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Parkinson's disease (PD) is a slowly progressive debilitatingneurodegenerative disease which is prominently characterised by motor symptoms.Indoor localisation, including number and speed of room to room transitions,provides a proxy outcome which represents mobility and could be used as adigital biomarker to quantify how mobility changes as this disease progresses.We use data collected from 10 people with Parkinson's, and 10 controls, each ofwhom lived for five days in a smart home with various sensors. In order to moreeffectively localise them indoors, we propose a transformer-based approachutilizing two data modalities, Received Signal Strength Indicator (RSSI) andaccelerometer data from wearable devices, which provide complementary views ofmovement. Our approach makes asymmetric and dynamic correlations by a) learningtemporal correlations at different scales and levels, and b) utilizing variousgating mechanisms to select relevant features within modality and suppressunnecessary modalities. On a dataset with real patients, we demonstrate thatour proposed method gives an average accuracy of 89.9%, outperformingcompetitors. We also show that our model is able to better predict in-homemobility for people with Parkinson's with an average offset of 1.13 seconds toground truth.

Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers