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.

翻译：帕金森病是一种缓慢进展的、致残性神经退行性疾病，会导致包括步态功能障碍在内的运动症状。药物波动表现为左旋多巴治疗有效阶段（“开期”）与药物作用消退后帕金森病症状重新出现阶段（“关期”）之间的交替。这些波动常影响步态速度，并随疾病进展而加重致残影响。为提升现有室内定位方法的有效性，本文提出一种基于Transformer的双模态方法，利用可穿戴设备的接收信号强度指示（RSSI）和加速度计数据提供互补的运动视角。次要目标旨在评估室内定位（包括居家步态速度特征，如房间间行走时间）是否可通过检测帕金森病患者服用或停用左旋多巴药物来评估药物波动。为充分评估所提方法，我们采用自由生活数据集，其中受试者的运动和活动性高度多变且无结构化，符合真实世界条件。24名参与者成对居住（每组包括1名帕金森病患者和1名对照者），在配备多种传感器的智能家居中生活五天。对所得数据集的评估表明，我们的网络在室内定位任务上优于其他方法。次要目标评估显示，精确的房间级定位预测转化为居家步态速度特征后，能够准确预测帕金森病患者是否服药或停药。