INDOOR LOCALISATION FOR DETECTING MEDICA-TION USE IN 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).

1. INTRODUCTION

Parkinson's disease (PD) is a neurodegenerative disorder that affects around six million people worldwide. It is a chronic disease with four main symptoms: tremor, bradykinesia (slowness of movement), rigidity, and postural instability Jankovic (2008) . Although PD is a slowly progressive disease, the symptoms can fluctuate hourly depending on medication intake timing, stress and other factors. These fluctuations make it challenging to capture the slow symptom progression in an individual (needed for example to measure how a new therapy changes disease progression) accurately over time using infrequent clinic or lab-based "snapshot" clinician-patient interactions. Also, to help a clinician understand the impact of PD on a patient, continuous monitoring is needed to give an accurate evaluation of how severe the symptoms and their fluctuations are for that individual. One method to determine whether sensor based monitoring can detect symptom fluctuations in PD is by evaluating the patients in an "OFF" medication state (i.e. when they have not taken their medication) since, when they withhold medications, patients tend to experience a worsening of symptoms. For example, when "OFF" medications, motor symptoms can become more severe which may hinder the subject's gait and movement around their own home. As a result, they may typically need more time to transition between rooms. Indoor localisation can be used to measure transitions between rooms in a home. Knowing how slow or fast a person transitions between rooms may be able to predict whether a person is in an "OFF" medication state or not and, longer-term, interpret their symptoms' severity (e.g. de novo freezing of gait in a hallway could indicate symptom progression) Bachlin et al. (2009) . Localisation can also add context in the measurement of other behaviours such as urinary function monitoring (e.g., how many times someone visits the bathroom overnight) which can impact the quality of life with PD He et al. (2016) . To perform indoor localisation in home environments, IoT-based platforms with multimodal sensors combined with machine learning can be used to provide an unobtrusive and continuous localisation. 1

