An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing.
An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing.
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DOI：
10.1123/jmpb.2018-0068
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发表时间：
2019-12
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影响因子：
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通讯作者：
Intille S
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文献类型：
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作者：
John D;Tang Q;Albinali F;Intille S
使用运动传感器的物理行为研究人员经常使用加速度摘要来可视化、清理和解释数据。此类输出取决于设备规格（例如动态范围、采样率）和/或是专有的，这使得使用不同设备时结果的交叉研究比较无效。这限制了选择测量身体活动、久坐行为和睡眠的设备的灵活性。开发一个开源的通用加速汇总指标，以解释研究和消费设备之间原始数据的差异。我们使用信号处理技术来生成一个独立于监视器的运动摘要单元（MIMS 单元），该单元经过优化以捕获正常的人体运动。方法步骤包括原始信号协调以消除设备间的变异性（例如动态 g 范围、采样率）、带通滤波 (0.2–5.0 Hz) 以消除非人类运动，以及信号聚合以减少数据以简化可视化和汇总。我们使用八个具有不同动态范围（±2 至 ±8 g）和采样率（20–100 Hz）的加速度计的轨道振动台测试以及来自 ActiGraph GT9X 的人体数据（N = 60）来检查 MIMS 单元的一致性。在振动台测试过程中，MIMS 装置产生的设备间变异系数低于专有的 ActiGraph 和 ENMO 加速汇总。与广泛使用的 ActiGraph 活动计数不同，MIMS 装置能够灵敏地检测久坐行为期间的细微手腕运动。开源 MIMS 单元可以提供一种以独立于设备的方式汇总高分辨率原始数据的方法，从而提高数据清理和分析程序的标准化，以估计跨研究的物理行为的选定属性。
Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2–5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20–100 Hz), and human data (N = 60) from an ActiGraph GT9X. During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.