Music Recommendation Model for Exercise Capacity Enhancement
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Solution Overview
Problem
There is a lack of examination on the influence of music on exercise capacity, and no known technique exists for obtaining recommendation information for music that can temporarily enhance a user's exercise capacity.
Innovation Solution
A learning apparatus and recommender apparatus that utilize training data including piece-of-music information, biological information of users before listening, and index values of exercise capacity improvement to learn an estimation model. This model recommends pieces of music expected to have the largest effect on improving exercise capacity based on the user's biological information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional studies on music influence are conducted, then understanding of music's effect on internal organs and nerves is improved, but no information is obtained on music's effect on exercise capacity
Solution Approach 1:
The system performs preliminary actions by collecting training data in advance that includes piece-of-music information, biological information, and exercise capacity improvement indices. This pre-collected data enables the subsequent recommendation system to function without requiring complex real-time analysis, thus resolving the contradiction between obtaining new information and maintaining system simplicity.
Solution Approach 2:
The patent introduces an estimation model as an intermediary between the input biological information and the output music recommendations. This model acts as a mediator that processes the relationship between music, biology, and exercise capacity, allowing the system to provide exercise capacity improvement information without requiring direct complex measurements or experiments during operation.
2Adaptability or versatility
If a recommendation system for exercise capacity enhancement is developed, then useful music recommendations are provided, but system complexity increases due to need for biological information collection and analysis
Solution Approach 1:
The system utilizes changes in biological parameters (such as heart rate, respiration) as input to the estimation model. By monitoring these parameter changes before and during music listening, the system can provide personalized music recommendations for exercise capacity enhancement without requiring complex intervention mechanisms, thus achieving adaptability while controlling system complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where the estimation model uses biological information as input and generates music recommendations based on observed exercise capacity improvement. This feedback loop allows the system to adapt to individual user responses and refine recommendations over time, achieving personalization through a relatively simple closed-loop structure rather than complex predictive algorithms.
Data Source
AI summary
From information for specifying a piece of music, biological information on a user before listening to the piece of music, and information regarding improvement in exercise capacity of the user, for a plurality of pieces of music, learning is performed of an estimation model that uses biological information as an input and obtains information on a piece of music estimated to have the largest effect of improving exercise capacity of the user in a state of the input biological information or information on a predetermined number of pieces of music in order from the piece of music estimated to have the largest effect, and by using the estimation model obtained by learning, on the basis of biological information on a user who is a target of recommendation of a piece of music, information on a piece of music estimated to have the largest effect of improving exercise capacity when the user is in a state of the biological information, or information on a predetermined number of pieces of music in order from the piece of music estimated to have the largest effect is obtained as a recommendation result.