BLE User Counting for Indoor Movement Trend Detection
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Solution Overview
Problem
Existing location-based services face challenges in accurately detecting user counts and movement trends, particularly in indoor spaces where GPS signals are weak, and require user consent for personal information usage.
Innovation Solution
A method and apparatus using Bluetooth Low Energy (BLE) communication to collect smart device count information, calculate user counts, and generate location estimation and movement trend information without relying on GPS or sensitive personal data, utilizing a network of smart device counting apparatuses and a user information generation apparatus to process and combine data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS-based location-based services are used, then location information can be obtained, but GPS signals cannot reach indoor spaces or tunnels
Solution Approach 1:
The patent introduces BLE signals as an intermediary technology to bridge the gap between GPS and indoor location detection. BLE devices act as mediators that can operate in both outdoor GPS-covered areas and indoor GPS-denied areas, enabling seamless location-based services across different environments without requiring separate systems.
Solution Approach 2:
The patent creates a universal location detection system that can function in multiple environments (outdoor and indoor) using a single integrated approach. The system uses GPS when available and automatically transitions to BLE-based detection when GPS signals are unavailable, providing multi-functional capability across diverse spatial contexts.
2Measurement precision
If personal information is collected for user count detection, then accurate user information can be obtained, but user consent requirements increase complexity
Solution Approach 1:
The patent extracts and removes sensitive personal information from the location-based service system. Instead of collecting user identities, names, or personal data, the system only collects anonymous device signals and location information, thereby maintaining measurement precision while eliminating the need for complex user consent management and personal information protection mechanisms.
Solution Approach 2:
The patent uses temporary, anonymous device identifiers that are discarded after use rather than storing persistent personal information. Each BLE device provides transient identification data that enables accurate counting without creating long-term personal records, simplifying the system by eliminating the need for complex data management and consent storage infrastructure.
3Ease of manufacture
If BLE-based user count detection is implemented, then user count and movement trend can be detected with low cost, but detection range is limited to narrow areas
Solution Approach 1:
The patent divides the large detection area into multiple smaller zones, each covered by individual BLE devices. By segmenting the overall service area into manageable sections and deploying multiple low-cost BLE detectors throughout the space, the system achieves broad coverage while maintaining the cost-effectiveness of individual BLE devices. The segmented approach allows strategic placement of devices to maximize detection range.
Solution Approach 2:
The patent merges data from multiple BLE devices to achieve extended detection coverage. By combining the detection capabilities of several narrow-range BLE devices positioned throughout the area, the system creates a unified wide-area detection network that maintains the low cost of individual devices while achieving comprehensive spatial coverage.
Data Source
AI summary
A user information generation method and apparatus is proposed. The method may include collecting, from at least one smart device counting apparatus, smart device count information generated using a signal transmitted by at least one smart device within a predetermined range. The method may also include generating user count information as a result of calculating a number of users within the range by applying an average number of smart devices possessed by each person to the smart device count information. The method may further include generating location estimation information about the smart device, based on the smart device count information, and generating movement trend detection information about users based on the location estimation information about the smart device, the user count information, and map information.