Moreover, recognition of common human activity types like lying, sitting, standing, walking, running and biking from accelerometry data using multiple independent monitors has been addressed in several studies. The ability to combine measurements from multiple devices has received increased attention due to improved accuracy for the assessment of PA. Traditionally, the most frequent use is a single device worn at the waist or wrist. Objectively monitoring physical activity (PA) using activity monitors on a population-wide scale is an important element of public health surveillance and is increasingly being used in research due to concerns with inaccuracy and bias in self-reporting. Applying the algorithm to the n = 2513 samples worn for 7-days suggest a wide and substantial issue with drift over time when each subject wears two independent activity monitors. The results from the Bland–Altman analysis show good agreement between the proposed algorithm and the reference suggesting that the described method is valid for reducing the temporal misalignment and thus reduce the measurement error with aggregated data. The method was evaluated with free-living recordings using both combined wrist/hip ( n = 9) and thigh/hip device ( n = 30) wear locations, and descriptive data on initial offset and accumulated day 7 drift in a large-scale population-based study ( n = 2513) were calculated. In this study, a novel method not requiring human interaction is described for the temporal alignment of triaxial acceleration measured with two independent activity monitors and evaluating the performance with the misalignment manually identified.

However, a difference in the real time clock accuracy of the activity monitor introduces a substantial temporal misalignment with long duration recordings which is commonly not considered. Combining accelerometry from multiple independent activity monitors worn by the same subject have gained widespread interest with the assessment of physical activity behavior.

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