Indoor Localisation for Detecting Medication Use in Parkinson's Disease

Ferdian Jovan, Catherine Morgan, Ryan McConville, Emma Tonkin, Alan Whone, Ian Craddock

Keywords: Transformer, Indoor Localisation, Medication State Classification, Parkinson's Disease

TL;DR: A transformer-based network is proposed for indoor localisation utilising dual modalities where the derived in-home mobility features can be used to classify the medication state of a person with Parkinson's disease

Abstract: Parkinson’s disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including its in-home mobility features, could provide a digital biomarker that can be used to quantify how mobility changes as this disease progresses. To improve the effectiveness of current methods for indoor localisation, a transformer-based approach utilising multiple modalities, Received Signal Strength Indicator (RSSI) and accelerometer data from wearable devices, which provide complementary views of movement, is proposed. 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. 12 pairs of people (one with PD, and the other a control participant) lived for five days in a smart home with various sensors. Our evaluation on such a dataset, which includes subjects with and without PD, demonstrates that our proposed network outperforms the current state-of-the-art in indoor localisation. We also show how the accurate room-level localisation predictions can be transformed into in-home mobility features (i.e. room-to-room transition duration) which can be used to effectively classify whether the PD participant is taking their medications or withholding them (increasing their symptoms)

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