Influence of posture on the range of axial rotation and coupled lateral flexion of the thoracic spine

Objective
This study examines the influence of posture on the range of axial rotation of the thorax and the range and direction of the coupled lateral flexion.

Methods
The ranges of mid thoracic axial rotation and coupled lateral flexion were measured in 52 asymptomatic subjects (aged 18-43 years) using an optical motion analysis system. To examine the influence of posture on primary and coupled motion, we initiated axial rotation from a neutral sitting posture and from end-range thoracic flexion and extension.

Results
There was a significant decrease in the range of thoracic rotation in flexion compared with the neutral and extended postures (P < .001). The mean range of coupled lateral flexion was 8.9% of the axial rotation range in the neutral posture and increased to 14.3% and 23.2% in the extended and flexed postures, respectively. Patterns of coupled motion varied between subjects, but an ipsilateral pattern was more common in the flexed posture, whereas a contralateral pattern was more common in the neutral and extended postures.

Conclusions
The ranges and patterns of coupled motion of the thorax appear to be strongly influenced by the posture from which the movement is initiated. This has important implications in relation to the interpretation of clinical tests of thoracic motion and in consideration of mechanisms of development of thoracic pain disorders.

Postural neck pain : an investigation of habitual sitting posture, perception of 'good' posture and cervicothoracic kinaesthesia

Impairments of cervico-cephalic kinaesthesia and habitual forward head posture have been considered important in the aetiology of postural neck pain, yet these factors have not been specifically examined in a homogeneous clinical population. The objective of this study was to compare the habitual sitting posture (HSP), perception of good posture and postural repositioning error (PRE) of the cervico-thoracic (CT) spine in individuals with postural neck pain, with a matched group of asymptomatic subjects. Twenty-one subjects with postural neck pain and 22 asymptomatic control subjects were recruited into the study. An optical motion analysis system was used to measure the HSP and perceived ‘good’ sitting posture. PRE was measured over six trials where the subject attempted to replicate their self-selected ‘good’ posture. There was no difference between the groups in the HSP but significant differences were identified in the perception of ‘good’ posture. Posture repositioning error was higher for the head posture variables than for CT and shoulder girdle variables in both groups. However, there was no significant difference in posture repositioning error between groups for any of the posture measures. The findings suggest that individuals with postural neck pain may have a different perception of ‘good’ posture, but no significant difference in HSP or kinaesthetic sensibility compared with matched asymptomatic subjects.

Human action recognition based on 3D SIFT and LDA model

How to recognize human action from videos captured by modern cameras efficiently and effectively is a challenge in real applications. Traditional methods which need professional analysts are facing a bottleneck because of their shortcomings. To cope with the disadvantage, methods based on computer vision techniques, without or with only a few human interventions, have been proposed to analyse human actions in videos automatically. This paper provides a method combining the three dimensional Scale Invariant Feature Transform (SIFT) detector and the Latent Dirichlet Allocation (LDA) model for human motion analysis. To represent videos effectively and robustly, we extract the 3D SIFT descriptor around each interest point, which is sampled densely from 3D Space-time video volumes. After obtaining the representation of each video frame, the LDA model is adopted to discover the underlying structure-the categorization of human actions in the collection of videos. Public available standard datasets are used to test our method. The concluding part discusses the research challenges and future directions.

Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time.

Early jump-out corner detectors

Two corner detectors are presented, one of which works by testing similarity of image patches along the contour direction to detect curves in the image contour, and the other of which uses direct estimation image curvature along the contour direction. The operators are fast, robust to noise, and self-thresholding. An interpretation of the Kitchen-Rosenfeld corner operator is presented which shows that this operator can also be viewed as the second derivative of the image function along the edge direction.

Recognising and monitoring high-level behaviours in complex spatial environments

The recognition of activities from sensory data is important in advanced surveillance systems to enable prediction of high-level goals and intentions of the target under surveillance. The problem is complicated by sensory noise and complex activity spanning large spatial and temporal extents. This paper presents a system for recognising high-level human activities from multi-camera video data in complex spatial environments. The Abstract Hidden Markov mEmory Model (AHMEM) is used to deal with noise and scalability The AHMEM is an extension of the Abstract Hidden Markov Model (AHMM) that allows us to represent a richer class of both state-dependent and context-free behaviours. The model also supports integration with low-level sensory models and efficient probabilistic inference. We present experimental results showing the ability of the system to perform real-time monitoring and recognition of complex behaviours of people from observing their trajectories within a real, complex indoor environment.

