Sleep Control Apparatus Using Biological Data for Awakening Timing
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Solution Overview
Problem
Existing sleep control technologies fail to accurately determine the optimal awakening time for individuals based on their sleep cycles and environmental conditions, leading to inefficient wake-up times.
Innovation Solution
A sleep control apparatus that acquires biological and environmental data to predict sleep depth and determine an optimal awakening point, using a combination of body motion sensors, environment sensors, and preference information to stimulate the individual at the appropriate time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sleep control is based on simple alarm timing, then device complexity is low, but awakening accuracy is poor
Solution Approach 1:
The patent segments the sleep control function into multiple independent modules: biological information acquisition section (21), environment information acquisition section (22), preference information acquisition section (23), determination section (31), and controller (36). Each module performs a specific function, allowing the system to achieve high awakening accuracy through coordinated operation of simple, specialized components rather than a single complex unit.
Solution Approach 2:
The determination section (31) serves multiple functions: it processes biological information, environment information, and preference information simultaneously to determine both the awakening point and the type of stimulus. This multi-functional design allows the system to maintain high accuracy without proportionally increasing overall device complexity, as one component handles multiple decision-making tasks.
2Measurement precision
If multiple data sources are integrated for awakening determination, then awakening accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges three separate information sources (biological information from section 21, environment information from section 22, and preference information from section 23) into a single determination section (31) that processes all inputs together. This consolidation allows the system to leverage multiple data sources for improved awakening point accuracy while avoiding the complexity of multiple independent determination systems operating in parallel.
Solution Approach 2:
The determination section (31) acts as an intermediary that receives and integrates data from multiple acquisition sections (21, 22, 23) and translates this integrated information into actionable awakening parameters for the controller (36). This intermediary layer simplifies the system architecture by providing a single integration point rather than requiring direct complex interactions between all data sources and the control function.
3Ease of operation
If awakening time is determined without considering sleep cycles, then device simplicity is maintained, but wake-up quality deteriorates
Solution Approach 1:
The biological information acquisition section (21) automatically monitors the target person's sleep state through physiological parameters (such as heart rate, breathing patterns, or brain waves), and the determination section (31) uses this self-generated data to identify optimal awakening points within sleep cycles. The system serves itself by using its own measurements to make awakening decisions, eliminating the need for external complex control algorithms while improving wake-up quality through cycle-aware timing.
Solution Approach 2:
The system implements feedback by continuously monitoring biological information during sleep and using this real-time data to adjust the awakening point determination. The determination section (31) processes ongoing biological signals to identify when the target person transitions between sleep stages, providing feedback that guides the selection of the optimal awakening moment. This feedback mechanism enables cycle-aware control without requiring pre-programmed complex algorithms.
Data Source
AI summary
A determination section of a sleep control apparatus determines an awakening point based on target data. The target data is time-series data of biological information of a target person in a target period. The target period is a period between wakefulness to failing asleep of the target person, or a period from the wakefulness of the target person to a time point at which a predetermined time has passed since the target person fell asleep. The controller controls a target device so that the target device stimulates the target person to wake up at the awakening point.