PDF(Pubmed)
Abstract：
With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30-60, 20-90, and 30-110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials.
摘要：
随着个性化医疗的推进，研究人员和临床医生已经开始探索使用可穿戴传感器来跟踪患者活动。这些传感器通常优先考虑设备寿命而不是强大的板载分析，这导致步数的准确性降低，尤其是在较低的节奏。为了优化活动监测设备的准确性，特别是在较慢的步行速度下，在人体验证试验之前,必须建立经过验证的方法,以可控和可重复的方式确定合适的设置.目前,在人工验证之前，没有优化这些低功耗可穿戴传感器设置的方法，这需要对实验室内参与者进行手动计数，并且受到时间和可以测试的节奏的限制。本文提出了一种新颖的方法，用于在人体验证试验之前通过使用产生连续步进的机械凸轮轴致动器来确定传感器步进计数精度。在可能的传感器设置组合的代表性子空间上以30步/分钟至110步/分钟的节奏识别传感器误差。然后使用这些真实误差来训练多变量多项式回归,以在所有可能的设置组合和步调上对误差进行建模。所得到的模型预测所有设置组合的R2为0.8和均方根误差(RMSE)为0.044的误差。然后使用优化算法来确定设置的组合，这些设置产生了代表残疾低机动性救护车的三个节奏范围的最低RMSE和中值误差，残疾人高机动性救护车，和健康的救护车(30-60、20-90和30-110步/分钟，分别)。该模型为每个感兴趣的范围识别了六个设置组合,其在人类验证之前实现了±10%的节奏误差。在较低的步行速度下，优化设置的预期误差范围低于可穿戴传感器的报告误差（±30％）。这表明传感器的人工验证前优化可以减少较低节奏的误差。该方法提供了一种新颖且有效的方法来在人体验证试验之前优化可穿戴活动监测器的准确性。