Profile of position movement demands in elite junior Australian rules footballers

This study investigated the positional movement patterns in elite junior Australian Football (AF). Thirty players (17.1 ± 0.9 years) participating in this study were tracked over seven home games of the regular 2006 Victorian junior (Under 18) league season. Using lapsed-time video analysis, each position for an entire match was videotaped on three separate occasions over the course of the season. Data analysed included the number of individual efforts, duration and frequency of efforts; distance and percentage time for the classifications of standing, walking jogging, running and sprinting. Results showed that the midfield position travelled the greatest distance (4173 ± 238 m per quarter; p < 0.05; ES = .94) whilst the full forward/full back travelled the least (2605 ± 348 m per quarter, p < 0.05, ES = 1.21). For all positions, walking or jogging accounted for the greatest number of efforts (45-55%), conversely running and sprinting accounted for 5-13% of match efforts. The majority of efforts across all classifications were between 0-3.99 s. The data from this study provides further evidence that AF is an intermittent sport characterised by high intensity movements separated by low intensity movements at a ratio of one high intensity effort every 12-40 s. However, careful interpretation of the data is required when training junior AF players for specific positions, given the specific group studied.

Player movement patterns in an elite junior Australian Rules Football team : an exploratory study

This study explored the physical movement patterns associated with an elite Under 18 Australian Football (AF) team. Five field positions were selected with observations recording the number and relative per cent of "working" efforts (jogging, running, and sprinting), "resting" efforts (walking) and the total distances associated with "working" or "resting" efforts. Intra-observer reliability, using test- retest method, showed correlations were r = 0.98 or greater. The Wing position covered 11,877 m, the greatest total distance during an entire game, whilst the HBF and Centre positions both recorded 11,545 m and 11,537 m respectively and the Ruck position covered 9,203 m. The HBF recorded the greatest frequency of 'working' and 'resting' efforts (180 and 182 respectively), whilst the Wing (166 and 158), Centre (162 and 149) and Ruck (161 and 166) showed similarities in their results. The Wing position recorded the longest average distance per 'working' effort (58 m) whilst the Centre position recorded the longest average distance per 'resting' effort (17 m). Results also show the completion of less total efforts and smaller total distances, in Under 18 players, recorded compared to professional senior AF data. The results from this study suggest that further in-depth research is required into movement patterns and game activity demands in this AF playing group.

Influence of field position and phase of play on the physical demands of match-play in professional rugby league forwards

This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.

Validation of a trunk-mounted accelerometer to measure peak impacts during team sport movements

This study assessed the validity of an accelerometer to measure impacts in team sports. 76 participants completed a team sport circuit. Accelerations were collected concurrently at 100 Hz using an accelerometer and a 36-camera motion analysis system. The largest peak accelerations per movement were compared in 2 ways: i) pooled together and filtered at 13 different cut-off frequencies (range 6-25 Hz) to identify the optimal filtering frequency, and ii) the optimal cut-off frequency split into the 7 movements performed (n=532). Raw and 25-16 Hz filtering frequencies significantly overestimated and 6 Hz underestimated motion analysis peak accelerations (P <0.007). The 12 Hz filtered accelerometer data revealed the strongest relationship with motion analysis data (accuracy - 0.01±0.27 g, effect size - 0.01, agreement - 0.55 to 0.53 g, precision 0.27 g, and relative error 5.5%; P=1.00). The accelerometer underestimated peak accelerations during tackling and jumping, and overestimated during walking, jogging, sprinting and change of direction. Lower agreement and reduced precision were associated with sprinting, jumping and tackling. The accelerometer demonstrated an acceptable level of concurrent validity compared to a motion analysis system when filtered at a cut-off frequency of 12 Hz. The results advocate the use of accelerometers to measure movements in team sport.

Game and training load differences in elite junior Australian football

Game demands and training practices within team sports such as Australian football (AF) have changed considerably over recent decades, including the requirement of coaching staff to effectively control, manipulate and monitor training and competition loads. The purpose of this investigation was to assess the differences in external and internal physical load measures between game and training in elite junior AF. Twenty five male, adolescent players (mean ±SD: age 17.6 ± 0.5 y) recruited from three elite under 18 AF clubs participated. Global positioning system (GPS), heart rate (HR) and rating of perceived exertion (RPE) data were obtained from 32 game files during four games, and 84 training files during 19 training sessions. Matched-pairs statistics along with Cohen's d effect size and percent difference were used to compare game and training events. Players were exposed to a higher physical load in the game environment, for both external (GPS) and internal (HR, Session-RPE) load parameters, compared to in-season training. Session time (d = 1.23; percent difference = 31.4% (95% confidence intervals = 17.4 - 45.4)), total distance (3.5; 63.5% (17.4 - 45.4)), distance per minute (1.93; 33.0% (25.8 - 40.1)), high speed distance (2.24; 77.3% (60.3 - 94.2)), number of sprints (0.94; 43.6% (18.9 - 68.6)), mean HR (1.83; 14.3% (10.5 - 18.1)), minutes spent above 80% of predicted HRmax (2.65; 103.7% (89.9 - 117.6)) and Session-RPE (1.22; 48.1% (22.1 - 74.1)) were all higher in competition compared to training. While training should not be expected to fully replicate competition, the observed differences suggest that monitoring of physical load in both environments is warranted to allow comparisons and evaluate whether training objectives are being met. Key pointsPhysical loads, including intensity, are typically lower in training compared to competition in junior elite Australian football.Monitoring of player loads in team sports should include both internal and external measures.Selected training drills should look to replicate game intensities, however training is unlikely to match the overall physical demands of competition.

