Physical workload and musculoskeletal symptoms in the human-horse work environment

Most work in horse stables is performed manually in much the same way as a century ago, with old-fashioned tools and equipment. It is one of the least mechanised sectors dealing with large animals, which often involves work in awkward postures and lifts of heavy loads. However, there is a lack of knowledge of the ergonomic risks in the human-horse work environment. This thesis seeks to provide a deeper understanding of the human-horse work environment, work tasks, workload and frequency of musculoskeletal symptoms and to identify potential risk factors for the development of musculoskeletal symptoms. Self-reporting methods (questionnaires, rating scales), observation methods (OWAS, REBA), descriptive task analysis (HTA, HA, GTS) and biomechanical analysis (JACK) were used to collect and analyse data. Riding instructors surveyed in the questionnaire study reported high levels of perceived musculoskeletal symptoms in at least one of nine anatomical areas during the past year and the past week. The most frequently reported problem areas were the shoulders, the lower back and the neck. Mucking out stables was considered to be the task involving the heaviest work. OWAS analysis showed that three work tasks contained a high proportion of unacceptably awkward work postures, namely mucking out, preparing bedding and sweeping. During mucking out and sweeping, the back was bent and twisted for most of the time. There were many high-risk operations involved in mucking out boxes and disposing of bedding material. Emptying a wheel barrow on the muck heap included high-risk operations with awkward postures such as twisted, bent back arms over shoulder level and handling high loads. The analytical methods used clearly revealed where in the work tasks the ergonomic problems occurred. In almost all operations with a high risk level, a shafted tool or wheelbarrow was used. Analysis of the shaft length of two hand-held tools used for mucking out (manure fork, shavings fork) showed that the manure fork should have a longer shaft to reduce loading on the back. The results for the shavings fork were inconclusive, but indicated the importance of changes in work technique. More in-depth knowledge of the musculoskeletal symptoms and work tasks performed in the human-horse work environment makes it easier to plan and implement measures to prevent musculoskeletal symptoms in this particular group of workers

Comparing the Kinematic and Kinetic Outputs from Digital Human Modeling Tools to a Lab-Based Rigid-Link Model for the Investigation of Musculoskeletal Disorder Hazards During Patient Handling

Patient repositioning tasks expose healthcare providers (HCPs) to high bone-on-bone forces, resulting in the development of musculoskeletal disorders (MSDs) (Fragala,2011). Researchers have been able to estimate biomechanical exposures during patient turning using kinematic and kinetic data collected from HCPs (e.g., Marras et al., 1999); however, many of these laboratory-based studies require considerable time and resources to execute and it also remains challenging to gather reliable data (Jäger et al., 2013). Digital human modeling (DHM) may offer unique advantages over direct measurement to estimate biomechanically relevant exposures. Investigators have used DHM to evaluate MSD hazards (Cao et al., 2013; Potvin, 2017); however, there is limited evidence on the fidelity of their outputs. The objective of this study was to compare the kinematic and kinetic outputs produced by two commercial DHM software packages against those generated using a lab-based motion-capture driven approach when analyzing HCPs performance of patient turns. Twenty-five (25) HCPs (eight males) performed a patient turn in the laboratory using a hospital bed with a live 82kg male patient. Whole body kinematics and sagittal plane video were collected. External peak hand force was measured using a force gauge. An accelerometer was placed on the sternum of the patient to identify point of initial patient motion which was assumed to represent the time-point of peak hand force application. Whole body kinematics were used to drive a rigid linked segment model for each participant using Visual3D (C-Motion Inc., Germantown, USA). Measured peak hand force was divided by two and applied to the model at the grip center of each hand at the frame of peak force application. A top down modeling approach was used to calculate trunk and shoulder joint angles and L4-L5 and shoulder joint moments about the flexion/extension axis. These outputs were extracted and compared against DHM software outputs. Siemens Jack (V 8.4) and Santos Pro DHM software packages were used to simulate the patient turn. The static patient turn posture used by the HCP was modeled using the manual joint manipulation, posture prediction and motion capture data importing approaches available in both software. Anthropometrics and peak hand force gathered from the laboratory experiment were inputted into the digital models. trunk and shoulder joint angles and L4-L5 and shoulder joint moments were computed and extracted about the flexion/extension axis from each digitally modeled posture. RMANOVAs, Pearson Product Moment correlation coefficients and Bland Altman analyses were used to compare DHM outputs to the lab-based model outputs. Results from this investigation indicate that the use of Siemens Jack’s (V 8.4) manual joint manipulation approach estimated low back and shoulder kinematics and kinetics that were in agreement with lab-based model outputs. The kinematics and kinetics computed using the posture prediction and motion capture driven approaches to modeling the patient repositioning task, using both Siemens Jack (V 8.4) and Santos Pro were not in agreement with the lab-based outputs. This may have been a result of differences in kinematic modeling assumptions related to the structure of skeletal linkage models, joint decompositions, degrees of freedom in each model and anthropometrics used in DHM software. The use of DHM tools for biomechanical analyses of patient repositioning tasks has the possibility to aide in the investigation of MSD exposures; however, it is important for investigators to understand the purpose of each DHM modeling approach as well as the underlying assumptions of digital human models that may affect kinematic and kinetic outputs used to quantify the exposure to MSDs

