Improving the efficacy of motion analysis as a clinical tool through artificial intelligence techniques

Abstract

Technology supporting human motion analysis has advanced dramatically and yet its clinical application has not grown at the same pace. The issue of its clinical value is related to the length of time it takes to do an interpretation, the cost, and the quality of the interpretation. Techniques from artificial intelligence such as neural networks and knowledge-based systems can help overcome these limitations. In this paper we give an overview of these techniques and describe current research efforts that apply these techniques in the field of human motion analysis.

Exploring Sensor Gloves for Teaching Children Sign Language

“…The drawback with using NNs and HMMs, is that significant time and effort is required in order to design and train the networks. It is also hard to search for errors, and to explain the outcome of the classification process [15]. Template matching based classification methods on the other hand, can be devised relatively fast, makes it easy to search for errors, and makes it easy to explain the outcome of the classification process [15].…”

“…It is also hard to search for errors, and to explain the outcome of the classification process [15]. Template matching based classification methods on the other hand, can be devised relatively fast, makes it easy to search for errors, and makes it easy to explain the outcome of the classification process [15]. One form of template matching, which has been successfully used for classifying data from sensor gloves, is referred to as "conditional template matching."…”