Wearable Sensor Bed-Time Detection Using Posture and Activity
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Solution Overview
Problem
Conventional bed-time monitoring technologies using sensors on beds are limited by high costs, maintenance requirements, inaccuracy for under/normal weight individuals, and incorrect sensor placement, leading to false positives and negatives in determining bed-entry and bed-exit events.
Innovation Solution
A wearable sensor device that detects physiological signals, including posture angles and activity levels, using a wireless, portable system with embedded algorithms to accurately determine bed-time periods by combining posture angle calculations with activity metrics, thereby reducing false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensors (pressure, force, temperature, movement) are mounted on beds to monitor sleep duration and timing, then bed-time monitoring capability is provided, but the system suffers from high expense, wired integration requirements, periodic maintenance needs, exposure to fluids, decreased accuracy for under/normal weight people, and incorrect sensor placement requirements
Solution Approach 1:
The patent replaces mechanical/physical sensors (pressure, force, temperature, movement sensors mounted on beds) with a wireless wearable device that uses accelerometers and signal processing algorithms to detect bed-entry and bed-exit events. This substitution eliminates the need for wired integration, complex sensor placement, and periodic maintenance while maintaining monitoring accuracy.
Solution Approach 2:
The patent creates a portable copy of the monitoring functionality by placing a wireless wearable device on the user's body, which replicates the bed-time monitoring capability without requiring infrastructure changes to the bed or room. This copying approach simplifies the system while maintaining measurement precision.
2Reliability
If conventional bed-mounted sensors are used to detect bed-entry and bed-exit events, then sleep monitoring is enabled, but false positives and negatives occur due to incorrect sensor placement and decreased accuracy for under/normal weight people
Solution Approach 1:
The wearable device automatically detects bed-entry and bed-exit events by monitoring the user's own movement and posture changes, eliminating the need for external sensor placement on the bed. The device serves itself by using its own sensors to detect events, which improves reliability and removes placement complexity.
Solution Approach 2:
The patent moves the sensing dimension from the bed (stationary reference frame) to the user's body (moving reference frame). By detecting events relative to the user's movement rather than the bed's position, the system achieves higher reliability and eliminates placement issues associated with bed-mounted sensors.
3Quantity of substance
If wired integrated sensor systems are deployed for sleep monitoring, then comprehensive data collection is achieved, but maintenance requirements and system complexity increase
Solution Approach 1:
The patent replaces wired sensor systems with a wireless wearable device that collects physiological signals (acceleration, posture, activity level) and transmits data wirelessly. This eliminates periodic maintenance requirements such as wire checking, sensor recalibration, and system reintegration while maintaining comprehensive data collection capabilities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The wearable device provides continuous and accurate monitoring of bed-time periods, enhancing the precision of bed-entry and bed-exit event detection, reducing false alarms and improving overall sleep analysis and health assessment.
Implementation Method 1
a plurality of sensors, including a tri-axial accelerometer, to detect a plurality of physiological signals of the user
Data Source
AI summary
A method and system for automatically determining bed-time periods are disclosed. The method comprises detecting at least one physiological signal, determining a posture angle and an activity level using the at least one detected physiological signal, and determining a bed-time period using both the posture angle and the activity level. The system includes at least one sensor to detect a plurality of physiological signals, a processor coupled to the at least one sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to detect a posture angle using at least one of the plurality of detected physiological signals and to determine a bed-time period using both the posture angle and the activity level.