Abstract
The objective of this research effort is to integrate therapy instruction with child-robot play interaction in order to better assess upper-arm rehabilitation. Using computer vision techniques such as Motion History Imaging (MHI), edge detection, and Random Sample Consensus (RANSAC), movements can be quantified through robot observation. In addition, incorporating prior knowledge regarding exercise data, physical therapeutic metrics, and novel approaches, a mapping to therapist instructions can be created allowing robotic feedback and intelligent interaction. The results are compared with ground truth data retrieved via the Trimble 5606 Robotic Total Station and visual experts for the purpose of assessing the efficiency of this approach. We performed a series of upper-arm exercises with two male subjects, which were captured via a simple webcam. The specific exercises involved adduction and abduction and lateral and medial movements. The analysis shows that our algorithmic results compare closely to the results obtain from the ground truth data, with an average algorithmic error is less than 9% for the range of motion and less than 8% for the peak angular velocity of each subject.
Highlights
In the United States, the Individuals with Disabilities Education Act (IDEA) states that children with a physical disability are entitled to a free public education that emphasizes special education and related services designed to meet their unique needs and prepare them for further education, employment, and independent living
Sensory-motor rehabilitation techniques based on the use of robotic and mechatronic devices have been applied with stroke patients [6, 23, 26, 28, 30, 31, 34, 37, 41]
Since the research presented in this work focuses on non-contact, upper-arm rehabilitation, for the shoulder joint, we present the successful analysis of a typical metric used by physical therapist, range of motion (ROM)
Summary
In the United States, the Individuals with Disabilities Education Act (IDEA) states that children with a physical disability are entitled to a free public education that emphasizes special education and related services designed to meet their unique needs and prepare them for further education, employment, and independent living. One common technique for attaining the three-dimensional information from a particular movement is to recover the pose of the person at each time instant using a three-dimensional model [1] This generally requires a strong segmentation of foreground/background and of individual body parts to aid the model alignment process. While some algorithms utilize sequences of static configurations, which require recognition and segmentation of the person [35], here, a Motion History Image (MHI) to represent how motion in the image is moving is formed. This essentially allows real-time processing of the input data
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