使用时频和自回归隐马尔可夫模型方法使用可穿戴惯性传感器数据评估小脑疾病
Assessing Cerebellar Disorders With Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches
摘要：
我们使用自回归隐马尔可夫模型和时间-频率的方法来创建有意义的定量描述小脑共济失调的行为特征，从运动过程中收集的可穿戴惯性传感器数据。可穿戴传感器数据相对容易收集，并提供可用于开发有用的行为生物标志物的运动的直接测量。神经退行性疾病的敏感和特异性行为生物标志物对于支持早期检测，药物开发工作和靶向治疗至关重要。我们创建了一组灵活的描述性特征，这些特征来自于参与者执行临床评估任务时从可穿戴传感器收集的加速度计和陀螺仪数据，并利用这些数据估计疾病状态和严重程度。短时间的数据收集（$<$ 5分钟）产生足够的信息，以非常高的准确性有效地将共济失调患者与健康对照区分开，将共济失调与其他神经退行性疾病（如帕金森病）区分开，并估计疾病的严重程度。
英文摘要：
We use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors while participants perform clinical assessment tasks, and with them estimate disease status and severity. A short period of data collection ($<$ 5 minutes) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson's disease, and to give estimates of disease severity.
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