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Parkinson's disease (PD) is a slowly progressive, debilitatingneurodegenerative disease which causes motor symptoms including gaitdysfunction. Motor fluctuations are alterations between periods with a positiveresponse to levodopa therapy ("on") and periods marked by re-emergency of PDsymptoms ("off") as the response to medication wears off. These fluctuationsoften affect gait speed and they increase in their disabling impact as PDprogresses. To improve the effectiveness of current indoor localisationmethods, a transformer-based approach utilising dual modalities which providecomplementary views of movement, Received Signal Strength Indicator (RSSI) andaccelerometer data from wearable devices, is proposed. A sub-objective aims toevaluate whether indoor localisation, including its in-home gait speed features(i.e. the time taken to walk between rooms), could be used to evaluate motorfluctuations by detecting whether the person with PD is taking levodopamedications or withholding them. To properly evaluate our proposed method, weuse a free-living dataset where the movements and mobility are greatly variedand unstructured as expected in real-world conditions. 24 participants lived inpairs (consisting of one person with PD, one control) for five days in a smarthome with various sensors. Our evaluation on the resulting dataset demonstratesthat our proposed network outperforms other methods for indoor localisation.The sub-objective evaluation shows that precise room-level localisationpredictions, transformed into in-home gait speed features, produce accuratepredictions on whether the PD participant is taking or withholding theirmedications.

Multimodal Indoor Localisation in Parkinson's Disease for Detecting Medication Use: Observational Pilot Study in a Free-Living Setting