic Research).
