Treadmill Control Using Wearable Feedback and LLM Personalization
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Solution Overview
Problem
Treadmills in the related art lack personalized training plans based on users' individual physical conditions and health features, struggle to provide real-time feedback, and fail to dynamically adjust operating states based on users' current physical condition, leading to suboptimal workout effectiveness and potential health risks.
Innovation Solution
A treadmill control method utilizing a smart wearable device to collect user data, a pre-adapted large language model for natural language processing, and a pre-trained decision-making agent to generate personalized treadmill control policies, providing real-time guidance and adjustments based on training objectives, health data, and dynamic physiological data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If basic speed adjustment and incline control functions are provided, then the treadmill can operate, but users cannot achieve personalized training based on their physical conditions and fitness goals
Solution Approach 1:
The system segments the control architecture into multiple specialized modules: a data acquisition module for collecting physiological data, a large language model module for natural language processing and instruction generation, a decision-making agent module for policy generation, and an execution module for treadmill control. Each module handles specific tasks, enabling personalized training without requiring the entire system to be overly complex.
Solution Approach 2:
The patent introduces intermediary components including a natural language processing layer that translates user instructions into structured commands, and a decision-making agent that acts as a mediator between raw physiological data and control actions. These intermediaries bridge the gap between basic treadmill functions and personalized training requirements.
2Ease of operation
If users manually set treadmill parameters based on their physical conditions, then some control is achieved, but it is difficult for users lacking fitness knowledge and cannot maintain optimal workout effectiveness
Solution Approach 1:
The system implements self-service by enabling the treadmill to automatically collect physiological data through integrated sensors, process this data through the large language model and decision-making agent, and adjust parameters without user intervention. The system serves itself by making intelligent decisions based on real-time feedback from wearable devices.
Solution Approach 2:
The patent establishes a closed-loop feedback system where physiological data from wearable devices continuously feeds back to the decision-making agent, which adjusts treadmill parameters in real-time. This feedback mechanism ensures workout effectiveness is maintained by dynamically responding to changes in user physical condition.
3Adaptability or versatility
If treadmill parameters are statically configured, then the system is simple to operate, but it cannot dynamically adjust to users' current physical condition during exercise
Solution Approach 1:
The system transitions from static parameter configuration to dynamic adjustment by implementing real-time physiological monitoring through wearable devices. The decision-making agent continuously processes streaming data and dynamically modifies treadmill parameters during exercise sessions, enabling the system to adapt to changing physical conditions.
Solution Approach 2:
The patent applies preliminary action by pre-adapting the large language model with sports domain knowledge and pre-training the decision-making agent with exercise simulation data before actual use. This preparation enables the system to quickly respond to dynamic conditions without requiring complex real-time decision-making architecture.
4Ease of operation
If natural language processing is implemented for user instructions, then user-friendly interaction is achieved, but complex parsing and understanding require advanced AI models
Solution Approach 1:
The patent applies preliminary action by pre-adapting the large language model with sports domain knowledge and pre-training it on exercise-related instructions before deployment. This advance preparation enables the model to effectively parse and understand natural language instructions without requiring excessively complex architecture during actual operation.
Data Source
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AI summary
A treadmill control method is applied to controlling a treadmill and includes obtaining (S101) a target user's training objective information, user health data, and static physiological data; collecting (S102) the target user's dynamic physiological data through a smart wearable device; generating (S103) instructional language based on the training objective information, the user health data, the static physiological data, and the dynamic physiological data to obtain a natural language instruction; performing (S104), by a pre-adapted target large language model, inferencing and parsing of the natural language instruction to obtain user profile parsing information; generating (S105) a treadmill control strategy adapted to the target user based on the user profile parsing information using a pre-trained decision-making agent; and controlling (S106) the treadmill's operating state based on the treadmill control strategy to provide personalized operating modes for different users.