AI FITNESS WORKOUT ASSISTANT USING NLP TECHNIQUES

This paper presents an AI fitness workout assistant utilizing Natural Language Processing (NLP) techniques, specifically Multilayer Perceptron (MLP) algorithm, to enhance user experience and engagement in personalized exercise routines. The proposed system employs NLP to comprehend and interpret user input, such as fitness goals, preferences, and constraints, extracted from textual descriptions or voice commands. The Multilayer Perceptron algorithm is utilized for its capability to model complex non- linear relationships between input and output variables, enabling efficient learning from user interactions and historical workout data. Through continuous interaction, the assistant tailor workout recommendations and provides real-time feedback, adapting to user progress and preferences. Experimental results demonstrate the effectiveness of the MLP- based NLP approach in accurately understanding user intents and generating personalized workout plans, fostering a more engaging and productive fitness experience.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-03-28
DOI
https://doi.org/10.71097/ijsat.v16.i1.2874
Primary Topic
Sports and Physical Education Research
Type
article
Field-Weighted Citation Impact
0.00
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article

AI FITNESS WORKOUT ASSISTANT USING NLP TECHNIQUES

Somnath Arjun, M. Sujitha, M Priyanka, Shoail Nizam - et al.
International Journal on Science and Technology
Sports and Physical Education Research
article

AI FITNESS WORKOUT ASSISTANT USING NLP TECHNIQUES

Somnath Arjun, M. Sujitha, M Priyanka, Shoail Nizam -, Ranjeet Saw -
article en

Abstract

This paper presents an AI fitness workout assistant utilizing Natural Language Processing (NLP) techniques, specifically Multilayer Perceptron (MLP) algorithm, to enhance user experience and engagement in personalized exercise routines. The proposed system employs NLP to comprehend and interpret user input, such as fitness goals, preferences, and constraints, extracted from textual descriptions or voice commands. The Multilayer Perceptron algorithm is utilized for its capability to model complex non- linear relationships between input and output variables, enabling efficient learning from user interactions and historical workout data. Through continuous interaction, the assistant tailor workout recommendations and provides real-time feedback, adapting to user progress and preferences. Experimental results demonstrate the effectiveness of the MLP- based NLP approach in accurately understanding user intents and generating personalized workout plans, fostering a more engaging and productive fitness experience.

International Journal on Science and TechnologyVol. 16(1)
Openalex Percentile: Top 91%
Sports and Physical Education Research
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