帕金森病中的多模态室内定位用于检测药物使用：自由生活环境下的观察性试点研究
Multimodal Indoor Localisation in Parkinson's Disease for Detecting Medication Use: Observational Pilot Study in a Free-Living Setting
AI总结：
提出基于Transformer和RSSI、加速度计双模态的室内定位方法，利用智能家居自由生活数据集验证其在PD患者步态速度评估及左旋多巴药物服用检测中的有效性。
摘要：
帕金森病（PD）是一种缓慢进展、使人衰弱的神经退行性疾病，会导致包括步态功能障碍在内的运动症状。运动波动是指在左旋多巴治疗有积极反应的时期（“开”）与随着药效消退PD症状再次出现的时期（“off”）之间的交替变化。这些波动通常影响步态速度，并且随着PD的进展，其致残影响也会增加。为了提高现有室内定位方法的有效性，提出了一种基于Transformer的方法，该方法利用提供运动互补视图的双重模态，即可穿戴设备的接收信号强度指示（RSSI）和加速度计数据。一个子目标旨在评估室内定位（包括其家庭内步态速度特征，即在房间之间行走所需的时间）是否可用于评估运动波动，方法是通过检测PD患者是否正在服用左旋多巴药物或弃权（不执行）服用。为了正确评估我们提出的方法，我们使用了一个自由生活数据集，其中的运动和移动性如真实世界条件下预期的那样高度多变且非结构化。24名参与者以两人一组（由一名PD患者和一名对照者组成）的形式在一个配备各种传感器的智能家居中生活了五天。我们在生成的数据集上的评估表明，我们提出的网络在室内定位方面优于其他方法。子目标评估显示，精确的房间级定位预测被转换为家庭内步态速度特征后，能够准确预测PD参与者是否正在服用或弃权（不执行）服用其药物。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
