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Background: Objective assessment of shoulder joint active range of motion (AROM) is critical to monitor patient progress after conservative or surgical intervention. Advancements in miniature devices have led researchers to validate inertial sensors to capture human movement. This study investigated the construct validity as well as intra- and inter-rater reliability of active shoulder mobility measurements using a coupled system of inertial sensors and the Microsoft Kinect (HumanTrak). Methods: 50 healthy participants with no history of shoulder pathology were tested bilaterally for fixed and free ROM: (1) shoulder flexion, and (2) abduction using HumanTrak and goniometry. The repeat testing of the standardised protocol was completed after seven days by two physiotherapists. Results: All HumanTrak shoulder movements demonstrated adequate reliability (intra-class correlation (ICC) ≥ 0.70). HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.93 and 0.85) than goniometry (ICCs: 0.75 and 0.53) for measuring free shoulder flexion and abduction AROM, respectively. Similarly, HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.81 and 0.94) than goniometry (ICCs: 0.70 and 0.93) for fixed flexion and abduction AROM, respectively. Construct validity between HumanTrak and goniometry was adequate except for free abduction. The differences between raters were predominately acceptable and below ±10°. Conclusions: These results indicated that the HumanTrak system is an objective, valid and reliable way to assess and track shoulder ROM.

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In this paper, we present the design and implementation of a 3D digital phantom of the neonatal brain. Commonly used digital brain phantoms (e.g. BrainWeb) are based on adults' brains. With the increasing interest in computer aided analysis of neonatal Magnetic Resonance (MR) images, it becomes necessary to create a special digital phantom for neonatal brains. This is because of the pronounced differences not only in size but more important in geometrical proportions of different brain tissues in adults and neonates and the additional need to subdivide the white matter of neonatal brains into two different types. Thus, the here created neonatal brain phantom consists of 6 different tissue types: scalp, skull, gray matter, myelinated and non-myelinated white matter and cerebrospinal fluid. Every voxel has a vector consisting of 6 probabilities of being part of one of these six tissues. The digital brain phantom will be used for simulation of tomographic images of the newborns' head and may serve as well as an evaluation data set for comparison of analysis methods for neonatal MR images, e.g. segmentation/registration algorithms, providing the possibility of controlled degradation of image data.

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