Digital Human Modeling Information

Digital human modeling information can be mobilized to reduce workplace injuries and improve human performance in product development. In order to achieve these goals, however, the digital information needs to be comprehensive, organized, and accessible; it also needs to be incorporated into human-hardware interfaces that can use it successfully. Bio-robotic systems are the most complex human modeling systems, but also the most useful. They require even further data integration than other types of digital information models. Their most basic requirement is some kind of interface that allows a closed-loop response through sensors that activate actuators so that the system can respond to the data it receives by modifying the humanÆs environment or other conditions accordingly.

Hasada et al. developed an agent-based physiological digital human and applied the data to a health-enhancing chair. Their chair was interfaced to modular software that has an easily-modified interface. The ôagentö consisted of a computer program that was self-contained, able to run its own program without outside intervention, and able to work with other agents. It also had the capacity to respond to external stimuli. Their chair was rigged with multiple sensors to provide information on temperature, total fluid in body extremities, heart rate, pressure points, and so forth. The actuators allowed the digital human system to react to information fr

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ntly, changing the position of the chair can restore blood flow to the legs; or When an EMG detects muscle restriction leading to possible cramping, the chair can induce movement to relieve the restriction. Statistical analysis of psychometric data can provide valuable insight into the design process as well, permitting statistical data to help designers interpret psychological variables such as intelligence or aptitude and incorporate the results into the design of the process or product. The success of this approach is dependent on the quality of the questions being asked to determine psychometric factors. Examples of useful questions grouped by variable might be: Variable 1: Education. What is the highest level of education that the person operating this device will have? and, What subjects did the subject study? Variable 2: Intelligence. What are the subjectÆs test scores on standardized intelligence tests? and, How does the subject score on cognitive learning tests? Variable 3: Motor skill. What level of motor skill does the subject exhibit in motor testing? and, What motor limitations does the subject have? Consideration should be given to both dependent and independent variables. The dependent variables will be those
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Approximate Word count = 1240
Approximate Pages = 5 (250 words per page)

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