Sleep Quality Evaluation via Wearable and Environmental Sound Data
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Solution Overview
Problem
Wearable devices that collect physiological data during sleep do not provide a comprehensive picture of sleep quality, as they fail to account for environmental factors such as noise, temperature, and light, which significantly impact sleep restfulness.
Innovation Solution
Combining physiological data from wearable devices with environmental sound data collected using external devices, such as microphones, to evaluate sleep quality more accurately. This integration allows for improved sleep stage classification and identification of specific sound instances that may affect sleep.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only physiological data from wearable devices is used, then device complexity is reduced, but measurement precision of sleep quality is insufficient
Solution Approach 1:
The system divides the sleep quality evaluation into two independent parts: physiological data collection (via wearable device) and environmental sound data collection (via external device). This segmentation allows each component to focus on specific measurement tasks, improving overall measurement precision without requiring a single complex device to handle all functions.
Solution Approach 2:
The system introduces an intermediary processing mechanism that collects, synchronizes, and integrates data from multiple sources (wearable device and external microphone). This intermediary layer manages the complexity of multi-source data integration while enabling comprehensive sleep quality assessment through combined physiological and environmental analysis.
2Loss of information
If environmental sound data is collected using external devices, then information completeness about sleep factors is improved, but device complexity increases
Solution Approach 1:
The system employs a universal data integration platform that can handle multiple data types (physiological signals and environmental sounds) through a common processing framework. This multi-functional approach allows the system to capture comprehensive environmental information while using standardized procedures that reduce operational complexity.
Solution Approach 2:
The system implements automated data synchronization and correlation features that self-manage the integration process. The processing circuit automatically aligns temporal data from different sources, identifies relevant environmental factors, and generates comprehensive sleep quality reports without requiring manual configuration, thereby reducing user burden despite the system's comprehensive capabilities.
Data Source
AI summary
Methods, systems, and devices for evaluating a sleep quality of a user using wearable-based data are described. The sleep quality of a user may be evaluated by combining physiological data collected via a wearable device, along with environmental sound data collected via one or more external devices. For example, a microphone on a device may monitor environmental sounds while a user sleeps, and the environmental sounds may be used to improve sleep stage classification. Additionally, or alternatively, sound instances occurring throughout a sleep interval may be identified. In some examples, sound data may be combined with other data, such as physiological data, to determine relationships between the sleep sound data and sleep quality. An indication of the sleep stages and transitions between the sleep stages, the sound instances, the sleep quality, or a combination thereof, may be presented to the user via an application.