Near-Field Positioning Device for On-Body Wireless Reliability
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Solution Overview
Problem
Existing near-field wireless communication systems face challenges in ensuring robust on-body communication, as users often experience laborious and ineffective trial-and-error processes to optimize device positioning, particularly for incapacitated users, leading to potential communication link failures and safety risks.
Innovation Solution
A near-field positioning device that inputs user body-parameters and uses a calculated near-field channel loss model to recommend optimal positions for near-field wireless devices, ensuring acceptable channel loss and varying orientations, thereby enhancing communication reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually optimize device positioning through trial-and-error, then device placement can be adjusted, but the process becomes laborious and time-consuming
Solution Approach 1:
The system pre-calculates and stores optimal device positions based on user body parameters before actual device placement. The controller generates recommended positions using a near-field channel loss model that predicts optimal locations, eliminating the need for users to perform trial-and-error adjustments during actual device deployment.
Solution Approach 2:
The system automatically determines optimal device positions without requiring manual user adjustment. The controller uses the near-field channel loss model to self-optimize device placement by calculating recommended positions based on user body parameters, body topology data, and communication requirements, then guides users to place devices at these pre-determined locations.
2Reliability
If device positions are optimized for specific users, then communication reliability improves, but the system complexity increases
Solution Approach 1:
The near-field channel loss model serves multiple functions: it predicts channel loss for different body types, determines optimal device positions, and generates recommendations for various user scenarios. This single model handles diverse communication scenarios (different body topologies, device types, and positions) without requiring separate optimization systems for each case.
Solution Approach 2:
The system adapts to different users by changing key parameters in the near-field channel loss model, specifically body parameters (height, weight, body composition) and body topology data. By adjusting these parameters based on user measurements, the model generates customized optimal positions without requiring complex reconfiguration of the entire system architecture.
3Measurement precision
If the system collects detailed body parameters from users, then positioning accuracy improves, but the data collection process becomes more complex
Solution Approach 1:
The system uses an intermediary measurement approach where a smartphone or portable device captures body parameters (height, weight, body composition) through existing sensors and cameras, then transmits this data to the controller. This intermediary step simplifies the data collection process by leveraging existing devices rather than requiring specialized measurement equipment integrated into the near-field communication system.
Solution Approach 2:
The system collects and processes user body parameters in advance before device placement optimization. By gathering body parameters and generating the near-field channel loss model beforehand, the system prepares all necessary data for accurate positioning recommendations, eliminating the need for complex real-time measurements during device deployment.
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 solution effectively reduces the laborious process of optimizing device positions, improves communication reliability, and ensures safer operations, especially in medical contexts, by providing recommended positions that minimize channel loss.
Implementation Method 1
a near-field antenna having a first conductive surface and a second conductive surface; wherein the conductive surfaces are configured to carry non-propagating quasi-static near-field electric-induction (NFEI) signals exchanged within the near-field communications link
Implementation Method 2
a near-field antenna having a coil; wherein the coil is configured to carry non-propagating quasi-static near-field magnetic-induction (NFMI) signals exchanged within the near-field communications link
Data Source
AI summary
One example discloses a near-field positioning device, including: an input interface configured to receive a set of body-parameters from a user; a controller configured to generate a set of recommended positions for a set of near-field wireless devices to be coupled to the user based on the body-parameters; and an output interface configured to output the recommended positions.