  Parkinson's disease (PD) is a slowly progressive debilitating
neurodegenerative 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 a
digital biomarker to quantify how mobility changes as this disease progresses.
We use data collected from 10 people with Parkinson's, and 10 controls, each of
whom lived for five days in a smart home with various sensors. In order to more
effectively localise them indoors, we propose a transformer-based approach
utilizing two data modalities, Received Signal Strength Indicator (RSSI) and
accelerometer data from wearable devices, which provide complementary views of
movement. Our approach makes asymmetric and dynamic correlations by a) learning
temporal correlations at different scales and levels, and b) utilizing various
gating mechanisms to select relevant features within modality and suppress
unnecessary modalities. On a dataset with real patients, we demonstrate that
our proposed method gives an average accuracy of 89.9%, outperforming
competitors. We also show that our model is able to better predict in-home
mobility for people with Parkinson's with an average offset of 1.13 seconds to
ground truth.