Validity of a trunk-mounted accelerometer to measure physical collisions in contact sports

CONTEXT: Accelerometer peak impact accelerations are being used to measure player physical demands in contact sports. However, their accuracy to do so has not been ascertained. PURPOSE: To compare peak-impact-acceleration data from an accelerometer contained in a wearable tracking device with a 3-dimensional motion-analysis (MA) system during tackling and bumping. METHODS: Twenty-five semielite rugby athletes wore a tracking device containing a 100-Hz triaxial accelerometer (MinimaxX S4, Catapult Innovations, Australia). A single retroreflective marker was attached to the device, with its position recorded by a 12-camera MA system during 3 physical-collision tasks (tackle bag, bump pad, and tackle drill; N = 625). The accuracy, effect size, agreement, precision, and relative errors for each comparison were obtained as measures of accelerometer validity. RESULTS: Physical-collision peak impact accelerations recorded by the accelerometer overestimated (mean bias 0.60 g) those recorded by the MA system (P < .01). Filtering the raw data at a 20-Hz cutoff improved the accelerometer's relationship with MA data (mean bias 0.01 g; P > .05). When considering the data in 9 magnitude bands, the strongest relationship with the MA system was found in the 3.0-g or less band, and the precision of the accelerometer tended to reduce as the magnitude of impact acceleration increased. Of the 3 movements performed, the tackle-bag task displayed the greatest validity with MA. CONCLUSIONS: The findings indicate that the MinimaxX S4 accelerometer can accurately measure physical-collision peak impact accelerations when data are filtered at a 20-Hz cutoff frequency. As a result, accelerometers may be useful to measure physical collisions in contact sports.

The effects of military body armour on trunk and hip kinematics during performance of manual handling tasks

Musculoskeletal injuries are reported as burdening the military. An identified risk factor for injury is carrying heavy loads; however, soldiers are also required to wear their load as body armour. To investigate the effects of body armour on trunk and hip kinematics during military-specific manual handling tasks, 16 males completed 3 tasks while wearing each of 4 body armour conditions plus a control. Three-dimensional motion analysis captured and quantified all kinematic data. Average trunk flexion for the weightiest armour type was higher compared with control during the carry component of the ammunition box lift (p < 0.001) and sandbag lift tasks (p < 0.001). Trunk rotation ROM was lower for all armour types compared with control during the ammunition box place component (p < 0.001). The altered kinematics with body armour occurred independent of armour design. In order to optimise armour design, manufacturers need to work with end-users to explore how armour configurations interact with range of personal and situational factors in operationally relevant environments. Practitioner Summary: Musculoskeletal injuries are reported as burdening the military and may relate to body armour wear. Body armour increased trunk flexion and reduced trunk rotation during military-specific lifting and carrying tasks. The altered kinematics may contribute to injury risk, but more research is required.

Validity of wearable technology to measure peak impact during high-intensity treadmill running

The purpose of this study was to identify the validity of an upper-body mounted accelerometer to measure peak acceleration during high-intensity treadmill running. A twelve camera motion analysis (MA) system was used as the criterion measure with markers placed on and close to the accelerometer. Ten peak impacts per participant were compared (n = 390). All accelerometer values were significantly different between the MA unit and T6 reflective marker’s acceleration data. Smoothing accelerometer data at 8 and 6 Hz provides an acceptable indirect measure of peak impact acceleration performed during high-intensity running. Consequently, smoothing algorithms should be incorporated into the commercially available software that the devices are supplied with.

A review on otolith models in human perception

The vestibular system, which consists of semicircular canals and otolith, are the main sensors mammals use to perceive rotational and linear motions. Identifying the most suitable and consistent mathematical model of the vestibular system is important for research related to driving perception. An appropriate vestibular model is essential for implementation of the Motion Cueing Algorithm (MCA) for motion simulation purposes, because the quality of the MCA is directly dependent on the vestibular model used. In this review, the history and development process of otolith models are presented and analyzed. The otolith organs can detect linear acceleration and transmit information about sensed applied specific forces on the human body. The main purpose of this review is to determine the appropriate otolith models that agree with theoretical analyses and experimental results as well as provide reliable estimation for the vestibular system functions. Formulating and selecting the most appropriate mathematical model of the vestibular system is important to ensure successful human perception modelling and simulation when implementing the model into the MCA for motion analysis.