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In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r2=78.6%, RMSD=15.1) compared to the regression approach (r2=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations.

The measurement of handgrip force responses is important in many aspects, for example: to complement neurological assessments, to investigate the contribution of muscle mass in predicting functional outcomes, in setting realistic treatment goals, evaluating rehabilitation strategies. Normative data about handgrip force can assist the therapist in interpreting a patient's results compared with healthy individuals of the same age and gender and can serve as key decision criteria. In this context, establishing normative values of handgrip strength is crucial. Hence, the aim of the this study is to develop a tool that could be used both in rehabilitation and in the prevention of work-related musculoskeletal disorders. This tool takes the form of population-specific predictive equations, which express maximum handgrip force as a function of age. In order to collect data from studies measuring maximum handgrip force, three databases were searched. The search yielded 5,058 articles. Upon the removal of duplicates, the screening of abstracts and the full-text review of potentially relevant articles, 143 publications which focussed on experimental studies on various age groups were considered as fulfilling the eligibility criteria. A comprehensive literature review produced 1,276 mean values of maximum handgrip force. A meta-analysis resulted in gender- and world region-specific (general population, USA, Europe and Asia) equations expressing maximum force as a function of age. The equations showed quantitative differences and trends in maximum handgrip force among age, gender and national groups. They also showed that values of maximum handgrip force are about 40% higher for males than for females and that age-induced decrease in force differs between males and females, with a proved 35% difference between the ages of 35 and 75. The difference was lowest for the 60-64 year olds and highest for the 18-25 year-olds. The equations also showed that differences due to region are smaller than those due to age or gender. The equations that were developed for this study can be beneficial in setting population-specific thresholds for rehabilitation programmes and workstation exposure. They can also contribute to the modification of commonly used methods for assessing musculoskeletal load and work-related risk of developing musculoskeletal disorders by scaling their limit values.

Capability-based planning as an approach to defense planning is an almost infinitely complex engineered system with countless nodes and layers of interdependency, influenced by state and non-state diplomatic activities, information, military and economic actions creating secondary and third order effects. The main output of capability-based planning is the set of capability requirements needed to achieve the expected end-state. One revitalized qualitative technique that allows us to gain insights into unstructured and fuzzy problems in the military is wargaming—in its simplest form this involves manual wargaming. At the same time, there has been a push to bring computer assistance to such wargaming, especially to support umpire adjudication and move more generally towards full automation of human elements in wargames. However, computer assistance in wargaming should not be pushed, regardless of cost, towards quantitative techniques. The objective complexity of a problem often does not allow us to replicate the operational environment with the required fidelity to get credible experimental results. This paper discusses a discovery experiment aiming to verify the concept of applying a qualitative expert system within computer assisted wargaming for developing capability requirements in order to reduce umpire bias and risk associated with their decisions. The innovation here lies in applying system dynamics modelling and simulation paradigms when designing the theoretical model of capability development, which forms the core of the expert system. This new approach enables qualitative comparisons between different sets of proposed capability requirements. Moreover, the expert system allows us to reveal the effects of budget cuts on proposed capability requirement solutions, which the umpire was previously unable to articulate when comparing individual solutions by relying solely on his own knowledge. Players in the wargame validated the proposed concept and suggested how the study might be developed going forward: namely, by enabling users to define their own capabilities and not being limited by a predefined set of capabilities.

The maximum cooperative grasping mass and diameter of the human thumb and index finger were investigated by 7560 grasp-release trials on various masses of solid cylinders and various sizes of hollow rings. The maximum grasping mass of the participants’ thumb-index finger depended on gender, age and the sum of thumb-index finger lengths (P 0.05). The maximum grasping diameter of the participants’ thumb-index finger depended on the age, sum of thumb-index finger lengths and ratio of index finger to thumb length (P 0.05). There was a nonlinear regression model for the dependence of the maximum grasping mass on gender, age and the sum of thumb-index finger lengths and another nonlinear regression model for the dependence of the maximum grasping diameter on the age, sum of thumb-index finger lengths and ratio of index finger to thumb length. Two regression models were useful in the optimal size design of robotic hands intending to replicate thumb-index finger grasping ability. This research can help to define not only a reasonable grasp mass and size for a bionic robotic hand, but also the requirements for hand rehabilitation.

Quantifiable differences among fingers in force control capability have both important practical and theoretical values in characterizing force control of accurate finger-tapping tasks. Following the classical Fitts’ law paradigm, we quantified the differences among ten fingers in term of speed–accuracy trade-off (SAT) in performing repetitive discrete force control tasks. Visual cues displaying targeted force magnitudes and tolerances were provided. Users were required to apply the targeted force within the given tolerance quickly and accurately by pressing a force sensor using the specified finger. We found that ten fingers obeyed the Meyer model in the SAT performance and they differed in reaction time, the index of performance (IP), and the goodness of fit for the Meyer model. A modified Meyer model was proposed to quantify the difference between ten fingers in the SAT performance using only one parameter, making the quantification easier than using the original Meyer model. Pairwise comparisons showed that the differences between symmetrical fingers on both hands were insignificant except for the pair of index fingers. These findings provided us with multiple perspectives on the differentiation among ten fingers in the force control capabilities. Our study helps lay the foundation for engineering systems that rely on finger force control ability.