Wearable Activity Metric Calculation via Sensor Data Segmentation
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Solution Overview
Problem
Current wearable computing devices lack an efficient method to accurately measure and analyze physiological parameters and activity metrics in real-time, especially when combined with demographical data, which limits their ability to provide personalized and comprehensive health monitoring and feedback to users.
Innovation Solution
A wearable device equipped with sensors and processors that measure physiological parameters and receive activity metrics, which are then calculated and ranked using demographical data, allowing for real-time feedback and analysis, and a server system that compiles and processes data from multiple devices to provide personalized insights and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices collect and process physiological parameter data locally, then measurement precision and real-time feedback are improved, but device complexity and power consumption increase
Solution Approach 1:
The system divides the data processing function into two segments: the wearable device collects and transmits physiological parameter data, while the external device performs the complex activity metric calculation. This segmentation allows the wearable device to remain simple while still achieving accurate measurements through external processing.
Solution Approach 2:
The patent introduces an external device as an intermediary between the wearable device and the user. The external device receives physiological parameter data from the wearable device, combines it with demographical data, calculates activity metrics, and provides feedback. This intermediary handles the complex processing while the wearable device remains relatively simple.
2Adaptability or versatility
If wearable devices integrate multiple sensors and processing capabilities, then functionality and personalization are improved, but ease of operation and user burden increase
Solution Approach 1:
The system implements self-service by automatically collecting physiological parameter data through sensors, retrieving demographical data, calculating activity metrics, and providing feedback without requiring manual user input. The user simply wears the device and receives personalized health monitoring results automatically.
Solution Approach 2:
The external device serves multiple functions: it receives physiological parameter data from the wearable device, retrieves demographical data, calculates activity metrics, and provides feedback. This multi-functionality consolidates complex operations into a single external device, simplifying the user experience.
3Productivity
If activity metrics are calculated using only physiological parameter data, then calculation speed is improved, but measurement precision and personalization deteriorate
Solution Approach 1:
The system performs preliminary action by collecting and storing demographical data in advance. When calculating activity metrics, the system combines this pre-collected demographical data with physiological parameter data, enabling accurate and personalized calculations without delaying the process.
Data Source
AI summary
A wearable device described herein includes a housing and a mount configured to mount the housing to an external surface of a wearer. The wearable device further includes one or more sensors configured to measure at least one physiological parameter of the wearer. The wearable device may obtain an activity metric that is based on at least one physiological parameter of the wearer measured by the one or more sensors and demographical data specific to the wearer. In some examples, the wearable may be configured to calculate the activity metric or a preliminary activity metric and to indicate the activity metric and/or preliminary activity metric to the wearer. In some examples, the wearable device may transmit the physiological parameter measurement to an external device and receive an indication of the activity metric from the external device.