Workout Schedule Generation via Inverse Reinforcement Learning
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Solution Overview
Problem
Existing workout scheduling techniques fail to consider various states related to a workout, leading to impracticable or physically challenging exercises in specific workout facilities or based on the individual's physical strength.
Innovation Solution
A workout support apparatus and method that utilize data acquisition and optimization calculations with an objective function generated by inverse reinforcement learning, taking into account state data such as workout facility constraints and individual physical capabilities to generate tailored workout schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a workout schedule is generated using conventional techniques based on statistics databases and mathematical models, then a workout schedule can be provided, but the schedule may include exercises that are impracticable in the workout facility or difficult to implement considering the targeted person's physical strength
Solution Approach 1:
The system performs preliminary analysis of the workout facility environment and the targeted person's physical state before generating the workout schedule. By acquiring facility information (equipment availability, space constraints) and individual data (physical strength, health conditions) in advance, the system can pre-filter and adapt exercise selections to ensure practicability, rather than generating generic schedules and hoping they work.
Solution Approach 2:
The system tailors the workout schedule to the specific local conditions of the workout facility and the individual's physical characteristics. Instead of applying a uniform exercise selection criteria, the system adjusts exercise difficulty, type, and sequence based on the particular facility's equipment and the individual's specific physical state, making each schedule locally optimized rather than universally applicable.
2Device complexity
If generic workout schedules are generated without considering specific states, then generation complexity is reduced, but the workout effectiveness and safety are compromised
Solution Approach 1:
The system incorporates feedback loops where the generated workout schedules are evaluated against the acquired facility and individual state data. The system uses this feedback to iteratively refine and adjust exercise selections, ensuring that safety and effectiveness requirements are met while maintaining reasonable system complexity through automated evaluation criteria.
Solution Approach 2:
The system dynamically adjusts workout schedule parameters (exercise intensity, duration, frequency, type) based on the input data about facility conditions and individual physical state. By changing these parameters automatically rather than manually designing complex schedules, the system achieves high reliability without proportionally increasing generation complexity.
Data Source
AI summary
In order to generate a workout schedule in consideration of a state regarding a workout, a workout support apparatus (2) includes: a data acquiring section (21) for acquiring state data which indicates a state regarding a workout done by a targeted person; and a generating section (22) for generating a workout schedule in accordance with the state indicated by the state data, by performing an optimization calculation with use of an objective function, the objective function being generated by inverse reinforcement learning with use of training data which indicates a workout schedule that is in accordance with a state regarding a workout and that is to be applied in the state.