Contemporary quality issues in product design are moving from materialistic to emotional user fulfillment; comprehensive research is needed to examine quality product feelings. This research is directed toward a deeper understanding of user and customer quality feelings for different product types, including services. The quality feelings concept includes dimensions of product quality, especially functionality, ergonomics and aesthetics. The first objective of this thesis is to identify, prioritize and synthesize quality feelings into product attributes in product development applications. The second objective is to explore, test and propose methodological approaches for designing quality feelings into products. Several methods from psychology, ergonomics, statistics and probabilistic methods and heuristics were applied to achieve the objectives. From a methodological viewpoint, Likert scales, free elicitation technique and Just About Right scales were applied for data collection. Multiple Regression, Factor Analysis, Correspondence Analysis, Genetic algorithms, Partial Least Squares (PLS) and Rough Sets (RS) were applied for data analyses. For ergonomic product evaluations, direct observations, 3D workload simulations, time and frequency analyses were conducted. Five product applications are included in this thesis: operator driver cabin design of reach trucks, steering wheel design trigger switch design in right-angled nutrunners, bed-making systemsproducts and waiting room environments. Heuristic methods were found effective when there is a high number of product attributes that interact to provide quality feelings. RS results are consistent with PLS attribute predictions. When the number of product attributes is large in comparison to the number of observations, PLS extracts informative results for quality feelings. The RS method is effective in identifying interactions among design attributes. Quality feelings are associated with both tangible (tactile characteristics) and intangible (quick and easy to use) product characteristics. Words such as safety, functionality, ergonomics, comfort, reliability, supportiveness, usability, feedback, pleasantness, attractiveness, durability and distinctiveness describe quality feelings from tangible products and services. Based on product type, the quality dimensions represented by these words possess different interactions and dependencies. In work environments, products act as prostheses between workers for social interaction, which need to be considered as important quality feelings dimensions. / QC 20111017

Nowadays virtual simulations are commonly used to solve problems regarding worker well-being or productivity in manufacturing companies. However, when it comes to finding a solution to one of these two objectives, the other usually tends to be secondary. In order to solve this problem, the Ergonomics in Production Platform (EPP) has been developed within research efforts at University of Skövde, which through the use of optimizations is able to obtain solutions where both objectives are taken into account. In turn, in order to address worker well-being, EPP makes use of the digital human modelling (DHM) tool. DHM tools are often used to evaluate simulations focused on studying human-machine interaction. However, as these software evolve and start to be able to reproduce complete motions, before they were only considering frames, new methods are needed to be able to assess risk factors such as time and prevent the occurrence of musculoskeletal disorders (MSDs). In order to assist in the development of EPP optimizations for simulations carried out in DHM tools, the time-based observational method RAMP was implemented, specifically the posture-related criteria of RAMP II. Using the Design and Creation research methodology, a welding gun case study located in China offered by Volvo Cars was used to evaluate the results of the optimizations carried out with EPP. For the evaluation of this case study, a manikin family of 10 members representing key cases of the Asian population was created for this task. Later, this task was recreated in IPS IMMA, where the 10 cases interacted with 3 welding guns to weld different spots on a piece. The analysis of this case study consisted of two distinct phases where the results of RAMP II implemented in EPP could be evaluated. The first phase focused on analyzing initial results of three different trajectories for all members of the family. The second phase consisted of optimizing one of the trajectories analyzed in the previous phase in such a way as to find the best welding angle of the gun to improve the results of the worst case in the first analysis. Three different factors were evaluated in this phase: RAMP II results versus the new angle, RAMP II results versus the results of other methods and the effect of productivity versus worker well-being. The results showed that welding angles of 116º and 80º were able to improve the values of the RAMP II criteria for the most disadvantaged manikin in the welding task. At the same time, it was observed that the higher the percentage of value added time, the higher the risk obtained in the analysis, worsening the worker's well-being.

<p>Training and ergonomics evaluation for laparoscopic surgery is an important tool for the assessment of trainees. Timely and objective assessment helps surgeons improve hand dexterity and movement precision, and perform surgery in an ergonomic manner. Traditionally, skill is evaluated by expert surgeons observing trainees, but this approach is both expensive and subjective. The approach proposed by this research employs an Ascension 3DGuidance trakSTAR system that captures the positions and orientations of hand and laparoscopic tool trajectories. Recorded trajectories are automatically analysed to extract meaningful feedback for training evaluation using statistical and machine learning methods.</p> <p>The data are acquired while a subject performs a standardized task such as peg transfer or suturing. The system records laproscopic instrument positions, hand, forearms, elbows trajectories, as well as wrist angles. We propose several metrics that attempt to objectively quantify the skill level or ergonomics of the procedure. The metrics for surgical skills are based on surgical instrument tip trajectories, whereas the ergonomics metric uses wrist angles. These metrics have been developed using statistical and machine learning methods.</p> <p>The metrics have been experimentally evaluated by using a population of seven first year postgraduate urology residents, one general surgery resident, and eight fourth year postgraduate urology residents and fellows. The machine learning approach discriminated correctly in 73% of cases between experts and novices. The machine learning approach applied to ergonomics data correctly discriminates between experts and novices in 88% of the cases for the peg transfer task and 75% for the suturing task. We also propose a method to derive a competency-based score using either statistical or machine learning derived metrics.</p> <p>Initial experimental data show that the proposed methods discriminate between the skills and ergonomics of expert and novice surgeons. The proposed system can be a valuable tool for research and training evaluation in laparoscopic surgery.</p> / Master of Applied Science (MASc)