  Parkinson's disease (PD) is a slowly progressive, debilitating
neurodegenerative disease which causes motor symptoms including gait
dysfunction. Motor fluctuations are alterations between periods with a positive
response to levodopa therapy ("on") and periods marked by re-emergency of PD
symptoms ("off") as the response to medication wears off. These fluctuations
often affect gait speed and they increase in their disabling impact as PD
progresses. To improve the effectiveness of current indoor localisation
methods, a transformer-based approach utilising dual modalities which provide
complementary views of movement, Received Signal Strength Indicator (RSSI) and
accelerometer data from wearable devices, is proposed. A sub-objective aims to
evaluate whether indoor localisation, including its in-home gait speed features
(i.e. the time taken to walk between rooms), could be used to evaluate motor
fluctuations by detecting whether the person with PD is taking levodopa
medications or withholding them. To properly evaluate our proposed method, we
use a free-living dataset where the movements and mobility are greatly varied
and unstructured as expected in real-world conditions. 24 participants lived in
pairs (consisting of one person with PD, one control) for five days in a smart
home with various sensors. Our evaluation on the resulting dataset demonstrates
that our proposed network outperforms other methods for indoor localisation.
The sub-objective evaluation shows that precise room-level localisation
predictions, transformed into in-home gait speed features, produce accurate
predictions on whether the PD participant is taking or withholding their
medications.
