When designing work tasks, one goal should be to enable postures that maximize the force capabilities of the workers while minimizing the overall muscular demands; however, little is known regarding specific shoulder tissue loads during pushing and pulling. This study quantitatively evaluated the effects of direction (anterior-posterior pushing and pulling), handle height (100 cm and 150 cm), handle orientation (vertical and horizontal), included elbow angle (extended and flexed) as well as personal factors (gender, mass and stature) on hand force magnitudes, shoulder and L5/S1 joint moments, normalized mean muscle activation and electromyography (EMG)/force ratios during two-handed maximal push and pull exertions. Twelve female and twelve male volunteers performed maximal voluntary isometric contractions under 10 push and pull experimental conditions that emulated industrial tasks. Hand force magnitudes, kinematic data and bilateral EMG of seven superficial shoulder and trunk muscles were collected. Results showed that direction had the greatest influence on dependent measures. Push exertions produced the greatest forces while also reducing L5/S1 extensor moments, shoulder moments with the 150 cm height and overall muscular demands (p < 0.0001). The 100 cm handle height generated the greatest forces (p < 0.0001) and reduced muscular demands (p < 0.05), but were associated with greater sagittal plane moments (p < 0.05). Females generated, on average, 67% of male forces in addition to incurring greater muscular demands (p < 0.05). The flexed elbows condition in conjunction with pushing produced greater forces with reduced overall muscular demands (p < 0.0001). Furthermore, horizontal handle orientation caused greater resultant moments at all joints (p <. 0.05) The results have important ergonomics implications for evaluating, designing or modifying workstations, tasks or equipment towards improved task performance and the prevention of musculoskeletal injuries and associated health care costs.

When designing work tasks, one goal should be to enable postures that maximize the force capabilities of the workers while minimizing the overall muscular demands; however, little is known regarding specific shoulder tissue loads during pushing and pulling. This study quantitatively evaluated the effects of direction (anterior-posterior pushing and pulling), handle height (100 cm and 150 cm), handle orientation (vertical and horizontal), included elbow angle (extended and flexed) as well as personal factors (gender, mass and stature) on hand force magnitudes, shoulder and L5/S1 joint moments, normalized mean muscle activation and electromyography (EMG)/force ratios during two-handed maximal push and pull exertions. Twelve female and twelve male volunteers performed maximal voluntary isometric contractions under 10 push and pull experimental conditions that emulated industrial tasks. Hand force magnitudes, kinematic data and bilateral EMG of seven superficial shoulder and trunk muscles were collected. Results showed that direction had the greatest influence on dependent measures. Push exertions produced the greatest forces while also reducing L5/S1 extensor moments, shoulder moments with the 150 cm height and overall muscular demands (p < 0.0001). The 100 cm handle height generated the greatest forces (p < 0.0001) and reduced muscular demands (p < 0.05), but were associated with greater sagittal plane moments (p < 0.05). Females generated, on average, 67% of male forces in addition to incurring greater muscular demands (p < 0.05). The flexed elbows condition in conjunction with pushing produced greater forces with reduced overall muscular demands (p < 0.0001). Furthermore, horizontal handle orientation caused greater resultant moments at all joints (p <. 0.05) The results have important ergonomics implications for evaluating, designing or modifying workstations, tasks or equipment towards improved task performance and the prevention of musculoskeletal injuries and associated health care costs.

This study examined the neurological activation of children with autism spectrum disorders (ASD) while performing a pragmatic judgment task. In this study, children between the ages of 9 and 15 years responded to questions regarding a social situation, taken from the Comprehensive Assessment of Spoken Language, while concurrently having their brain activity measured. We targeted four brain regions for analysis: dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), superior temporal gyrus (STG), and the inferior parietal lobule (IPL). Ten children with ASD and 20 typically developing (TD) children participated. Matching occurred in a bracketing manner with each child in the ASD group being matched to two control children to account for natural variability. Neuroimgaging was conducted utilizing functional Near‐Infrared Spectroscopy (fNIRS). Oxygenated and deoxygenated blood concentration levels were measured through Near‐Infrared light cap with 44 channels. The cap was placed over frontal lobe and the left lateral cortex. The placement was spatially registered using the Polhemus. Analysis indicated that children in the ASD group performed significantly poorer than their controls on the pragmatic judgment task. Mixed repeated measures analysis of variance of neurological data indicated that the children with ASD had lower concentration levels of oxygenated and total hemoglobin across the four regions. There were significantly higher concentration levels for oxygenated and total hemoglobin in the STG. Analysis of correct and incorrect responses revealed significantly more activation in the OFC when responses were correct. Additionally, there was a significant interaction of Accuracy and Group in left DLPFC. Children with ASD presented higher oxygenated hemoglobin concentration values when responding correctly, while children in the control group presented higher oxygenated hemoglobin concentration values for the incorrect items. Statistical Parametric Mapping was performed for each triad to assess the diffusion of neural activation across the frontal cortex and the left lateral cortex. Individual comparisons revealed that 7 out of 10 children with ASD demonstrated patterns consistent with more diffuse brain activation than their TD controls. Findings from this study suggest that an fNIRS study can provide important information about the level and diffusion of neural processing of verbal children and adolescents with ASD.

