Co-located Mobile Device Sensor Fusion for Pedestrian Tracking
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Solution Overview
Problem
Mobile devices co-located with a person may face inaccuracies in movement tracking due to unreliable measurements from inertial sensors, especially when signal-based location services are unavailable.
Innovation Solution
A method where a mobile device collaborates with a wearable electronic device to obtain and combine sensor data from both devices, using accelerometers and gyroscopes to infer pedestrian movement parameters like speed and heading by analyzing periodicity and phase of sensed movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inertial sensor based positioning is used in a mobile device, then location services can be provided when signal-based services are unavailable, but the measurements from inertial sensors may not accurately reflect the movements of the object
Solution Approach 1:
The patent combines measurements from multiple inertial sensors located at different points on the same object (person). By merging data from sensors at different locations, the system achieves both reliable location service availability and improved measurement precision, as the combined measurements better represent the overall movement of the object
2Measurement precision
If multiple sensors from multiple devices are combined to improve movement tracking accuracy, then measurement precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The system designates one mobile device as a coordinator that aggregates and processes measurements from multiple devices. This intermediary approach simplifies the overall system architecture by centralizing the complexity in one device while allowing other devices to operate more simply, thus improving measurement precision without proportionally increasing overall system complexity
3Measurement precision
If multiple sensors from multiple devices are combined to improve movement tracking accuracy, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system selectively activates and uses measurements from multiple sensors only when needed, rather than continuously operating all sensors. This partial action approach improves measurement precision during critical periods while minimizing overall energy consumption by keeping sensors dormant or inactive during periods when high precision is not required
Data Source
AI summary
Example techniques are provided that may be implemented, at least in part, at a mobile device to determine certain parameters corresponding to movement of an object that is co-located with the mobile device and at least one other mobile device. In an example implementation, a mobile device may obtain measurements corresponding to sensors of a plurality of mobile devices co-located on an object, and determine at least one of an estimated speed of the object, an estimated heading of the object, or an estimated heading rate of the object based, at least in part, on all or a selected subset of the sensor measurements which are accepted for use.