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Predicting Falls with Smartphone Accelerometers
We aim to develop an Android-based app that utilizes a developed machine learning model and accelerometer data collected via the user’s smartphone for fall detection.
Keywords: Time series analysis, biosignal analysis, data science, medical technologies, and digital health.
This study aims to develop an Android application with a machine-learning algorithm that has already been developed. The accelerometer data shall be collected using an Android mobile application for digital phenotyping. These algorithms could be used in rehabilitation and remote monitoring.
This study aims to develop an Android application with a machine-learning algorithm that has already been developed. The accelerometer data shall be collected using an Android mobile application for digital phenotyping. These algorithms could be used in rehabilitation and remote monitoring.
- Literature review (10%)
- Design and implement a multiclass machine learning classifier (20%)
- Design an android app (50%) Test, compare and evaluate results from different noise types (10%)
- Report and present results (10%)
**Your profile**
- Background in Computer Science, Biostatistics, or related fields
- Prior experience with programming (Matlab or Python) and Android apps
- 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%) - Design and implement a multiclass machine learning classifier (20%) - Design an android app (50%) Test, compare and evaluate results from different noise types (10%) - Report and present results (10%)
**Your profile**
- Background in Computer Science, Biostatistics, or related fields - Prior experience with programming (Matlab or Python) and Android apps - 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) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
Researchgate: https://www.researchgate.net/profile/Mohamed-Elgendi
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