The far-reaching successes of deep neural networks in a wide variety of learning tasks have prompted research on how model properties account for high network performance. For a specific class of models whose activation functions are piecewise linear, one such property of interest is the number of linear regions that the network generates. Such models themselves define piecewise linear functions by partitioning input space into disjoint regions and fitting a different linear function on each such piece. It would be expected that the number or configuration of such regions would describe the model’s ability to fit complicated functions. However, previous works have shown difficulty in identifying linear regions as satisfactory predictors of model success. In this thesis, the question of whether the generation of linear regions due to training encode the properties of the learning problem is explored. More specifically, it is investigated whether change in linear region density due to model fitting is related to the geometric properties of the training data. In this work, data geometry is characterized in terms of the curvature of the underlying manifold. Models with ReLU activation functions are trained on a variety of regression problems defined on artificial manifolds and the change in linear region density is recorded along trajectories in input space. Learning is performed on problems defined on curves, surfaces and for image data. Experiments are repeated as the data geometry is varied and the change in density is compared with the manifold curvature measure used. In no experimental setting, was the observed change in density found to be clearly linked with curvature. However, density was observed to increase at points of discontinuity. This suggests that linear regions can in some instances model data complexities, however, the findings presented here do not support that data curvature is encoded by the formation of linear regions. Thus, the role that linear regions play in controlling the capacity of piecewise linear networks remains open. Future research is needed to gain further insights into how data geometry and linear regions are connected. / De breda framgångar som djupa neurala nätverk har uppvisat i en mängd olika inlärningsproblem har inspirerat ny forskning med syfte att förklara vilka modellegenskaper som resulterar i högpresterande nätverk. För neurala nätverk som använder styckvis linjära aktiveringsfunktioner är en intressant egenskap att studera de linjära regioner som nätverket genererar i det vektorrum som utgör träningsdatans definitionsmängd. Nätverk med styckvis linjära aktiveringsfunktioner delar upp definitionsmängden i distinkta regioner på vilka olika linjära funktioner avbildas. Dessa nätverk avbildar själva styckvis linjära funktioner. Genom att anpassa flera skilda linjära avbildningar går det att approximera funktioner som är icke-linjära. Därför skulle man kunna förvänta sig att antalet linjära regioner som en modell genererar och hur de är fördelade i rummet kunde fungera som mått på modellens förmåga att lära sig komplicerade funktioner. Tidigare efterforskingar inom detta område har dock inte kunnat demonstrera ett samband mellan antalet eller fördelningen av linjära regioner och modellens prestanda. I den här avhandlingen undersöks det vilken roll linjära regioner spelar i att förklara en modells kapacitet och vad den lär sig. Fångar de linjära regioner som ett nätverk lär sig de underliggande egenskaperna hos träningsdatan? Mer specifikt så studeras huruvida den lokala förändringen i antalet linjära regioner efter modellträning korrelerar med träningsdatans geometri. Träningsdata genereras från syntetiska mångfalder och datageometrin beskrivs i termer av mångfaldens krökning. På dessa mångfalder definieras regressionsproblem och träning upprepas för topologier av olika form och med olika krökning. Skillnaden i antalet linjära regioner efter träning mäts längs banor i definitionsdomänen och jämförs med datans krökning. Ingen av de experiment som utfördes lyckades påvisa något tydligt samband mellan förändring i antal regioner och datans krökning. Det observerades dock att antalet linjära regioner ökar i närheten av punkter som utgör diskontinuiteter. Detta antyder att linjära regioner under vissa omständigheter kan modellera komplexitet. Således förblir rollen som linjära regioner har i att förklara modellförmåga diffus.

No description available.