The use of consumer depth cameras for calculating body segment parameters.

Body segment parameters (BSPs) are pivotal to a number of key analyses within sports and healthcare. Accuracy is paramount, as investigations have shown small errors in BSPs to have significant impact upon subsequent analyses, particularly when analysing the dynamics of high acceleration movements. There are many techniques with which to estimate BSPs, however, the majority are complex, time consuming, and make large assumptions about the underlying structure of the human body, leading to considerable errors. Interest is increasingly turning towards obtaining person-specific BSPs from 3D scans, however, the majority of current scanning systems are expensive, complex, require skilled operators, and require lengthy post processing of the captured data. The purpose of this study was to develop a low cost 3D scanning system capable of estimating accurate and reliable person-specific segmental volume, forming a fundamental first step towards calculation of the full range of BSPs.A low cost 3D scanning system was developed, comprising four Microsoft Kinect RGB-D sensors, and capable of estimating person-specific segmental volume in a scanning operation taking less than one second. Individual sensors were calibrated prior to first use, overcoming inherent distortion of the 3D data. Scans from each of the sensors were aligned with one another via an initial extrinsic calibration process, producing 360° colour rendered 3D scans. A scanning protocol was developed, designed to limit movement due to postural sway and breathing throughout the scanning operation. Scans were post processed to remove discontinuities at edges, and parameters of interest calculated using a combination of manual digitisation and automated algorithms.The scanning system was validated using a series of geometric objects representative of human body segments, showing high reliability and systematic over estimation of scan-derived measurements. Scan-derived volumes of living human participants were also compared to those calculated using a typical geometric BSP model. Results showed close agreement, however, absolute differences could not be quantified owing to the lack of gold standard data. The study suggests the scanning system would be well received by practitioners, offering many advantages over current techniques. However, future work is required to further characterise the scanning system's absolute accuracy

Accelerometry-based prediction of movement dynamics for balance monitoring

This paper proposes a 2D functional evaluation tool for estimating subject-specific body segment parameters, which uses a simple motor task (repeated sit-to-stand, rSTS), recorded with one single-axis accelerometer (SAA) per segment and a force plate (FP). After this preliminary estimation, the accelerometer alone is used to make quasi-real-time predictions of ground reaction force (anterior/posterior, F ( X ), and vertical, F ( Z ), components), center of pressure (CoP) and center of mass (CoM), during rSTS and postural oscillation in the sagittal plane. These predicted dynamic variables, as well as those obtained using anthropometric parameters derived from De Leva, were compared to actual FP outputs in terms of root mean-squared errors (RMSEs). Using De Leva's parameters in place of those estimated, RMSEs increase from 12 to 21 N (F ( X )), from 21 to 24 N (F ( Z )), and from 21.1 to 55.6 mm (CoP) in rSTS; similarly, RMSEs increase from 3.1 to 3.3 N (F ( X )) and from 5.5 to 6.6 mm (CoP) in oscillatory trials. A telescopic inverted pendulum model was adopted to analyze the balance control in rSTS using only predicted CoP and CoM. Results suggest that one SAA per segment is sufficient to predict the dynamics of a biomechanical model of any degrees of freedom