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

TLDR

Physical behavior researchers rely on motion sensor acceleration summaries, but these depend on device specifications and proprietary algorithms, limiting cross‑study comparability and device flexibility. The study aims to develop an open‑source, universal acceleration summary metric that mitigates raw data discrepancies across research and consumer devices. Using signal‑processing techniques, the authors created a Monitor‑Independent Movement Summary (MIMS‑unit) by harmonizing raw signals, applying a 0.2–5.0 Hz bandpass filter, and aggregating data, then validated it on shaker tests and human data from eight accelerometers. MIMS‑units exhibited lower between‑device coefficient of variation than proprietary ActiGraph and ENMO summaries, were more sensitive to subtle wrist movements during sedentary behavior, and offer a device‑independent tool to standardize data cleaning and analysis across studies.

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.