Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease. Can machine learning techniques be used to detect such patterns in patients?
Associate Professor Florenc Demrozi is doing a research project that focuses on using machine learning to detect symptoms of Parkinson's disease, particularly motor symptoms. Motor symptoms significantly impact daily life, making it crucial to understand them better. One of the most challenging symptoms is "freezing of gait" (FoG), where a person suddenly stops and cannot move their feet, often triggered by distractions like a narrow hallway at home. These episodes can last from one to several seconds and can lead to falls, which are especially dangerous for vulnerable and elderly patients.
In such research direction, together with national and international partners, they are developing both new machine learning approaches and wearables devices able to detect FoG patterns and intervene on the users. This combination of algorithmic (i.e., machine learning) and electronics (i.e., wearables) has two main functions: predicting FoG two to three seconds before it happens and helping the person get out of the frozen state through stimulation. The goal is to automate this mechanism, but due to the subjective experience of FoG, it is challenging to create a universal system for all patients.
Currently, the researchers are collecting data; however, due to the high variability of FoG and motor symptoms among individuals with Parkinson's disease, obtaining sufficient and diverse datasets is challenging. While research in this field is ongoing, only a small number of studies provide open-access data, limiting the ability to develop and validate machine learning models effectively. To overcome this challenge, the team is leveraging zero-shot (ZSL) and few-shot (FSL) learning techniques, allowing models to recognize and classify FoG episodes even with minimal available training data. Both the software (AI models) and hardware (wearable devices) are being developed in-house by the research team. The next step is to establish an open collaboration with a hospital to test the AI system on new Parkinson’s patients and evaluate its effectiveness in detecting and predicting FoG episodes in real-world scenarios.