Heart Rate Controlled Drone for Runner Guidance
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Solution Overview
Problem
Current technologies do not effectively utilize drones to enhance the running experience by providing personalized guidance and support based on real-time heart rate and location data of runners.
Innovation Solution
A method involving a processor that receives heart rate and location messages from a remote device worn by the runner, predicts the runner's future location, generates a target location for the drone, and commands it to move accordingly, allowing the drone to provide guidance, pacing, and filming support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the drone uses basic location tracking to follow the runner, then the drone can maintain general proximity, but it cannot anticipate the runner's future position and provide personalized guidance
Solution Approach 1:
The system performs preliminary actions by predicting the runner's future location based on current heart rate and location data before the runner actually reaches that position. This allows the drone to proactively move to the predicted location and provide guidance in advance, rather than simply reacting to the runner's current position. The prediction mechanism enables the drone to anticipate needs and prepare appropriate responses beforehand.
Solution Approach 2:
The system implements continuous feedback by constantly receiving heart rate messages and location updates from the runner's remote device. This real-time feedback loop allows the drone to adjust its behavior dynamically based on the runner's physiological state and movement patterns. The feedback mechanism enables personalized guidance by adapting to the runner's actual condition rather than following a predetermined path.
2Loss of time
If the drone follows the runner in real-time without prediction, then the drone can maintain current position accuracy, but it cannot provide forward-looking guidance and pacing support
Solution Approach 1:
The system performs preliminary actions by predicting the runner's future location based on current heart rate and location data before the runner actually reaches that position. This allows the drone to proactively move to the predicted location and provide guidance in advance, rather than simply reacting to the runner's current position. The prediction mechanism enables the drone to anticipate needs and prepare appropriate responses beforehand.
Solution Approach 2:
The system uses the runner's own physiological data (heart rate) and movement patterns to generate predictions about future behavior. The runner essentially serves their own prediction needs by providing the raw data through their remote device, eliminating the need for external observation or intervention. This self-service approach improves response time while maintaining reasonable prediction accuracy.
3Adaptability or versatility
If the drone uses simple location following, then the control system remains simple, but it cannot offer personalized guidance based on heart rate data
Solution Approach 1:
The system achieves multi-functionality by using the same heart rate and location data for multiple purposes: predicting future location, determining pacing guidance, and providing overall running support. This universal use of data maximizes the value extracted from the collected information without requiring separate sensing systems for each function. The processor leverages the same input data stream to provide diverse personalized services.
Solution Approach 2:
The system monitors changes in heart rate parameters and location coordinates over time to predict future states. By detecting and analyzing parameter changes in the incoming data stream, the system can adapt its predictions and guidance accordingly. This parameter-based approach enables personalization without requiring complex machine learning models, as it relies on straightforward analysis of how the measured parameters are changing.
Data Source
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AI summary
A method for controlling a drone including performing operations on a processor configured to control location of the drone are described. The operations on the processor include receiving heart rate messages from a remote device carried by a user, where each heart rate message includes heart rate information of the user, and receiving location messages from the remote device carried by the user, where each location message includes location information of the user. The method includes predicting a future location of the user based on the heart rate messages and the location messages, generating a target location to which the drone is to be moved based on the future location of the user, and commanding the drone to move to the target location. Related devices are disclosed.