Processes of task performance as measured by the assessment of motor and process skills (AMPS): a predictor of work-related outcomes for adults with schizophrenia?

Objective: To determine whether the processes of task performance as measured by the Assessment of Motor and Process Skills (AMPS) would discriminate between the employment levels of adults with schizophrenia. Participants: Twenty adults with schizophrenia who were engaged either in competitive employment, supported employment, prevocational training, or nonvocational activities, participated in this exploratory study. Methods: Each participant completed the AMPS, the Positive and Negative Syndrome Scale (PANSS), the Addiction Severity Index (ASI), and theWorker Role Interview (WRI) to gather data about their occupational performance, symptoms, drug / alcohol use, and psychosocial / environmental factors that might influence their work related outcomes. Results: Analysis revealed a moderate correlation between the level of employment and the global scores of the process skills scale in the AMPS. Conclusions: This should be seen as preliminary evidence that beyond the basic cognitive functions, processes of task performance may also be a predictor of work related outcomes for this population. The results also highlighted the importance of considering personal causation and worker roles when assessing the work capacities of these clients. Finally, findings supported the four levels of employment used in this study, which appeared to form a continuum from nonvocational activities, prevocational training, supported employment, through to competitive employment.

Analysis of the cost performance of structural building frames in Australia

The significance of this research is that it is the first comprehensive analysis of cost performance across Australia. It is well known that measuring cost performance is not an easy task; comparisons of building projects on a `like-for-like’ basis are uncommon, and rarely occur in the real world. However, this paper analyses 120 different structural frame models that represent various; structural designs, construction methods, grid spans, and locations.

The research produced price models that were representative of structural frames used in medium-rise non-residential buildings. It is based on pricing a number of standard building frame designs in five Australian cities. The results represent the cost of producing the same building in different locations, using similar building construction techniques. By utilizing a standard model, project variables like building quality, ground conditions and access were eradicated, thereby facilitating an unbiased comparison of cost performance. I addition, the results are an indicator of building productivity based on costs per square metre of various construction types.

This research provides the Australian industry with robust data about the relative cost performance of various structural building frames. In addition, this research has wider implications because the models may also become useful data for the measuring relative cost performance in other countries.

Identifying film takes for cinematic analysis

In this paper, we focus on the ‘reverse editing’ problem in movie analysis, i.e., the extraction of film takes, original camera shots that a film editor extracts and arranges to produce a finished scene. The ability to disassemble final scenes and shots into takes is essential for nonlinear browsing, content annotation and the extraction of higher order cinematic constructs from film. In this work, we investigate agglomerative hierachical clustering methods along with different similarity metrics and group distances for this task, and demonstrate our findings with 10 movies.

Automated film rhythm extraction for scene analysis

This paper examines film rhythm, an important expressive element in motion pictures, based on our ongoing study to exploit film grammar as a broad computational framework for the task of automated film and video understanding. Of the many, more or less elusive, narrative devices contributing to film rhythm, this paper discusses motion characteristics that form the basis of our analysis, and presents novel computational models for extracting rhythmic patterns induced through a perception of motion. In our rhythm model, motion behaviour is classified as being either nonexistent, fluid or staccato for a given shot. Shot neighbourhoods in movies are then grouped by proportional makeup of these motion behavioural classes to yield seven high-level rhythmic arrangements that prove to be adept at indicating likely scene content (e.g. dialogue or chase sequence) in our experiments. Underlying causes for this level of codification in our approach are postulated from film grammar, and are accompanied by detailed demonstration from real movies for the purposes of clarification.

Finding the beat : an analysis of the rhythmic elements of motion pictures

This paper forms a continuation of our work focused on exploiting film grammar for the task of automated film understanding. We examine film rhythm, a powerful narrative concept used to endow structure and form to the film compositionally and to enhance its lyrical quality experientially. Of the many, often complex, cinematic devices contributing to film rhythm, this paper investigates the rhythmic elements that are present in edited sequences of shots, and presents a novel computational model to detect shot structural rhythm as either metric, accelerated, decelerated, or free. Details of the algorithm for the extraction of these editing rhythm classes are presented, along with experimental results on real movie data. Following this we study the usefulness of combining the rhythmic patterns induced through both motion and editing in film. We show that, whilst detailed content identification via rhythm types alone is not possible by virtue of the fact that film is not codified to this level in terms of rhythmic elements, analysis of the combined motion/shot rhythm can allow us to determine that the content has changed and hypothesize as to why this is so. We present 3 such categories of change and demonstrate their efficacy for capturing useful film elements (e.g., scene change precipitated by plot event), by providing data support from 5 motion pictures.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Subjective job task analyses for physically demanding occupations : what is best practice?

Workers in physically demanding occupations (PDOs) are frequently subjected to physical selection tests. To avoid legal ramifications, workplaces must be able to show that any personnel selection procedures reflect the inherent requirements of the job. A job task analysis (JTA) is fundamental in determining the work tasks required for employees. To date, there are no published instructions guiding PDO researchers on how to conduct job task analyses. Job task analysis research for non-PDOs offers some insight into the expected reliability and validity of data obtained on the most prevalent task domains in job analysis (importance, frequency, time spent and difficulty). This review critiques such research, and the existing published material on JTA of PDOs, and provides recommendations for future research and practice.

