Predicting Sagittal Plane Lifting Postures from Image Bounding Box Dimensions

Details

• Personal Author:
  Bao S ; Difranco N ; Greene RL ; Hu YH ; Lin J-H ; Lu M-L ; Radwin RG ; Wang X
• Description:
  OBJECTIVE: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination. BACKGROUND: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle. METHODS: Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30 degrees. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures. RESULTS: The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing. CONCLUSIONS: The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes. APPLICATIONS: It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Musculoskeletal System Occupational Health Safety Software Workload
• Keywords:
  Algorithms Anthropometry Author Keywords: Anthropometry Biomechanical Modeling Biomechanical Models Biomechanics Computer Software Gait Humans Job Risk Assessment Lifting Low Back Manual Lifting Manual Materials Handling Models Physiological Function Physiology Posture Risk Assessment Skeletal System Spine Statistical Analysis Video Work Capacity Work Physiology

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• ISSN:
  0018-7208
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 10 ; OSHA Region 5 ; Washington ; Wisconsin
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DART - Division of Applied Research and Technology
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  64-77
• Volume:
  61
• Issue:
  1
• NIOSHTIC Number:
  nn:20052447
• Citation:
  Hum Factors 2019 Feb; 61(1):64-77
• Contact Point Address:
  Robert G. Radwin, Department of Industrial and Systems Engineering, University of Wisconsin-Madison, 1550 Engineering Drive, Madison, WI 53706
• Email:
  rradwin@wisc.edu
• Federal Fiscal Year:
  2019
• Peer Reviewed:
  True
• Source Full Name:
  Human Factors
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:6668f07f10fc02e80258984c371b0c616e9d88cb119ae03b88932aff3107e9e6b9be7c6a4d08f743a72e1b757e95c1166dc516324e7b2b7f655edaee4a02a63d
• Download URL:
  https://stacks.cdc.gov/view/cdc/211973/cdc_211973_DS1.pdf
• File Type:
  [PDF - 646.67 KB ]