Concurrently observed actions are represented as independent actions and not as a compound action

Recent research suggests that we can simultaneously represent the actions of multiple agents in our motor system. However, it is currently unclear exactly how we represent their actions. Here, we tested two competing hypotheses. According to the independence hypothesis, we represent concurrently observed actions as independent, competing actions. According to the compound hypothesis, we instead integrate those actions, whenever possible, into compound actions. In Experiment 1 (N = 32), we first show that the standard imitation-inhibition task with a single hand can be extended to measure automatic imitation of compound actions. In Experiment 2 (N = 55), we then tested how we represent concurrently observed actions by further extending this task to include two hands performing identical or different actions. The results revealed that two hands performing two different actions (e.g., one hand lifts index finger, one hand lifts middle finger) produced an effect similar to that of both hands performing just one of those actions (e.g., both hands lift index finger) but different from that of both hands performing both actions together (e.g., lift both index and middle finger). As such, our results show that concurrently observed actions are coded separately in the motor system.

A comparative study of classifier techniques for lift index data analysis

Purpose A classifier technique is one of the important tools which may be used to classify the data or information into systematic manner based on certain criteria pertaining to get the accurate statistical information for decision making. It plays a vital role in the various applications, such as business organization, e-commerce, health care, scientific and engineering application. The purpose of this paper is to examine the performance of different classification techniques in lift index (LI) data classification. Design/methodology/approach The analyses consist of two stages. First, the random data are generated for lifting task through computer programming, which is then put into the National Institute for Occupational Safety and Health equation for LI estimation. Based on the evaluated index, the task may be classified into two groups, i.e. high-risk and low-risk task. The classified task is considered to analyze the performance of different tools like Artificial Neural Network (ANN), discriminant analysis (DA) and support vector machines (SVMs). Findings The work clearly demonstrates the accuracy and computational ability of ANN, DA and SVM for data classification problems in general and LI data in particular. From the research it may be concluded that SVM may outperform ANN and DA. Research limitations/implications The research is limited to a particular kind of data that may be further explored by selecting the different controllable parameters and model specification. The study can also be applied to realistic problem of manual loading. It is expected that this will help researchers, designers and practicing engineers by making them aware of the performance of classification techniques in this area. Originality/value The objective of this research work is to assess and compare the relative performance of some well-known classification techniques like DA, ANN and SVM, which suggest that data characteristics considerably impact the classification performance of the methods.

Panel Discussion In Honor of Dr Tom Waters The NIOSH Lifting Equation - Part II

With the introduction of the NIOSH Lifting Equation, specifically after the publication of the Revised Lifting Equation (RLE) (Waters et al., 1993), occupational health and safety professionals across the world have successfully utilized the RLE to evaluate the risks associated with lifting and lowering tasks in the workplace. Although the RLE takes into consideration of various job task variables to determine recommended weight limits for a variety of task combinations, a number of articles and peer reviewed publications have appeared with a notion of either extending or modifying the RLE to address manual handling situations that the original equation was not able to assess comprehensively. The purpose of this panel discussion will be to provide an overview to ergonomics practitioners and researchers of these extensions and beyond, thus exploiting the full potential of this lifting equation. The first presentation will discuss the Variable Lift Index (VLI) for highly variable manual lifting tasks, whereas the second presentation describes the Sequential Lift Index (SLI) in assessing a sequence of lifting tasks that workers perform while they rotate to different tasks over a workday. In addition to the inherent variability in lifting tasks assumed by the RLE, we often encounter materials handling scenarios associated with one-handed lifting, team lifting, lifting of people (e.g., patient handling), lifting while seated or kneeling, lifting on improper frictional surfaces, lifting unstable loads, or lifting for more than 8 hours. To this effect, the third presentation examines the wider application of the RLE by adding new multipliers to the equation. Extending further on quantifying typical lifting task-related variables and associated risk on the lower back, the final presentation explores the fatigue failure process experienced by the lumbar spine when performing multiple and varied lifting tasks. This innovative approach is nascent in ergonomics literature, especially in ergonomics risk assessment, and has great potential in injury prevention at the workplace.

Clap and Fling Aerodynamics-An Experimental Evaluation

The ‘Clap and Fling’ hypothesis, which describes augmentation of lift during the wingbeat of certain insects and birds, was evaluated experimentally in model form. Using induced velocity output as a lift index, and testing at a Reynolds number of roughly 83000, it was learned that: 1. The main ‘Clap and Fling’ aerodynamic effect consists of raising the lift output realized at the beginning of the stroke. After one chord of travel, ‘Clap and Fling’ effects are minor.2. Considered over a complete stroke, ‘Clap and Fling’ lift output is limited to 1.15 times the lift output of an identical fixed incidence wing undergoing the same jump start trajectory.3. ‘Clap and Fling’ output is limited by considerations of maximum circulation and circulation persistence. These limitations are not envisioned in current analyses.