Abstract

Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.

Highlights

  • Digital innovations can promote and support an increase in physical activity and a reduction of sedentary time and thereby improve the health, well-being and participation of all individuals, according to the World Health Organization (WHO) [1]

  • For a healthy elderly population and a tracker worn on an upper leg location, validation of the optimized parameter settings showed good results for the fixed activity protocol (FAP) and the simulated free-living protocol

  • An adjustable physical activity classification algorithm that can discriminate between dynamic, standing, and sedentary behavior was transparently described

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Summary

Introduction

Digital innovations can promote and support an increase in physical activity and a reduction of sedentary time and thereby improve the health, well-being and participation of all individuals, according to the WHO [1]. E.g., accelerometers, they measure the movement of the body (segment) They estimate physical activity by applying application-specific algorithms to this raw data [9]. Both sensor and algorithm specifications are well described by the manufacturer or are available in literature. Due to the abundance of methods, protocols and measures of validity, combined with a lack of transparency on the algorithm methodology, it is difficult for researchers and clinicians to compare different physical activity trackers [9,10]. The validation procedure should be described transparently, include at least a simulated free-living protocol (which corresponds to the main daily activities), and should be performed by the target population [9,11].

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