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|
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.