An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing

Abstract

Background: 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.

Purpose: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: activity count; activity monitor; physical activity measurement.

Figure 1 —

Conceptual illustration depicting the steps involved in computing Monitor-Independent Movement Summary units (MIMS-units). The original signal (1 s in duration) is depicted in the top-left quadrant, which was collected using a wrist-worn GT9X from a participant who tapped the sensor with a finger and then performed the start of a jumping jack. Signals 2 to 4 were generated from the original signal to represent variability in raw signals due to differences in sampling rate and dynamic range, which is possible when using different devices. In Step 1, the signal is interpolated to 100 Hz. In Step 2, the signal is extrapolated (where needed: steps 2A and B), which involves estimating the edge of a maxed-out condition, estimating the extrapolated point to which the signal needs to be extended, and extending the signal to the extrapolated point (e.g., refer to oval inset in Step 2, signal 3 and to step 2B, signal). In Step 3, the signal is bandpass filtered to eliminate signal components that lie outside 0.2 to 5 Hz. In Steps 4 and 5, the signal is rectified and then integrated (area under the curve is computed for the rectified signal over a desired time-period), respectively, to obtain MIMS-units for that period. A more detailed description of each step can be found in the main body of the paper. In this example, Signal 4 yields comparably low MIMS-units/min due to the low sampling rate of the original signal.

Figure 2 —

(a) Average and variance of extrapolation rate for simulated sinusoidal signals cut off at ±2 g with different signal frequencies ranging from 1 to 8 Hz (1 Hz increments) with sampling rates from 20 to 100 Hz (10 Hz increments); (b) grid representations of extrapolation performance tested on simulated signals with amplitudes from ±2 to ±8 g and frequencies from 2 to 8 Hz at sampling rates of 20, 50, and 100 Hz (left to right); (c) extrapolation of a ±2 g (100 Hz sampling rate) signal during shaker testing (5 Hz); (d) running at 8.95 km/h (5.5 mph-ankle, 80 Hz sampling rate); (e) running at 12.1 km/h (7.5 mph-wrist, 40 Hz sampling rate); (f) and jumping jacks (wrist, 100 Hz sampling rate). In C–F, red, blue, and grey lines indicate the cut-off ±2 g signal, the extrapolated signal, and a ground-truth ±8 g signal, respectively. Note. Extrap. = extrapolated signal; smp. rt. = sampling rate.

Figure 3 —

(a) Coefficient of variation among acceleration summaries (i.e., Monitor-Independent Movement Summary units [MIMS-units] algorithm involving all steps outlined in Figure 1, MIMS-units algorithm without the extrapolation step, MIMS-units algorithm with a narrower passband of 0.25–2.5 Hz, ActiGraph counts, and UK Biobank Euclidean Norm Minus One-based summary), obtained from eight different acceleration sensors (each with different sampling rates and g range) at different frequencies during shaker testing; (b) MIMS-units from different devices during the shaker testing protocol. Numerical values in graph indicate peak g-forces generated during shaker testing.

Figure 4 —

ActiGraph counts, Monitor-Independent Movement Summary units (MIMS-units), and UK Biobank Euclidean Norm Minus One (ENMO) output from a GT9X worn on the hip and wrist during the treadmill ambulation protocol. MIMS-units and UK Biobank ENMO are derived from both an ±8 and a ±2 g signal. Horizontal panels show patterns in different output types for the same wear location. Y-axes of graphs in the two horizontal panels are scaled to minimize visual bias that may arise due to differences in the units of the three different types of outputs.

Figure 5 —

Boxplots for (a) total ActiGraph activity counts derived from an ±8 g signal, (b) total Monitor-Independent Movement Summary units (MIMS-units) derived, and (c) average UK Biobank Euclidean Norm Minus One (ENMO) output for the hip and wrist acceleration for various activities. MIMS-units and UK Biobank ENMO are derived from both an ±8 and a ±2 g signal. Activities are arranged in order of increasing MET values based on representative activities from the Compendium of Physical Activities. * indicates significant intra-location difference in outputs derived using a ±2 and ±8 g signal. Note: Sitting, standing, and walking at 4.8 and 5.6 km/h include grouped activities in the same posture or speed (Supplementary Table 3 [available online]).

Figure 6 —

(a) Boxplot for selected activities where at least one participant returned zero total ActiGraph activity counts derived from a ±8 g signal; (b) and (c) corresponding total Monitor-Independent Movement Summary units and average UK Biobank Euclidean Norm Minus One output (mg) derived from an ±8 and ±2 g signal. Note. H = hip; W = wrist; Cyc. Erg. = cycle ergometry at 300 kpm/m; Out. Cyc. = outdoor cycling; AG = ActiGraph.