Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity

Abstract

IntroductionHuman body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment.MethodsAn industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN+]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22–65 yr), and wrist in 63 women (20–35 yr) in whom daily activity-related energy expenditure (PAEE) was available.ResultsIn the robot experiment, HFEN+ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN).ConclusionIn conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity.

Cluster randomised controlled trial and economic and process evaluation to determine the effectiveness and cost-effectiveness of a novel intervention [Healthy Lifestyles Programme (HeLP)] to prevent obesity in school children

“…38 In these analyses, non-wear was determined if at least two accelerometer axes had a SD of < 13 mg and a range of < 50 mg over 60-minute windows, using moving increments of 15 minutes. 39 …”

“…Non-wear was determined as outlined previously in Study design, Outcome measures, Accelerometer measurements. 39 To minimise missing data due to non-wear, each 15-minute period of device non-wear time was replaced by the children's own data from the same time of day, averaged across all other recorded days. 48 This approach provided a person-specific method for imputing missing data.…”

“…Any time window with > 50% non-wear was treated as missing. 39 With the measures to minimise missing data put in place, the likely reasons for a child having missing outcome data at the 18-or 24-month follow-up were as follows.…”

Evaluation of haptic interfaces for simulation of drill vibration in virtual temporal bone surgery

“…Since gravity acceleration is included in the acceleration signals, first the gravity component was filtered by implementing a high pass 4th order Butterworth filter with a cut off frequency of 0.2 Hz [30]. This resulted in 3D acceleration signals without the gravity component.…”

Handheld pose tracking using vision-inertial sensors with occlusion handling

“…This topic is further studied in literature. 35 In case of Android mobile devices, a "gravity sensor" is embedded since API Level 9 (Android 2.3) was released. Thus, for this work we will not go further into details about the isolation of gravitational accelerations.…”