Gait analysis using wearable sensors in Parkinson's disease
| Title | Gait analysis using wearable sensors in Parkinson's disease |
|---|---|
| Summary | The project aims to develop a machine learning tool for the assessment of Parkinsonian gait in a natural environment |
| Keywords | Signal processing, Machine Learning, Parkinson's disease |
| TimeFrame | Autumn 2019 - Spring 2020 |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Taha Khan |
| Level | Master |
| Status | Open |
Gait impairment is an important symptom in Parkinson's disease. The project aims to investigate if wearables such as inertial sensors could be used to perform long-term continuous monitoring of Parkinsonian gait in a natural environment. Data consist of time-series of accelerometer readings and pressure insole sensors recorded from Parkinson patients during their assessment of gait by a doctor at a hospital in Johannesburg South Africa using a standard clinical protocol, as well as, during their walks around the clinic without the protocol in a natural setting. The project will explore and develop novel methods and gait features that could be extracted from the time-series and used for training machine learning models for automatic classification and monitoring of the severity of gait impairment in a natural environment.
