Detection of hand gestures using gesture language discrete values

Abstract

Computer implemented method for detecting a hand gesture of a user, comprising: (a) Receiving sequential logic models each representing a hand gesture. The sequential logic model maps pre-defined handposes and motions each represented by a hand features record defined by discrete hand values each indicating a state of respective hand feature. (b) Receiving a runtime sequence of runtime hand datasets each defined by discrete hand values scores indicating current state hand features of a user's moving hand which are inferred by analyzing timed images depicting the moving hand. (c) Submitting the runtime hand datasets and the pre-defined hand features records in SSVM functions to generate estimation terms for the runtime hand datasets with respect to the hand features records. (d) Estimatingwhich of the hand gestures best matches the runtime sequence depicted in the timed images by optimizing score functions using the estimation terms for the runtime hand datasets.

Description

Background technique [0001] With the development of the computerized environment, the use of human machine interfaces (HMI) has increased significantly. There is an increasing need for more natural human-machine user interface (NUI) methods (such as, for example, voice and / or gaze) and more specifically hand gesture interactions to replace and / or supplement traditional HMIs, such as keyboards, pointing devices, and / Or touch interface. This can be used, for example, to eliminate and / or reduce the need for intermediary devices (such as keyboards and / or pointing devices), support submission-free interaction, improve accessibility for (multiple) groups of people with disabilities, and / or provide multi-modality Interactive environment. There may be current solutions for identifying and / or recognizing hand gesture(s), but most of them are immature, with insufficient accuracy and / or high complexity, and require high computing resources for extensive computer vision processing and ...

AI Technical Summary

Problems solved by technology

Current solutions for identifying and / or recognizing hand pose(s) may exist, however they are mostly immature, insufficiently accurate and / or high in complexity while requiring a high degree of computational resources for extensive computer vision processing and / or machine learning

Such techniques may rely on the entire hand skeletal joints and / or complex machine learning algorithms to detect and / or classify hand poses, which can make such implementations expensive and unattractive for integration, preventing them from being targeted for broad use Adopted

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