Wearable Device Fall-Off Detection Using Physiological Data
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Solution Overview
Problem
Wearable devices currently lack the ability to determine the scenario mode of a wearer when they fall off, which is crucial for assessing the danger status and sending appropriate indications to external systems.
Innovation Solution
A fall-off detection method and device that collect physiological parameter information and activity information, analyze electrocardiogram signals, and determine scenario modes based on abnormality detection and location, sending corresponding indications via a radio frequency circuit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the wearable device collects and analyzes physiological parameter information and activity information to determine scenario modes, then the accuracy of fall-off detection and safety assessment is improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple sensors (physiological parameter sensor, activity sensor, location sensor) and integrates their data through a processor to determine scenario modes. This merging of multiple data sources improves detection accuracy while managing complexity through unified processing architecture.
Solution Approach 2:
The wearable device is designed with multi-functional capabilities, serving not only as a fitness tracker but also as a safety monitoring system that can detect fall-off, determine scenario modes, and send alerts. This multi-functionality justifies the added complexity by providing comprehensive safety features.
2Reliability
If the wearable device sends indications to external systems upon fall-off detection, then the safety response capability is improved, but the energy consumption increases
Solution Approach 1:
The device performs preliminary analysis of physiological and activity data to determine scenario modes before sending alerts. This preliminary action filters out false alarms and ensures that energy-intensive alert transmissions are only initiated when genuine safety concerns are detected, optimizing energy usage.
Solution Approach 2:
The system establishes a feedback loop where physiological and activity data continuously monitor wearer status, trigger scenario mode determination upon fall-off detection, and initiate appropriate responses. This feedback mechanism ensures reliable safety monitoring while managing energy consumption through condition-based activation.
3Measurement precision
If the wearable device analyzes electrocardiogram signals and determines scenario modes based on abnormality detection, then the accuracy of danger assessment is improved, but the processing time increases
Solution Approach 1:
The system performs partial analysis of electrocardiogram signals by focusing on specific abnormality indicators rather than comprehensive continuous analysis. This selective approach maintains accurate danger assessment while reducing processing time and computational burden.
Solution Approach 2:
The wearable device continuously monitors physiological parameters and activity levels, maintaining readiness for immediate scenario mode determination upon fall-off detection. This continuous monitoring eliminates detection delays while the processing time for analysis remains minimized through efficient algorithms.
Data Source
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AI summary
Embodiments of the present invention disclose a fall-off detection method for a wearable device and a wearable device. The method includes: collecting physiological parameter information and activity information of a wearer of the wearable device; when an abnormality is detected in the physiological parameter information of the wearer of the wearable device and a time of the abnormality exceeds a preset time, determining that the wearable device falls off from the wearer; when it is determined that the wearable device falls off from the wearer, determining, according to the collected physiological parameter information and activity information of the wearer of the wearable device, a scenario mode that the wearer is in; and sending, according to the scenario mode that the wearer is in, an indication corresponding to the scenario mode. By means of the method, when a wearable device falls off, a scenario mode that a wearer is in can be determined, and an indication can be sent to an external system.