DMGR: Divisible Multi-complex Gesture Recognition Based on Word Segmentation Processing

Abstract: In the realm of gesture recognition and computer algorithm optimization, traditional approaches have predominantly focused on recognizing isolated gestures. However, this paradigm proves inadequate when confronted with complex gestural sequences, resulting in cumbersome recognition processes and diminished accuracy. Contemporary human-computer interaction (HCI) applications often necessitate users to perform intricate series of gestures, rather than isolated movements. Consequently, there is a pressing need for systems capable of not only recognizing individual gestures but also accurately segmenting and interpreting sequences of complex gestures to infer user intent and provide natural, intuitive responses.Drawing parallels with natural language processing (NLP), where understanding complex sentences requires word segmentation, structural analysis, and contextual comprehension, the field of HCI faces similar challenges in multi-complex dynamic gesture interaction. The cornerstone of effective gesture-based interaction lies in precise gesture segmentation, recognition, and intention understanding. The crux of the matter is developing methods to accurately delineate individual gestures within a continuous sequence and establish contextual relationships between them to discern the user's overarching intent. To address these challenges and facilitate more natural and user-friendly multi-complex dynamic gesture interaction, this paper introduces a novel recognition model and segmentation algorithm. The proposed framework draws inspiration from word processing techniques in NLP, applying a list model to the multi-complex gesture task machine. This approach decomposes complex gestural sequences into constituent operations, which are further subdivided into consecutive actions corresponding to individual gestures. By recognizing each gesture independently and then synthesizing this information, the system can interpret the entire complex gesture task. The algorithm incorporates the concept of action elements to reduce gesture dimension and employs a probability density distribution-based segmentation and optimization technique to accurately partition gestures within multi-complex tasks. This innovative approach not only enhances recognition accuracy but also significantly reduces computational complexity, as demonstrated by experimental results on a multi-complex gesture task database.The paper is structured as follows: First, it elucidates the algorithm framework for divisible multi-complex dynamic gesture task recognition and the underlying model based on word processing techniques. Subsequently, it provides a detailed exposition of the algorithm's implementation, encompassing feature extraction, gesture classification, segmentation, and optimization methodologies. Finally, the paper presents the experimental design and results, offering empirical validation of the proposed approach's efficacy.This research represents a significant advancement in the field of gesture recognition, particularly in handling complex, multi-gesture sequences. By addressing the limitations of traditional single-gesture recognition systems, this work paves the way for more sophisticated and intuitive human-computer interaction paradigms. The proposed model's ability to accurately segment and interpret complex gesture sequences opens up new possibilities for applications in various domains, from virtual reality interfaces to robotic control systems. The integration of concepts from NLP into gesture recognition underscores the interdisciplinary nature of this research, highlighting the potential for cross-pollination of ideas between different fields of computer science. Furthermore, the emphasis on reducing computational complexity while maintaining high accuracy addresses a crucial concern in real-time interactive systems. In conclusion, this study makes substantial contributions to the field of gesture recognition and HCI, offering a robust framework for handling multi-complex dynamic gesture tasks. The proposed algorithms and models not only advance the state of the art in gesture recognition but also lay the groundwork for more natural and efficient human-computer interaction modalities in future applications.

Keywords: Dynamic Gesture Recognition, Human-computer Interaction, Temporal Logic Relationship Network

DOI: 10.54941/ahfe1005654

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