Validation of open-source step-counting algorithms for wrist-worn tri-axial accelerometers in cardiovascular patients

Abstract

BackgroundAccurate quantification of daily steps in a cardiovascular patient population is of high importance for primary and secondary prevention. While sensor derived step counts have been sufficiently validated for hip-worn devices and commercial wrist-worn devices, there is a lack of knowledge on validity of freely available step counting algorithms for raw acceleration data collected at the wrist. Research questionHow accurate are step-counting algorithms for wrist worn tri-axial accelerometers in a cardiac rehabilitation training setting? MethodsTwo step counting algorithms (Windowed Peak Detection, Autocorrelation) for tri-axial accelerometers (Axivity AX-3), were tested. Steps were recorded by chest-mounted GoPro video cameras as gold standard. Cardiovascular patients without neurological impairments enrolled in an ambulatory rehabilitation program were recruited. Recordings were performed during one 45–90 min outdoor physical therapy session of which 5-min segments of six movement categories, namely Walking, Running, Nordic, Stairs, Arm Movement [AM] With [+] and Without [-] Walking [W] were identified and analyzed. Mean absolute difference and mean absolute percentage error [MAPE] with regard to true steps measured from video are reported to report accuracy. ResultsTraining sessions of 22 patients were recorded and analyzed. Steps were overestimated during AM-W and underestimated during Walking, Running and Stairs. Windowed Peak Detection algorithm was more accurate during AM+W and AM-W and Autocorrelation performed better during Nordic. A MAPE of close or below 10% was achieved by both algorithms for the categories: Walking, Running, Stairs and Nordic. SignificanceBoth algorithms provided accurate results for estimation of step counts in a controlled setting of a cardiovascular patient population. The quantification of daily number of steps recorded by wrist-worn accelerometers delivering raw data analyzed by freely available algorithms is a cost-effective option for research studies.