Wearable Adaptive Illumination via Activity Classification

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Solution Overview

Problem

Existing wearable illumination devices struggle to adjust light output effectively in variable ambient light conditions, posing safety risks and slowing down activities due to manual adjustment challenges and reliance on unsuitable light settings based on predetermined values or network-dependent systems.

Innovation Solution

A wearable device with integrated sensors and AI-enabled adaptive illumination control, classifying user activity and environmental conditions to dynamically adjust light output through machine learning models, allowing for hands-free and context-aware lighting adjustments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual adjustment of light output is implemented, then users can control illumination intensity, but safety risks increase and activity speed decreases due to hands being occupied

Engineering Contradiction:

Improvelight output adjustmentVSAvoidsafety

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The illumination device automatically adjusts light output based on detected activity type and ambient light conditions without requiring manual user intervention. The system classifies activities using sensors and machine learning models, then autonomously selects appropriate lighting parameters, freeing the user's hands while maintaining optimal illumination.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors ambient light conditions using photodetectors and activity state using sensors, feeds this information to the machine learning model, and dynamically adjusts light output based on the classified activity type. This closed-loop feedback mechanism ensures appropriate illumination without manual intervention.

Inventive Principle:

Principle #23Feedback

2Device complexity

If predetermined light sensor values are used for control, then device complexity is reduced, but adaptability to actual user behavior and activity conditions deteriorates

Engineering Contradiction:

Improvecontrol mechanismVSAvoidlight output suitability

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system dynamically changes lighting parameters (intensity, color temperature) based on classified activity types and ambient conditions. Different activity categories (high-intensity sports, low-intensity recreation, manual labor) trigger different lighting profiles, allowing the device to adapt to diverse usage scenarios beyond simple on/off or dimming control.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The illumination device transitions from static predetermined light sensor thresholds to dynamic adjustment based on real-time activity classification. The machine learning model continuously processes sensor data and adjusts lighting parameters according to the current activity state, enabling the system to adapt to changing conditions and user behavior patterns.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If smart illumination devices require network access, then illumination control can be enhanced, but device complexity and power consumption increase

Engineering Contradiction:

Improveillumination control capabilityVSAvoidpower consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The device performs activity classification and lighting control autonomously using onboard sensors and machine learning models without requiring external network connectivity. The system processes all necessary data locally, eliminating dependence on Internet-enabled devices or communication networks while maintaining intelligent illumination adjustment capabilities.

Inventive Principle:

Principle #25Self-service

4Ease of operation

If hands-free illumination control is implemented, then ease of operation improves, but measurement precision of activity state deteriorates without multiple sensors

Engineering Contradiction:

Improvehands-free controlVSAvoidactivity state detection

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system uses a multi-functional sensor array where each sensor serves multiple purposes. Accelerometers detect both motion intensity and orientation, gyroscopes detect rotational movement and head position, and this multi-use approach enables comprehensive activity classification without adding excessive hardware complexity or power consumption.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12495475B2Adaptive illumination control via activity classification

Publication Date: 2025.12.09 OBER ALP

AI summary

Disclosed herein are embodiments for implementing active illumination control via activity classification. An embodiment includes a processor configured to perform operations comprising receiving first sensor data generated by at least one of the plurality of sensors. Based at least in part on the first sensor data, the processor may select a first lighting profile, and instruct the light-emitting element to emit light in accordance with the first lighting profile. The processor may be further configured to receive second sensor data generated by the at least one of the plurality of sensors and to update an activity classification stored in a memory, in response to the second sensor data being different from the first sensor data. The processor may transition from the first lighting profile to a second lighting profile, in response to the updating, and may instruct the light-emitting element to emit light in accordance with the second lighting profile.