Resting toucher: a time and motion analysis of elite lawn bowls

Whilst numerous investigations have explored the physical demands placed upon competitive sportspeople from a wide array of sports little is known about the physical demands placed on lawn bowlers. The purpose of this study was to ascertain the movement activities of Australian representative singles and pairs players and to determine the frequency and duration of these activities. One match each of two male and two female players (one singles and one pairs player per gender) were videotaped during an international tournament. During playback of the videotaped matches (n = 4), a single observer coded the players’ activities into five distinct categories (waiting, walking forward, walking backward, jogging and bowling) using a computerised video editing system (Gamebreaker™ Digital Video Analysis System). Field calibration of players over 30m for forward motions and 15m for the backward motion was performed to allow for the estimation of total distance covered during the match. Heart rate was monitored during each match. The duration of a match was found to be (mean ± SD) 1hr 28 ± 15mins. The total distance covered during each match was 2093 ± 276m. The mean percentage of match time spent in each motion was: waiting, 61.8 ± 9.3%; walking forward, 22.3 ± 5.6%; walking backward, 2.0 ± 0.4%; jogging, 1.1 ± 0.5%; and bowling, 8.5 ± 4.2%. Average heart rate was found to be 57 ± 7% of age-predicted HRmax with a maximum of 78 ± 9% of age-predicted HRmax. The results of this study suggest that playing lawn bowls at an international level requires light-moderate intensity activity similar to that reported for golf.

Human passive motions and a user-friendly energy harvesting system

Energy harvesting for wireless sensors and consumer electronic devices can significantly improve reliability and environmental sustainability of the devices. This is achieved by eliminating the dependency of these devices on rechargeable batteries, using clean and/or renewable energy sources. Energy harvesting from various energy sources is widely discussed among researchers and entrepreneurs, including harvesting energy from microscale phenomena. This topic is receiving increasing attention due to the rising numbers of low-power consumer electronic devices and wireless sensors, but also the increasing demand for more convenient and available devices. This article presents a feasibility study for an energy harvesting system based on a human's breathing motion. The system is based on a modified pants belt that is integrated with an array of piezoelectric films and a harvesting circuit. The proposed energy harvester generates electricity from reciprocal abdominal motions of the human subject. In comparison with existing breathing-based energy harvesters, the proposed system allows for safe and convenient energy harvesting with no influence on the natural movement of the lungs. Stomach pressure analysis and measurement, as well as the design and simulations of the proposed harvester, are presented. © 2013 The Author(s).

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.

Metabolic power method underestimates energy expenditure in field sport movements using a GPS tracking system

The purpose of this study was to assess the validity of a GPS tracking system to estimate energy expenditure (EE) during exercise and field sport locomotor movements. Twenty-seven participants each completed one 90 minute exercise session on an outdoor synthetic futsal pitch. During the exercise session participants wore a 5 Hz GPS unit interpolated to 15 Hz (SPI HPU, GPSports Pty Ltd, Australia) and a portable gas analyser (Metamax® 3B, Cortex Pty Ltd, Germany) which acted as the criterion measure of EE. The exercise session was comprised of alternating five minute exercise bouts of randomised walking, jogging, running or a field sport circuit (x3) followed by 10 minutes of recovery. One-way ANOVA showed significant (p<0.01) and very large underestimations between GPS metabolic power derived EE and VO2 derived EE for all field sport circuits (% difference ≈ -44%). No differences in EE were observed for the jog (7.8%) and run (4.8%) while very large overestimations were found for the walk (43.0%). The GPS metabolic power EE over the entire 90 minute session was significantly lower (p<0.01) than the VO2 EE, resulting in a moderate underestimation overall (-19%). The results of this study suggest that a GPS tracking system using the metabolic power model of EE does not accurately estimate EE in field sport movements or over an exercise session consisting of mixed locomotor activities interspersed with recovery periods; however is able to provide a reasonably accurate estimation of EE during continuous jogging and running.

Derivation of turbulent energy of fiber suspensions

The energy equation for turbulent flow of fiber suspensions was derived in terms of second order correlation tensors. Fiber motion of turbulent energy including the correlation between pressure fluctuations and velocity fluctuations was discussed at two points of flow field, at which the correlation tensors were the functions of space coordinates, distance between two points, and time.

Estimating energy expenditure with the RT3 triaxial accelerometer

The RT3 is a relatively new triaxial accelerometer that has replaced the TniTrac. The aim of this study was to validate the RT3 against doubly labeled water (DLW) in a free-living, mixed weight sample of adults. Total energy expenditure (TEE) was measured over a 15-day period using DLW Activity-related energy expenditure (AEE) was estimated by subtracting resting energy expenditure and thermic effect of feeding from TEE. The RT3 triaxial accelerometer was worn over 14 consecutive days. TEE and AEE were estimated using the RT3 proprietary equation. Thirty-six adults ages 18-56 years (56% women) with an average weight of 75.9 kg (SD = 14.8) completed all measurements. Compared to DLW the RT3 underestimated TEE by 539 kJ (4%) and AEE by 485 kJ (15%) on average. The RT3 provided a relatively accurate assessment of free-living activity-related energy expenditure at the group level and generally underestimated total and activity-related energy expenditure compared to DLW

Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A∗-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A∗ approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

CCTV scene perspective distortion estimation from low-level motion features

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to use low-level spatiotemporally local motion features only. This particular challenge arises in many semiautomatic surveillance systems that alert a human operator to potential abnormalities in the scene. Low-level spatiotemporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions. First, we describe a dense algorithm for perspective estimation, which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate. Second, we also present an alternative coarse algorithm that subdivides the image frame into blocks and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme. Third, we report the results of an evaluation using nine large sets acquired using existing closed-circuit television cameras, not installed specifically for the purposes of this paper. Our findings demonstrate that both proposed methods are successful, their accuracy matching that of human labeling using complete visual data (by the constraints of the setup unavailable to our algorithms).

Numerical investigation of the turbulent energy budget in the wake of freely oscillating elastically mounted cylinder at low reduced velocities

We present a numerical study of the turbulent kinetic energy budget in the wake of cylinders undergoing Vortex-Induced Vibration (VIV). We show three-dimensional Large Eddy Simulations (LES) of an elastically mounted circular cylinder in the synchronization regime at Reynolds number of Re=8000. The Immersed Boundary Method (IBM) is used to account for the presence of the cylinder. The flow field in the wake is decomposed using the triple decomposition splitting the flow variables in mean, coherent and stochastic components. The energy transfer between these scales of motions are then studied and the results of the free oscillation are compared to those of a forced oscillation. The turbulent kinetic energy budget shows that the maximum amplitude of VIV is defined by the ability of the mean flow to feed energy to the coherent structures in the wake. At amplitudes above this maximum amplitude, the energy of the coherent structures needs to be fed additionally by small scale, stochastic energy in form of backscatter to sustain its motion. Furthermore, we demonstrate that the maximum amplitude of the VIV is defined by the integral length scale of the turbulence in the wake