A Transparent Method for Step Detection using an Acceleration Threshold

Abstract

Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply do not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and non-ambulatory activities. Seventy-five adults (21-39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22-2.2ms-1. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their non-dominant wrist. Raw acceleration (g) signals from the anterior-posterior, medial-lateral, vertical, and vector magnitude (VM) directions were assessed separately for each device. Signals were demeaned across all activities and bandpass filtered [0.25, 2.5Hz]. Steps were detected via peak picking, with optimal thresholds (i.e., minimized absolute error from accumulated hand counted) determined by iterating minimum acceleration values to detect steps. Step counts were converted into cadence (steps/minute), and k-fold cross-validation quantified error (root mean squared error [RMSE]). We report optimal thresholds for use of either device on the waist (threshold=0.0267g) and wrist (threshold=0.0359g) using the VM signal. These thresholds yielded low error for the waist (RMSE<173 steps, ≤2.28 steps/minute) and wrist (RMSE<481 steps, ≤6.47 steps/minute) across all activities, and outperformed ActiLife's proprietary algorithm (RMSE=1312 and 2913 steps, 17.29 and 38.06 steps/minute for the waist and wrist, respectively). The thresholds reported herein provide a simple, transparent framework for step detection using accelerometers during treadmill ambulation and activities of daily living for waist- and wrist-worn locations.

Keywords: accelerometer; physical activity; physical activity monitor; step algorithm; wearable devices.

Figures

Figure 1:

Peak picking algorithm using time series acceleration signals. Peaks (red circles) were identified by definitive characteristics of the signal whereby the acceleration value is lower than the data point immediately preceding and following the point. Moreover, only peaks that occurred above a predefined value (i.e., acceleration threshold, represented here by the blue dashed horizontal line at an arbitrary value of y = 0.035 g) were accepted as peaks

Figure 2:

Bias (cadence error; steps/minute) values across activities for the waist (top) and wrist (bottom) using the SDT from the VM direction for the ActiGraph (red circles) and GENEActiv (blue triangles) acceleration signals compared to ActiGraph proprietary algorithm (green x’s). Data are mean ± CI95%.