abstract: Individuals fluent in sign language who have at least one deaf parent are considered native signers while those with non-signing, hearing parents are non-native signers. Musculoskeletal pain from repetitive motion is more common from non-natives than natives. The goal of this study was twofold: 1) to examine differences in upper extremity (UE) biomechanical measures between natives and non-natives and 2) upon creating a composite measure of injury-risk unique to signers, to compare differences in scores between natives and non-natives. Non-natives were hypothesized to have less favorable biomechanical measures and composite injury-risk scores compared to natives. Dynamometry was used for measurement of strength, electromyography for ‘micro’ rest breaks and muscle tension, optical motion capture for ballistic signing, non-neutral joint angle and work envelope, a numeric pain rating scale for pain, and the modified Strain Index (SI) as a composite measure of injury-risk. There were no differences in UE strength (all p≥0.22). Natives had more rest (natives 76.38%; non-natives 26.86%; p=0.002) and less muscle tension (natives 11.53%; non-natives 48.60%; p=0.008) for non-dominant upper trapezius across the first minute of the trial. For ballistic signing, no differences were found in resultant linear segment acceleration when producing the sign for ‘again’ (natives 27.59m/s2; non-natives 21.91m/s2; p=0.20). For non-neutral joint angle, natives had more wrist flexion-extension motion when producing the sign for ‘principal’ (natives 54.93°; non-natives 46.23°; p=0.04). Work envelope demonstrated the greatest significance when determining injury-risk. Natives had a marginally greater work envelope along the z-axis (inferior-superior) across the first minute of the trial (natives 35.80cm; non-natives 30.84cm; p=0.051). Natives (30%) presented with a lower pain prevalence than non-natives (40%); however, there was no significant difference in the modified SI scores (natives 4.70 points; non-natives 3.06 points; p=0.144) and no association between presence of pain with the modified SI score (r=0.087; p=0.680). This work offers a comprehensive analysis of all the previously identified UE biomechanics unique to signers and helped to inform a composite measure of injury-risk. Use of the modified SI demonstrates promise, although its lack of association with pain does confirm that injury-risk encompasses other variables in addition to a signer’s biomechanics. / Dissertation/Thesis / Doctoral Dissertation Exercise and Nutritional Sciences 2018

Machining chatter is an undesirable phenomenon of material removal processes and hardly to control or avoid. Its occurrence and extent essentially depend onthe kinematic, which alters with the position of the Tool Centre Point, of the machine tool. Research as to chatter was done widely but rarely with respect to changing structural dynamics during manufacturing. This thesis applies intelligent methods to learn the underlying functions of modal parameters – natural frequency, damping ratio, and mode shape – and defines the dynamic properties of a system firstly at this extent. To do so, it embraces three steps: first, the elaboration of the necessary dynamic parameters, second, the acquisition of the data via a simulation,and third, the prediction of the modal parameters with two kinds of Machine Learning techniques: Gradient Boosting Machine and Multilayer Perceptron. In total, it investigates three types of kinematics: cross bed, gantry, and overhead gantry. It becomes apparent that Light Gradient Boosting Machine outperforms Multilayer Perceptron throughout all studies. It achieves a prediction error of at most 1.7 % for natural frequency and damping ratio for all kinematics. However, it cannot really control the prediction of the participation factor yet which might originate in the complexity of the data and the data size. As expected, the error rises with noisy data and less amount of measurement points but at a tenable extent for both natural frequency and damping ratio. / 'Bearbetningsvibrationer är ett oönskat fenomen i materialborttagningsprocesser och är svåra att kontrollera eller undvika. Dess förekomst och omfattning beror i huvudsak på kinematiken, som förändras med positionen för verktygets centrumpunkt på verktygsmaskinen. Det har gjorts mycket forskning om bearbetningsvibrationer, men sällan om förändrad strukturell dynamik under tillverkningen. I denna avhandling tillämpas intelligenta metoder för att lära sig de underliggande funktionerna hos modalparametrar – egenfrekvens, dämpningsgrad och modalform – och definierar systemets dynamiska egenskaper för första gången i denna omfattning. För att göra detta omfattar den tre steg: för det första utarbetandet av de nödvändiga dynamiska parametrarna, för det andra insamling av data via en simulering och för det tredje förutsägelse av modalparametrarna med hjälp av två typer av tekniker för maskininlärning: Gradient Boosting Machine och Multilayer Perceptron. Sammanlagt undersöks tre typer av kinematik: crossbed, gantry och overhead gantry. Det framgår tydligt att Light Gradient Boosting Machine överträffar Multilayer Perceptron i alla studier. Den uppnår ett prediktionsfel på högst 1,7 % för egenfrekvens och dämpningsförhållande för alla kinematiker. Den kan dock ännu inte riktigt kontrollera förutsägelsen av deltagarfaktorn, vilket kan bero på datans komplexitet och datastorlek. Som väntat ökar felet med bullrig data och färre mätpunkter, men i en acceptabel omfattning för både naturfrekvens och dämpningsförhållande.