Video analysis for identifying human operation difficulties and faucet usability assessment
As the world struggles to cope with a growing elderly population, concerns of how to preserve independence are becoming increasingly acute. A major hurdle to independent living is the inability to use everyday household objects. This work aims to automate the assessment of product usability for the elderly population using the tools of computer vision and machine learning. A novel video analysis technique is presented that performs temporal segmentation of video containing humanproduct interaction and automatically identifies time segments in which the human has difficulties in operating the product. The method has applications in the automatic assessment of the usability of various product designs via measuring the frequency of operation difficulties. The approach is applied to a case study of water faucet design for the older adult population with dementia. Experiments in the automatic analysis of a large database of real-world recorded videos confirm the effectiveness of the approach in providing valid temporal segmentation (accuracy 88.1%) and in the correct estimation of the relative advantage (or disadvantage) of one design over another in terms of operation difficulties in performing various actions.
video analysis operation difficulty usability assessment computer vision machine learning temporal segmentation humanproduct interaction