Reliability maps and probabilistic guarantee of task motion for robotic manipulators

There are many applications for which reliable and safe robots are desired. For example, assistant robots for disabled or elderly people and surgical robots are required to be safe and reliable to prevent human injury and task failure. However, different levels of safety and reliability are required for different tasks so that understanding the reliability of robots is paramount. Currently, it is possible to guarantee the completion of a task when the robot is fault tolerant and the task remains in the fault-tolerant workspace (FTW). The traditional definition of FTW does not consider different reliabilities for the robotic manipulator's different joints. The aim of this paper is to extend the concept of a FTW to address the reliability of different joints. Such an extension can offer a wider FTW while maintaining the required level of reliability. This is achieved by associating a probability with every part of the workspace to extend the FTW. As a result, reliable fault-tolerant workspaces (RFTWs) are introduced by using the novel concept of conditional reliability maps. Such a RFTW can be used to improve the performance of assistant robots while providing the confidence that the robot remains reliable for completion of its assigned tasks. © 2012 Copyright Taylor & Francis and The Robotics Society of Japan.

The Integrated Model of Sport Confidence: A Canonical Correlation and Mediational Analysis

The main purpose of the study was to examine crucial parts of Vealey’s (2001) integrated framework hypothesizing that sport confidence is a mediating variable between sources of sport confidence (including achievement, self-regulation, and social climate) and athletes’ affect in competition. The sample consisted of 386 athletes, who completed the Sources of Sport Confidence Questionnaire, Trait Sport Confidence Inventory, and Dispositional Flow Scale-2. Canonical correlation analysis revealed a confidence-achievement dimension underlying flow. Bias-corrected bootstrap confidence intervals in AMOS 20.0 were used in examining mediation effects between source domains and dispositional flow. Results showed that sport confidence partially mediated the relationship between achievement and  self-regulation domains and flow, whereas no significant mediation was found for social climate. On a subscale level, full mediation models emerged for achievement and flow dimensions of challenge–skills balance, clear goals, and concentration on the task at hand.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Lower limb progressive resistance training improves leg strength but not gait speed or balance in Parkinson's disease: a systematic review and meta-analysis.

The use of progressive resistance training (PRT) to improve gait and balance in people with Parkinson's disease (PD) is an emerging area of interest. However, the main effects of PRT on lower limb functions such as gait, balance, and leg strength in people with PD remain unclear. Therefore, the aim of the meta-analysis is to evaluate the evidence surrounding the use of PRT to improve gait and balance in people with PD. Five electronic databases, from inception to December 2014, were searched to identify the relevant studies. Data extraction was performed by two independent reviewers and methodological quality was assessed using the PEDro scale. Standardized mean differences (SMD) and 95% confidence intervals (CIs) of fixed and random effects models were used to calculate the effect sizes between experimental and control groups and I (2) statistics were used to determine levels of heterogeneity. In total, seven studies were identified consisting of 172 participants (experimental n = 84; control n = 88). The pooled results showed a moderate but significant effect of PRT on leg strength (SMD 1.42, 95% CI 0.464-2.376); however, no significant effects were observed for gait speed (SMD 0.418, 95% CI -0.219 to 1.055). No significant effects were observed for balance measures included in this review. In conclusion, our results showed no discernable effect of PRT on gait and balance measures, although this is likely due to the lack of studies available. It may be suggested that PRT be performed in conjunction with balance or task-specific functional training to elicit greater lower limb functional benefits in people with PD.

Performance analysis and prediction in triathlon

Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008–2012). The analysis reveals patterns of performance in five components of triathlon (three race “legs” and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.

Analysis of phasic and tonic electromyographic signal characteristics: electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

The pattern of tonic and phasic components in an EMG signal reflects the underlying behaviour of the central nervous system (CNS) in controlling the musculature. One avenue for gaining a better understanding of this behaviour is to seek a quantitative characterisation of these phasic and tonic components. We propose that these signal characteristics can range between unvarying, tonic and intermittent, phasic activation through a continuum of EMG amplitude modulation. In this paper, we present two new algorithms for quantifying amplitude modulation: a linear-envelope approach, and a mathematical morphology approach. In addition we present an algorithm for synthesising EMG signals with known amplitude modulation. The efficacy of the synthesis algorithm is demonstrated using real EMG data. We present an evaluation and comparison of the two algorithms for quantifying amplitude modulation based on synthetic data generated by the proposed synthesis algorithm. The results demonstrate that the EMG synthesis parameters represent 91.9% and 96.2% of the variance of linear-envelopes extracted from lumbo-pelvic muscle EMG signals collected from subjects performing a repetitive-movement task. This depended, however, on the muscle and movement-speed considered (F=4.02, p<0.001). Coefficients of determination between input and output amplitude modulation variables were used to quantify the accuracy of the linear-envelope and morphological signal processing algorithms. The linear-envelope algorithm exhibited higher coefficients of determination than the most accurate morphological approach (and hence greater accuracy, T=8.16, p<0.001). Similarly, the standard deviation of the coefficients of determination was 1.691 times smaller (p<0.001). This signal processing algorithm represents a novel tool for the quantification of amplitude modulation in continuous EMG signals and can be used in the study of CNS motor control of the musculature in repetitive-movement tasks.

Studying mathematics teacher education: analysing the process of task variation on learning

Self-study of variations to task design offers a way of analysing how learning takes place. Over several years, variations were made to improve an assessment task completed by final-year teacher candidates in a primary mathematics teacher education subject. This article describes how alterations to a task informed on-going developments in self-study of one assessment task employed in an online subject. Analysis of my journal, notes from conversations with colleagues, teacher candidates’ work on the task and responses to online forums, and survey data inspired variations focused on better exploration of key concepts involved in the task, raising of focal awareness, developing a stronger professional eye in the students and the author, adaptations for multiple curriculum levels, and explorations of dual teacher–student perspectives. The overall challenge has been to support teacher candidates to learn to design effective open-ended tasks with a critical professional eye. Descriptions of the changes made to the task and the development of my own professional eye as a consequence of the application of self-study are included. Data show that variations to the task increased teacher candidates’ understanding of mathematics problem posing and generated pedagogical insights for task design.