Wearable Garment Drowsiness Detection via Sensor Segmentation
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Solution Overview
Problem
Current wearables and IoT systems lack effective solutions to monitor and prevent driver drowsiness, which can lead to dangerous situations while driving, and fail to efficiently alert users or adjust environmental conditions to maintain alertness.
Innovation Solution
A wearable garment integrated with sensors and a processor that communicates with mobile devices and IoT systems to detect precursor signs of drowsiness, adjust environmental conditions, and alert the user or redirect them to a safe location, using a network of sensors and processing power split between the garment, mobile device, and cloud processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wearables and IoT systems are integrated to monitor driver drowsiness, then driver safety is improved, but device complexity increases
Solution Approach 1:
The system divides monitoring functions across multiple independent components: wearable sensors on the driver, mobile device for data processing, and IoT environmental controls. This segmentation allows each component to be optimized independently while collectively improving driver safety without overwhelming complexity in any single device.
Solution Approach 2:
The wearable device serves multiple functions: monitoring physiological signals, tracking motion, and communicating with both mobile devices and IoT systems. This multi-functionality reduces the need for separate dedicated devices, improving safety while managing overall system complexity.
2Measurement precision
If multiple sensors are integrated in the wearable garment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Multiple sensor types (physiological sensors, motion sensors, temperature sensors) are merged into a single wearable garment. This consolidation improves measurement precision for drowsiness detection by combining data from multiple sensors while managing complexity through integrated design rather than separate devices.
Solution Approach 2:
The mobile device acts as an intermediary that receives data from multiple sensors in the wearable garment and processes the information. This mediator approach allows high measurement precision through multiple sensors while offloading processing complexity to the mobile device rather than the wearable itself.
3Adaptability or versatility
If processing power is distributed between wearable, mobile device, and cloud, then system adaptability is improved, but device complexity increases
Solution Approach 1:
Processing power is segmented across three levels: simple real-time processing in the wearable, intermediate processing in the mobile device, and complex cloud-based analytics. This segmentation provides processing flexibility for different scenarios while managing complexity at each level rather than concentrating all complexity in one device.
Solution Approach 2:
The system dynamically allocates processing tasks based on available resources and requirements. Simple tasks are handled locally in the wearable, moderate tasks use the mobile device, and complex analytics utilize cloud resources. This dynamic distribution improves adaptability while managing complexity through contextual task allocation.
4Reliability
If environmental conditions are adjusted to prevent drowsiness, then driver alertness is improved, but device complexity increases
Solution Approach 1:
The mobile device and IoT system act as intermediaries between the wearable sensor and environmental controls. The wearable detects drowsiness, the mobile device processes the data and determines appropriate responses, and the IoT system executes environmental adjustments (temperature, lighting, ventilation). This intermediary approach improves driver alertness while distributing complexity across multiple components rather than concentrating it in the wearable.
Data Source
AI summary
In one embodiment, the present system is designed to be a wearable garment, worn in a vehicle, such as a car. The wearable garment includes a plurality of sensors. These sensors may be used by a processor to identify precursor signs of dozing off. In one embodiment, the processor may be in the wearable garment. In another embodiment, the processor may be in a mobile device linked to the wearable garment.