Exercise Posture Detection Mat Using Segmented Sensor Matrix
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Solution Overview
Problem
Existing exercise posture detection systems are inefficient and unreliable due to the complexity and cost associated with larger sensor matrices, which require high power consumption and generate excessive outputs, making them expensive and difficult to manufacture, and often fail to provide accurate real-time guidance to users.
Innovation Solution
A system comprising a mat communicatively coupled with wearable devices, featuring a sensor matrix with sensors that generate electrical signals upon contact, and a processing subsystem that extracts and processes data from sensors and wearable devices to determine exercise posture and strength, using a predefined ratio of sensor lines and power lines to optimize data acquisition and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger sensor matrix is used to improve posture detection accuracy, then measurement precision is improved, but device complexity and power consumption increase exponentially
Solution Approach 1:
The patent divides the sensor matrix into multiple smaller sub-matrices or sensor groups, where each sub-matrix handles a specific region or aspect of posture detection. This segmentation reduces the complexity of acquiring and processing data from all sensors simultaneously, while maintaining overall detection accuracy through coordinated data from multiple segments.
Solution Approach 2:
The system implements periodic or sequential activation of sensor groups rather than continuous activation of all sensors. By activating subsets of sensors in alternating time intervals, the system reduces power consumption and data processing complexity while still achieving comprehensive posture monitoring over time through the aggregated periodic measurements.
2Measurement precision
If a larger sensor matrix is used to improve posture detection accuracy, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The system implements periodic or sequential activation of sensor groups rather than continuous activation of all sensors. By activating subsets of sensors in alternating time intervals, the system reduces power consumption and data processing complexity while still achieving comprehensive posture monitoring over time through the aggregated periodic measurements.
Solution Approach 2:
The patent activates only the necessary subset of sensors required for detecting the current exercise posture type rather than keeping all sensors active. This partial action approach reduces power consumption by keeping unused sensors in low-power states while maintaining sufficient detection accuracy for the specific exercise being performed.
3Measurement precision
If all sensors are activated to recognize user posture, then measurement precision is improved, but the system becomes less reliable due to excessive power consumption
Solution Approach 1:
The patent assigns different activation states to different sensor groups based on the specific exercise type and user position. Sensors in regions relevant to the current exercise receive full power and activation, while sensors in irrelevant regions remain in low-power or inactive states. This local quality approach ensures reliable posture recognition for the active exercise while reducing overall power consumption.
Solution Approach 2:
The patent activates only the necessary subset of sensors required for detecting the current exercise posture type rather than keeping all sensors active. This partial action approach reduces power consumption by keeping unused sensors in low-power states while maintaining sufficient detection accuracy for the specific exercise being performed.
4Measurement precision
If a larger number of sensors are used to improve posture detection, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent divides the sensor matrix into multiple smaller sub-matrices or sensor groups, where each sub-matrix handles a specific region or aspect of posture detection. This segmentation reduces the complexity of acquiring and processing data from all sensors simultaneously, while maintaining overall detection accuracy through coordinated data from multiple segments.
Solution Approach 2:
The sensor matrix is designed with multi-functional sensors that can detect multiple parameters (pressure, touch, position) and serve multiple exercise detection purposes. This universality allows a smaller number of versatile sensors to replace what would otherwise require a larger number of specialized sensors, reducing manufacturing cost while maintaining detection precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate and efficient exercise posture monitoring and guidance, reducing power consumption and complexity, allowing for precise determination of user posture without relying on external cameras, and offering a non-intrusive method for users to improve their exercise performance.
Implementation Method 1
Each of the at least one sensor matrix includes one or more sensors, wherein the one or more sensors is configured to generate an electrical signal upon making a contact of at least one part of a body of the user with the mat
Data Source
AI summary
A system and non-intrusive method for exercise posture detection are provided. The system includes a mat, at least one sensor matrix comprising one or more sensors and configured to generate an electrical signal upon making a contact by the user with the mat, a plurality of sensor lines, a plurality of power lines and a processing subsystem. The processing subsystem includes a data acquisition module configured to extract a first set of data, to extract a second set of data; a data processing module to process the first set of data and the second set of data, to concatenate the first set of data and the second set of data to get a final set of data, to compare the final set of data with a pre-defined set of posture data, to determine the posture of the at least one exercise.