title: A Method to Find Generic Thresholds for Identifying Relevant Physical Activity Events in Sensor Data.
abstract: The increasing use of wearable actimetry devices in cohort studies can provide a deep and objective insight in physical activity (PA) patterns. For reliable and reproducible pattern recognition, and to minimize the influence of specific device characteristics, there is a need for a generic method to identify relevant PA events in sensor data sets on the basis of comprehensive features such as PA duration and intensity. The objectives of this paper are to present a method to identify universal event detection thresholds for such parameters, and to attempt to find stable meta-clusters of PA behaviour. PA events of 5, 10, 20 and 30 min with low, medium and high intensity thresholds found in literature and intensity deciles were computed for a random sample (N = 100) of the NHANES 2005-06 accelerometer data set (N = 7457). On the basis of all combinations of the above, activity events were detected, and parameters mean duration, mean intensity and event regularity were computed. Results were clustered using x-Means clustering and visualized for 5-, 10-, 20-, and 30-min events. Stable clustering results are obtained with intensity thresholds up to the 8th decile and for event durations up to 10 min. Two stable meta-clusters were detected: 'irregularly active' (intensity at 52nd percentile) and 'regularly active' (intensity at 42nd percentile). Distinct generic thresholds could be identified and are proposed. They may prove useful for further investigations of similar actimetry data sets, minimising the influence of specific device characteristics. The results also confirm that distinct PA event patterns - including event regularity - can be identified using wearable sensor devices, especially when regarding low-intensity, short-term activities which do not correspond to current PA recommendations. Further research is necessary to evaluate actual associations between sensor-based PA parameters and health outcome. The author identified generic intensity and duration thresholds for analysing objective PA data from wearable devices. This may contribute to further analyses of PA patterns along with their relations with health outcome parameters.
authors.affiliation: Hanover Medical School, Peter L. Reichertz Institute for Medical Informatics, Carl-Neuberg-Str. 1, Hanover, 30625, Germany. Michael.Marschollek@plri.de.
authors.forename: Michael
authors.initials: M
authors.lastname: Marschollek
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D015444:Exercise; D006801:Humans; D009749:Nutrition Surveys; D010363:Pattern Recognition, Automated; D058998:Remote Sensing Technology / Q000295:instrumentation*; D013997:Time Factors
keywords: Accelerometry; Cohort studies; Pattern recognition; Physical activity