使用电话传感器和人工神经网络检测自然环境中饮酒期间的步态变化
Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment
摘要：
手机传感器可能有助于评估饮酒引起的步态变化。这项研究确定了（1）在自然环境中饮酒时收集步态相关数据的可行性，以及（2）手机传感器测量的步态相关特征与估计的血液酒精浓度（eBAC）的关系。10名年轻的成年重度饮酒者被要求在连续四个周末的晚上8点到凌晨12点之间每小时完成一项5步步态任务。我们从手机传感器收集了3轴加速度计、陀螺仪和磁力计数据，并使用滑动窗口技术计算了24个步态相关特征。基于酒精使用的生态瞬时评估（EMA）计算每个时间点的eBAC水平。我们使用人工神经网络模型来分析训练（70%的数据）和验证和测试（30%的数据）数据集中传感器特征与eBAC之间的关联。我们分析了128个数据点，其中eBAC和步态相关的传感器数据都被捕获，无论是在不喝酒时（n=60），而eBAC上升（n=55）或eBAC下降（n=13）。在eBAC大于法定限值（0.08 mg/dl）时采集了21个数据点。使用贝叶斯正则化神经网络，步态相关的手机传感器特征显示出与eBAC的高度相关性（Pearson's r > 0.9），并且>95%的估计eBAC将落在实际eBAC的-0.012和+0.012之间。在自然环境中的饮酒场合，从智能手机传感器收集步态相关数据是可行的。基于传感器的特征可用于推断与血液酒精含量升高相关的步态变化。
英文摘要：
Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to complete a 5-step gait task every hour from 8pm to 12am over four consecutive weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data from phone sensors, and computed 24 gait-related features using a sliding window technique. eBAC levels were calculated at each time point based on Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial neural network model to analyze associations between sensor features and eBACs in training (70% of the data) and validation and test (30% of the data) datasets. We analyzed 128 data points where both eBAC and gait-related sensor data was captured, either when not drinking (n=60), while eBAC was ascending (n=55) or eBAC was descending (n=13). 21 data points were captured at times when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian regularized neural network, gait-related phone sensor features showed a high correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect gait-related data from smartphone sensors during drinking occasions in the natural environment. Sensor-based features can be used to infer gait changes associated with elevated blood alcohol content.
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