Hybrid Wireless Wearable Tracking for Low-Power Indoor-Outdoor Coverage
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Solution Overview
Problem
Conventional data-acquiring devices for monitoring individuals with dementia or other health conditions require significant electrical energy consumption for long-range data transmission, leading to battery life issues and limited detection range, making them unsuitable for continuous tracking both indoors and outdoors.
Innovation Solution
A wearable electronic device with a hybrid wireless communication module that includes sub-modules for beacon, GNSS, and LPWAN signals, allowing selective data receipt and transmission, coupled with sensors for environmental, activity, and physiological data, and a machine learning model for health condition analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If conventional data-acquiring devices use significant electrical energy for long-range data transmission, then transmission range is improved, but battery life deteriorates
Solution Approach 1:
The communication system is segmented into three distinct sub-modules: beacon sub-module for indoor location tracking, GNSS sub-module for outdoor satellite-based location, and LPWAN sub-module for low-power wide-area network transmission. Each sub-module handles specific transmission tasks with optimized power consumption characteristics, allowing the system to achieve long-range coverage without requiring continuous high-power operation from a single module.
Solution Approach 2:
The system employs periodic action by selectively activating different communication sub-modules based on the device's operational state and location requirements. The controller activates appropriate sub-modules only when needed, rather than maintaining all modules in continuous operation, thereby reducing overall power consumption while preserving long-range transmission capability when required.
2Duration of action of stationary object
If devices use conventional Bluetooth and WiFi for long battery life, then battery life is improved, but detection range deteriorates
Solution Approach 1:
The wearable device integrates multiple communication sub-modules (beacon, GNSS, and LPWAN) that each serve different functional purposes and operational scenarios. This multi-functional communication architecture allows the device to achieve both long battery life through low-power operation and extended detection range through the combined capabilities of the various sub-modules, with each contributing to overall system performance based on contextual needs.
3Use of energy by moving object
If devices are designed for indoor tracking only, then power consumption is reduced, but outdoor tracking capability deteriorates
Solution Approach 1:
The system dynamically adapts its communication capabilities based on the operational environment. The controller selectively activates the beacon sub-module for indoor tracking, the GNSS sub-module for outdoor location services, or the LPWAN sub-module for low-power wide-area communication. This dynamic adaptation allows the device to maintain low power consumption for indoor operations while gaining the ability to perform outdoor tracking when needed, without requiring all functionality to be active simultaneously.
Data Source
AI summary
A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.