Motion analysis systems deliver quantitative information, e.g. on the progress of rehabilitation programs aimed at improving range of motion. Markerless systems are of interest for clinical application because they are low-cost and easy to use. The first generation of the Kinect™ sensor showed promising results in validity assessment compared to an established marker-based system. However, no literature is available on the validity of the new 'Kinect™ for Xbox one' (KinectOne) in tracking upper body motion. Consequently, this study was conducted to analyze the accuracy and reliability of the KinectOne in tracking upper body motion. Twenty subjects performed shoulder abduction in frontal and scapula plane, flexion, external rotation and horizontal flexion in two conditions (sitting and standing). Arm and trunk motion were analyzed using the KinectOne and compared to a marker-based system. Comparisons were made using Bland Altman statistics and Coefficient of Multiple Correlation. On average, differences between systems of 3.9±4.0° and 0.1±3.8° were found for arm and trunk motion, respectively. Correlation was higher for the arm than for the trunk motion. Based on the observed bias, the accuracy of the KinectOne was found to be adequate to measure arm motion in a clinical setting. Although trunk motion showed a very low absolute bias between the two systems, the KinectOne was not able to track small changes over time. Before the KinectOne can find clinical application, further research is required analyzing whether validity can be improved using a customized tracking algorithm or other sensor placement, and to analyze test-retest reliability.

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The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture classification to measure sedentary behavior. The aim of the present study was to develop an ActiGraph (waist‐worn, 30 Hz) posture classification to detect prolonged sitting bouts, and to compare the classification to proprietary ActiGraph data. The activPAL, a highly valid posture classification device, served as reference criterion. Both sensors were worn by 38 office workers over a median duration of 9 days. An automated feature selection extracted the relevant signal information for a minute‐based posture classification. The machine learning algorithm with optimal feature number to predict the time in prolonged sitting bouts (≥5 and ≥10 minutes) was searched and compared to the activPAL using Bland‐Altman statistics. The comparison included optimized and frequently used cut‐points (100 and 150 counts per minute (cpm), with and without low‐frequency‐extension (LFE) filtering). The new algorithm predicted the time in prolonged sitting bouts most accurate (bias ≤ 7 minutes/d). Of all proprietary ActiGraph methods, only 150 cpm without LFE predicted the time in prolonged sitting bouts non‐significantly different from the activPAL (bias ≤ 18 minutes/d). However, the frequently used 100 cpm with LFE accurately predicted total sitting time (bias ≤ 7 minutes/d). To study the health effects of ActiGraph measured prolonged sitting, we recommend using the new algorithm. In case a cut‐point is used, we recommend 150 cpm without LFE to measure prolonged sitting and 100 cpm with LFE to measure total sitting time. However, both cpm cut‐points are not recommended for a detailed bout analysis.

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