The Human Assistant System

Kumaraswamy, R.; Srivastav, Shivam; Saurabh, .; Rishabh,; Ks, Nithin Bharadwaj

doi:10.1109/incoft55651.2022.10094343

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…In a smart home environment, a variety of activities can be performed through voice command, providing convenience and control without the need to interact directly with devices. Virtual assistants, such as Amazon's Alexa or Google Assistant, provide facilities for users to control home lighting, adjust room temperature, play music, open and close curtains or blinds, turn on and off appliances such as the coffee maker or vacuum cleaner, and manage home security such as activating and deactivating alarms and surveillance cameras [28]. Other more complex tasks that can be performed by these types of systems focus on programming customized routines, such as simulating presence at home during vacations by turning lights on and off and playing ambient sounds.…”

Section: Characterize the Work Domainmentioning

confidence: 99%

Recognition of Human Actions through Speech or Voice Using Machine Learning Techniques

Peña-Cáceres,

Silva-Marchan,

Albert

et al. 2023

CMC

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The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in the smart home. It seeks to demonstrate the effectiveness and feasibility of using machine learning techniques in predicting categories, subcategories, and actions from sentences. A dataset labeled with relevant information about categories, subcategories, and actions related to human actions in the smart home is used. The methodology uses machine learning techniques implemented in Python, extracting features using CountVectorizer to convert sentences into numerical representations. The results show that the classification model is able to accurately predict categories, subcategories, and actions based on sentences, with 82.99% accuracy for category, 76.19% accuracy for subcategory, and 90.28% accuracy for action. The study concludes that using machine learning techniques is effective for recognizing and classifying human actions in the smart home, supporting its feasibility in various scenarios and opening new possibilities for advanced natural language processing systems in the field of AI and smart homes.

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Section: Characterize the Work Domainmentioning

confidence: 99%