Detailed analysis and comparison of different activity metrics

Abstract

Actigraphic measurements are an important part of research in different disciplines, yet the procedure of determining activity values is unexpectedly not standardized in the literature. Although the measured raw acceleration signal can be diversely processed, and then the activity values can be calculated by different activity calculation methods, the documentations of them are generally incomplete or vary by manufacturer. These numerous activity metrics may require different types of preprocessing of the acceleration signal. For example, digital filtering of the acceleration signals can have various parameters; moreover, both the filter and the activity metrics can also be applied per axis or on the magnitudes of the acceleration vector. Level crossing-based activity metrics also depend on threshold level values, yet the determination of their exact values is unclear as well. Due to the serious inconsistency of determining activity values, we created a detailed and comprehensive comparison of the different available activity calculation procedures because, up to the present, it was lacking in the literature. We assessed the different methods by analysing the triaxial acceleration signals measured during a 10-day movement of 42 subjects. We calculated 148 different activity signals for each subject’s movement using the combinations of various types of preprocessing and 7 different activity metrics applied on both axial and magnitude data. We determined the strength of the linear relationship between the metrics by correlation analysis, while we also examined the effects of the preprocessing steps. Moreover, we established that the standard deviation of the data series can be used as an appropriate, adaptive and generalized threshold level for the level intersection-based metrics. On the basis of these results, our work also serves as a general guide on how to proceed if one wants to determine activity from the raw acceleration data. All of the analysed raw acceleration signals are also publicly available.

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

“…In a previous paper [ 46 ], members of our research team presented a novel actigraphy sensor used to measure and collect the required data. The device was housed in a 3D-printed capsule measuring 41 mm × 16 mm × 11.3 mm (LWH) and weighing only 5.94 g, which contained a microcontroller (C8051F410), a 3-axis MEMS accelerometer (LIS3DH), a flash memory chip with a capacity of 1 GB, and a real-time quartz clock with an accuracy tolerance of +/−20 ppm.…”

“…The three axes of acceleration data collected in this way were then first subjected to a bandpass filtering procedure to filter out low-frequency noise (such as the Earth’s gravity) and high-frequency noise (which might be caused by vibrations, for example). The bandpass filter chosen was the maximally flat magnitude filter or Butterworth filter with cutoff frequencies of 0.25 and 2.5 Hz [ 46 ].…”

“…The actigraphic devices available on the market use different activity metrics, such as integration-based methods including Activity Count (AC) or Proportional Integration Method (PIM), threshold-based methods such as Zero Crossing Method (ZCM) or Time Above Threshold (TAT), and many others (AI, ENMO, MAD, etc.) [ 46 ]. For activity calculation, we have chosen the ZCM metric, which identifies activity based on the number of times a certain threshold is crossed by the acceleration signal.…”

“…An activity metric can be applied to the magnitude of the acceleration or separately to the axial components. However, the resulting activity data produced by the two approaches could differ significantly, in the case of the ZCM metric, this difference is negligible [ 46 ]. Therefore, for the ZCM activity calculation, the crossings counted on the three axes were summed into 60 s epochs.…”

“…Our analysis presented in this study is based on the measurement data, collected by Maczák et al [ 46 ], which are publicly available. The 42 10-day-long raw triaxial acceleration signals (measured on different healthy human subjects’ non-dominant wrists, sampled at 10 Hz in the ±8 g measurement interval) are downloadable from Figshare under CC-BY 4.0 license through the following DOI: 10.6084/m9.figshare.16437684.…”