Wearable Data Hub for Group Sync and Building Management
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Solution Overview
Problem
Existing technologies face challenges in efficiently syncing data from wearable devices across multiple manufacturers in group settings, such as athletic teams or military units, and in managing energy usage and indoor air quality in large spaces.
Innovation Solution
A hub system that automatically syncs and uploads data from multiple wearable devices to a performance data management system, eliminating the need for intermediary devices and enabling proximity-based data collection. Additionally, the hub integrates with building management systems to optimize energy usage and manage indoor air quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional syncing methods using mobile phones or USB connections are used, then data can be transferred from wearable devices, but the process requires manual user intervention and is not scalable to large groups
Solution Approach 1:
The patent introduces a hub device as an intermediary between wearable devices and the cloud database. The hub automatically receives data from multiple wearables via Bluetooth and uploads to the cloud, eliminating the need for manual phone-based syncing. This intermediary system enables automated batch processing of data from large groups without requiring user intervention for each device.
Solution Approach 2:
The hub system operates autonomously by automatically discovering, connecting to, and data-syncing with wearable devices in its proximity. The system performs self-service data collection without requiring users to manually initiate sync operations, making the process scalable to large groups while maintaining ease of operation.
2Reliability
If each wearable device communicates only with its manufacturer's database, then device-specific data integrity is maintained, but an integrated view of data from all devices requires accessing multiple cloud databases
Solution Approach 1:
The patent merges data from multiple manufacturer-specific cloud databases into a single integrated view at the hub or client application level. The hub collects data from various wearables and consolidates them, allowing users to access comprehensive team or group data in one place rather than navigating multiple separate manufacturer databases.
Solution Approach 2:
The hub system serves as a universal data collection point that can interface with wearable devices from different manufacturers. It provides a multi-functional platform that handles data aggregation, validation, and integration from diverse sources, enabling a unified view across heterogeneous device ecosystems.
3Productivity
If automated hub-based syncing is implemented for large groups, then data collection efficiency improves, but infrastructure requirements and initial setup complexity increase
Solution Approach 1:
The hub automatically performs device discovery, connection establishment, and data synchronization without requiring manual configuration. It self-manages the complexity of interfacing with multiple wearable types and cloud services, presenting a simple interface to users while handling infrastructure complexity internally.
Solution Approach 2:
The hub acts as an intermediary that absorbs and manages infrastructure complexity, shielding users from technical details. It handles device pairing, data formatting, and cloud communication, transforming a complex multi-device synchronization problem into a simple automated process.
Data Source
AI summary
A method for training individuals includes receiving physiological data from wearable devices worn by an individual; generating a personalized training program based on the received physiological data; transmitting instructions for the personalized training program to a user device associated with the individual; continuously monitoring real-time physiological data from the wearable devices during execution of the training program; analyzing the real-time physiological data to determine performance metrics; providing real-time feedback to the individual based on the performance metrics; and dynamically adjusting the personalized training program based on the analyzed real-time physiological data and performance metrics.