Assistive Technology/Rehabilitation Engineering: Faculty Research

Assistive technology, rehabilitation engineering and biomedical image analysis (advanced prosthetics, control, biomimetics, and more).

Adam K. Piper:
Cognitive and Physical Ergonomic Design and Assessment

Research in the Human Engineering Lab housed in the Industrial Engineering & Engineering Management Department focuses on modeling and evaluating human performance using biomechanics, EMG, posture analysis, and wearable bioinstrumentation, especially in occupational settings. This information is useful for modeling the demands on humans during a variety of activities so that injuries and other negative health effects can be predicted and prevented. Additional research is underway using computational intelligence to inform humans of health hazards and to learn to accommodate physical disabilities with limited human input and effort. Some recent applications of these research lines are included below. There are currently opportunities for both MS Theses and PhD Dissertations on these and other similar research topics.

• Designing a Biometrically Adaptable Workstation
  The Human Engineering Lab has recently prototyped a workbench capable of learning and recalling specific physical adjustments to optimize its usage for any number of individuals. With a single finger swipe, the workstation uses intelligent automation to adjust all of its dimensions (surface height, monitor arm location and angle, etc.). Whether standing, seated, or in a wheelchair—for computing, assembly or equipment operation—the workstation can accommodate most needs. This is especially important for disabled individuals who are physically limited from making manual adjustments and might otherwise settle for a workstation setup that can lead to injury and productivity decline. Future versions of the workstation will involve machine vision to self-adjust to the user without the need to recall previously saved preferences.
  
• Modeling the Physical Cost of Loading/Unloading Shipping Containers
  Loading shipping containers or long-haul trailers to minimize wasted space and maximize product volume in each container is common practice. However, saving a few cents per unit can create dramatically challenging jobs for those tasked with loading and unloading these giant metal boxes. With wearable bioinstrumentation, we can monitor the muscle activity, postures and physiological responses of workers during loading and unloading activities to determine the risk for musculoskeletal disorder or other disabling injuries. Once the physical demands on material handlers for various loading strategies are understood, a model will be developed to compare one strategy to the next so that decisions on which loading strategy to use can be made based both on minimizing shipping costs and minimizing ergonomic costs.
  
• Developing Symbol-based Risk Communication Using Computational Intelligence
  Safety symbols are present on almost every modern product, label or piece of industrial equipment. While many symbols are familiar and even ubiquitous, a large percentage of symbols do not meet the comprehension expectations outlined in national and international standards. To improve the development of symbols with greater comprehensibility, a new design technique is under development in the human engineering lab that incorporates an artificial intelligent assistant to learn from human users what symbols are most easily understood for a given safety message, and to offer improvement suggests based on global feedback in real time. The project uses genetic algorithms and mathematical clustering algorithms to semi-automate the process of collecting symbol sketches from novice participants, evaluating those sketches for thematic similarities, and proposing new and improved symbol designs that increase in comprehensibility.