Safe Patient Handling among STNA’s in Nursing Homes: Compliance, Monitoring and Continuous Improvement of Best Practices; and Prevent Work-Related Injuries in Construction, Wholesale and Retail Trades Workspaces Using Wearable Computer Technology [8016]
Date: Wednesday, March 8, 2017
Time: 4:00 PM - 5:00 PM
Location: Greater Columbus Convention Center
Room: B240

Speakers:
   Glenn Goodman, Associate Director, School of Health Sciences, Cleveland State University
   Wenbing Zhao, Professor, Cleveland State University
   Joan Niederriter, Associate Professor, School of Nursing, Cleveland State University
   Alaa Badokhon, Graduate Student, Case Western Reserve University

Track: Medical
Secondary Track: Ergonomics
Session Type: Educational Session
Skill Level: Intermediate

Description:

Presenters discuss results from two research projects funded by BWC’s Ohio Occupational Safety and Health Research Program.
Presenters from project eight demonstrate the use of a Microsoft Kinect sensor and smart watches to monitor movement of health-care workers for bedside activities. They describe and demonstrate components of a safe movement program that a center for older adults implemented. In addition, they present preliminary outcomes of the program. This is relevant to administrators of skilled nursing or other health-care facilities, ergonomists and health-care workers.
In project nine, the presenter discusses a wearable computer system design that records lower extremities motion and underfoot planar pressure distribution. This wearable design has the potential to enable objective identification of risk factors, such as slip, trip and fall events.



Learning Objectives:
Explain the use of Microsoft Kinect to monitor movement of state tested nurse aides doing bedside tasks
Identify components and summarize findings of outcomes of the safe movement program
Describe a wearable computer system that enables objective identification of risk factors, such as slip, trip and fall from natural activities
Explain how statistical analytics methods are used in balance and gait patterns recognition from time-stamped wearable sensor data streams

CEU Hours:  
  IACET: 0.1
  BELTSS: 0.5
  BWC Discount Programs: 1

File & Handouts:
File 1
File 2
File 3
Work and Balance-Related Injury Prevention Using Portable and Wearable Computer Technology
Final Handout WCGL Print - Work and Balance-Related Injury Prevention Using Portable and Wearable Computer Technology







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