Adaptive Exercise Playlist Using Real-Time Biometric Feedback
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Solution Overview
Problem
Existing exercise video platforms fail to personalize each individual exercise of a workout class to the user's real-time physical abilities and preferences, leading to workouts that may be too easy or too difficult.
Innovation Solution
A system that generates a personalized exercise playlist by modifying segments in real-time based on user biometric data, spatial constraints, and other factors, using sensors to adjust exercise intensity and equipment settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static workout class is provided to all users, then the workout structure remains simple and consistent, but the exercise intensity does not adapt to individual user fitness levels
Solution Approach 1:
The workout system transitions from a static, fixed sequence of exercises to a dynamic, adaptive playlist that automatically adjusts exercise intensity and selection based on real-time biometric feedback. The system monitors user responses and dynamically modifies the workout plan to match individual fitness levels.
Solution Approach 2:
The system changes key parameters of the workout including exercise intensity level, exercise selection, and playlist structure based on monitored biometric data such as heart rate, calories burned, and exercise performance metrics. These parameter changes enable the workout to adapt to each user's current fitness state.
2Measurement precision
If biometric data is collected and processed in real-time, then exercise personalization is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system continuously monitors biometric data from sensors and uses this feedback to adjust the workout playlist in real-time. The feedback loop measures user performance, compares it against target metrics, and automatically modifies exercise intensity or selection to optimize the workout experience.
Solution Approach 2:
The system automatically processes biometric data and adjusts the workout plan without requiring manual user input or intervention. The automated processing of sensor data enables the system to self-regulate and adapt the exercise playlist based on real-time physiological measurements.
3Reliability
If the workout playlist is dynamically adjusted based on user performance, then exercise effectiveness is improved, but the ability to maintain consistent workout structure is reduced
Solution Approach 1:
The workout is divided into discrete, modular exercise segments that can be independently selected and adjusted. This segmentation allows the system to maintain overall workout structure while dynamically adjusting individual exercise components based on user performance and fitness level.
Solution Approach 2:
The system applies partial adjustments to the workout playlist rather than complete reconfiguration. It modifies specific exercise segments or intensity levels while preserving the overall workout framework, thereby maintaining structural consistency while improving effectiveness.
Data Source
AI summary
Systems and methods are described for generating for presentation to a user at least one modified segment corresponding to a physical exercise depicted during presentation of a media asset to a user. The media asset may comprise multiple segments and one or more exercises, where each respective exercise corresponds to one or more segments, and input (related to the user) may be received from one or more sensors during presentation of the media asset. Based on the received input, a determination may be made that an alternate version of an exercise corresponding to at least one particular segment of the multiple segments should be provided instead of a version of the exercise scheduled to be provided. The at least one particular segment may be modified to correspond to the alternate version of the exercise, and the at least one modified particular segment may be generated for presentation.