Product conceptualization through a 3D natural interface considering in real-time spatial and ergonomic restrictions

Currently, the conceptualization of products whose shape and configuration depends on the context is a highly time-consuming process since it has to be achieved asynchronously between the real environment of the product’s usage and the design office -- In general, the designer manually maps the context in order to create a 3D model of it and then to start the product design process -- The literature presents some proposals to digitalize the context without the need of a manual mapping -- However, these approaches are mainly computer-centric tools where the designer is desk-bound and (s)he does not have a clear spatial perception since the interactions with the 3D models are usually based on 2D interfaces -- On this research we aimed to prove that conceptualization of context-dependent products directly over its real environment through gesture-based modeling tools, allows the designer to consider spatial and ergonomic restrictions that the context imposes to the product, through the real-time analysis of the interaction user-context -- In order to prove that, we developed a tool called Äir-Modeling, in which the designer is able to create virtual conceptual products quickly and efficiently, taking advantage of hand gestures meanwhile (s)he is interacting directly with the real scenario in an Augmented Reality (AR) environment -- Air-Modeling also allows a continuous evaluation of the user postures involved in the product usage and assembly in order to analyze ergonomic risks, and perform the necessary changes in the product shape or configuration from early stages of the design process -- A test was carried out to prove the effects of the use of the proposed tool in the design process in comparison with the traditional way through traditional CAD packages -- We found that the real context can be used as an information input in real-time during product conceptualization -- Beside this, we could notice that virtual parts creation is more efficient from a 3D input than a 2D interface such as a mouse or a keyboard -- This was reflected in the experiment carried out in which 21 users conceptualized a bookcase for a given context using both Air-Modeling and a commercial CAD tool -- It was obtained a reduction in the modeling time using our tool on 76% of the cases with a final average reduction of 44%. Finally, we concluded that 3D modeling in AR environments using the hands as interface and the context as an information input in realtime, allows the designer to conceptualize potential solutions in quick and efficient manner, exploiting as much as possible, inspirational instants -- On the other hand, modeling in a natural scale directly over the real scene prevents the designer to draw his/her attention on dimensional details but allows him/her to focus on the product itself and its relation with the environment -- Besides, developing 3D models in natural scale allows analyzing the interaction between the user, the context and the virtual model for determining ergonomic issues related with the product usage or assembly process -- We believe that this kind of technologies makes the development of customized products more efficient by adding spatial and ergonomic restrictions to the conceptualization process in real-time -- This facilitates the convergence to the design solution, possibly avoiding some iteration in the design proces

Towards Conversational Diagnostic AI

At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.Comment: 46 pages, 5 figures in main text, 19 figures in appendi

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 233, June 1982

This bibliograhy lists 387 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1982

The causes and prevention of airline baggage handler back injuries : Safe designs required where behaviour and administrative solutions have had limited effect

"Back injuries have consistently been the most common types of injuries suffered by people at work. They have been a significant worker injury problem in most, if not all, industrialised countries for many years and manual handling has long been established as a significant task related back injury causal factor.[...] This research project established that the manufacturers of the jet airlines used by the airlines in this study had not previously been acquainted with the issue of baggage handler back injuries.[...] This study also canvassed the opinion of airline safety professionals and airline baggage handlers concerning baggage handling tasks and working environment related causal factors. [...] A major focus of this research project was also to measure the effect of ACE and Sliding Carpet, two commercially available retro-fit baggage systems, on the risk of back injuries to baggage handlers stacking baggage within Boeing B737 narrow-body aircraft."Doctor of Philosoph

Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Compiles peer-reviewed research and literature reviews on issues regarding patient safety and quality of care, ranging from evidence-based practice, patient-centered care, and nurses' working conditions to critical opportunities and tools for improvement

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 240, January 1983

Reports, articles and other documents, numbering 357, introduced into the NASA scientific and technical information system in December 1982 are given

Assembling life : models, the cell, and the reformations of biological science, 1920-1960

Imperial Users onl

Aerospace Medicine and Biology: A cumulative index to the 1982 issues

This publication is a cumulative index to the abstracts contained in the Supplements 229 through 240 of Aerospace Medicine and Biology: A continuing Bibliography. It includes three indexes: subject, personal author, and corporate source