Lora Cavuto

Lora Cavuoto received her M.S. and PhD. degrees in Industrial and Systems Engineering from Virginia Tech in 2009 and 2012, respectively. She earned an M.S. degree in Occupational Ergonomics and Safety from the University of Miami in 2008. She is currently an Assistant Professor in the Industrial and Systems Engineering Department at the University at Buffalo. Dr. Cavuoto is the Director of the Ergonomics and Biomechanics Laboratory at the University at Buffalo. Her current research focuses on quantifying physical exposures and physiological responses in the workplace to identify indicators of fatigue development. Her research work also aims to understand and model the effects of health conditions, particularly obesity and aging, on physical capacity, specifically strength, fatigue, and motor performance. She is an Associate Editor for Human Factors and Ergonomics in Manufacturing & Service Industries and serves on the Editorial Board for Applied Ergonomics.


Contributions

  • Low-Cost Sensor System Design for In-Home Physical Activity Tracking
    Low-Cost Sensor System Design for In-Home Physical Activity Tracking

    An aging and more sedentary population requires interventions aimed at monitoring physical activity, particularly within the home. This research uses simulation, optimization, and regression analyses to assess the feasibility of using a small number of sensors to track movement and infer physical activity levels of older adults. Based on activity data from the American Time Use Survey and assisted living apartment layouts, we determined that using three to four doorway sensors can be used to effectively capture a sufficient amount of movements in order to estimate activity.

  • Towards Unobtrusive Patient Handling Activity Recognition for Reducing Injury Risk among Caregivers
    Towards Unobtrusive Patient Handling Activity Recognition for Reducing Injury Risk among Caregivers

    Nurses regularly perform patient handling activities. These activities with awkward postures expose healthcare providers to a high risk of overexertion injury. The recognition of patient handling activities is the first step to timely detect the injury development then reduce injury risk for caregivers. The current practice on workplace activity recognition is based on human observational approach, which is neither accurate nor projectable to a large population. In this paper, we aim at addressing these challenges.

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