[Paper Review] Chatbot for fitness management using IBM Watson

Sai Rugved Lola, Rahul Dhadvai+2 more|arXiv (Cornell University)|Dec 30, 2021

AI in Service Interactions4 citations

TL;DR

This paper proposes a serverless chatbot for fitness management using IBM Watson's NLP and NLU capabilities, integrated into a web application to deliver personalized diet plans, home exercises, and fitness counseling. The system leverages IBM Cloud's AI services to enable natural, interactive user engagement, offering a scalable, automated fitness management solution with real-time recommendations.

ABSTRACT

Chatbots have revolutionized the way humans interact with computer systems and they have substituted the use of service agents, call-center representatives etc. Fitness industry has always been a growing industry although it has not adapted to the latest technologies like AI, ML and cloud computing. In this paper, we propose an idea to develop a chatbot for fitness management using IBM Watson and integrate it with a web application. We proposed using Natural Language Processing (NLP) and Natural Language Understanding (NLU) along with frameworks of IBM Cloud Watson provided for the Chatbot Assistant. This software uses a serverless architecture to combine the services of a professional by offering diet plans, home exercises, interactive counseling sessions, fitness recommendations.

Motivation & Objective

• To address the fitness industry's slow adoption of AI and cloud technologies by developing an intelligent, automated fitness management solution.
• To leverage IBM Watson's NLP and NLU capabilities for natural, human-like interaction in fitness guidance.
• To design a scalable, serverless architecture that integrates chatbot services with a web application for real-time fitness recommendations.
• To provide personalized, on-demand fitness support including diet plans, home workouts, and counseling sessions.
• To reduce reliance on human service agents by automating fitness consultation and management through AI.

Proposed method

• The system uses IBM Watson Assistant to process natural language inputs and understand user queries related to fitness and nutrition.
• It integrates with a web application via a serverless architecture to deliver dynamic, context-aware responses.
• Natural Language Understanding (NLU) is employed to extract intents and entities from user messages, such as exercise type or dietary preferences.
• The chatbot is trained on domain-specific fitness and nutrition data to improve accuracy in recommendations.
• A modular design allows for pluggable services, including diet planning, exercise routines, and counseling sessions.
• The system leverages IBM Cloud's infrastructure for hosting and scaling the chatbot without managing servers.

Experimental results

Research questions

• RQ1 How can a chatbot powered by IBM Watson improve accessibility and personalization in fitness management?
• RQ2 To what extent can NLP and NLU techniques enable accurate interpretation of diverse fitness-related user queries?
• RQ3 What is the feasibility of deploying a serverless, cloud-based chatbot for continuous, real-time fitness guidance?
• RQ4 How effectively can an AI-driven system deliver tailored diet and exercise recommendations without human intervention?
• RQ5 What are the architectural and integration challenges in combining a chatbot with a web-based fitness application?

Key findings

• The chatbot successfully interprets a wide range of fitness-related queries using IBM Watson's NLU, enabling accurate intent detection and entity extraction.
• The serverless architecture ensures low-latency responses and high scalability, suitable for production deployment.
• Users received personalized fitness and diet recommendations based on their input, demonstrating the system's adaptability.
• The integration with a web application enabled a seamless, interactive experience for end users.
• The system reduced dependency on human agents by automating common fitness consultation tasks.
• The solution demonstrates the viability of AI-driven chatbots in the fitness domain using existing cloud-based NLP platforms.