Pfizer Inc., 610 Main Street, Cambridge, MA 02139, USA.
Sensors (Basel). 2022 Sep 1;22(17):6600. doi: 10.3390/s22176600.
Stair climb power (SCP) is a clinical measure of leg muscular function assessed in-clinic via the Stair Climb Power Test (SCPT). This method is subject to human error and cannot provide continuous remote monitoring. Continuous monitoring using wearable sensors may provide a more comprehensive assessment of lower-limb muscular function. In this work, we propose an algorithm to classify stair climbing periods and estimate SCP from a lower-back worn accelerometer, which strongly agrees with the clinical standard (r = 0.92, p < 0.001; ICC = 0.90, [0.82, 0.94]). Data were collected in-lab from healthy adults (n = 65) performing the four-step SCPT and a walking assessment while instrumented (accelerometer + gyroscope), which allowed us to investigate tradeoffs between sensor modalities. Using two classifiers, we were able to identify periods of stair ascent with >89% accuracy [sensitivity = >0.89, specificity = >0.90] using two ensemble machine learning algorithms, trained on accelerometer signal features. Minimal changes in model performances were observed using the gyroscope alone (±0−6% accuracy) versus the accelerometer model. While we observed a slight increase in accuracy when combining gyroscope and accelerometer (about +3−6% accuracy), this is tolerable to preserve battery life in the at-home environment. This work is impactful as it shows potential for an accelerometer-based at-home assessment of SCP.
楼梯爬升功率（SCP）是一种通过楼梯爬升功率测试（SCPT）在诊所评估的腿部肌肉功能的临床测量方法。这种方法容易出现人为错误，无法提供连续的远程监测。使用可穿戴传感器进行连续监测可能会更全面地评估下肢肌肉功能。在这项工作中，我们提出了一种从背部佩戴的加速度计中分类楼梯爬升阶段并估算 SCP 的算法，该算法与临床标准（r = 0.92，p < 0.001；ICC = 0.90，[0.82，0.94]）高度一致。数据是在实验室中从健康成年人（n = 65）在佩戴仪器（加速度计+陀螺仪）进行四步 SCPT 和步行评估时收集的，这使我们能够研究传感器模式之间的权衡。使用两种分类器，我们能够使用两种集成机器学习算法，基于加速度计信号特征，以>89%的准确率识别楼梯上升阶段[灵敏度>0.89，特异性>0.90]。仅使用陀螺仪时，模型性能的变化极小（准确性±0-6%），而与加速度计模型相比。当我们将陀螺仪和加速度计结合使用时，准确性略有提高（约+3-6%的准确性），这可以在家庭环境中保持电池寿命。这项工作具有影响力，因为它显示了基于加速度计的在家中进行 SCP 评估的潜力。
