AI Headlamp Lighting Profiles for Automatic Activity Detection
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Solution Overview
Problem
Traditional headlamps require manual adjustment of lighting geometry and brightness for different activities, which is time-consuming and unintuitive, and often results in unsatisfactory settings due to varying user preferences and activity-specific lighting needs.
Innovation Solution
A portable headlamp equipped with an AI unit that automatically classifies user activities using sensors and adjusts the light beam's geometry and brightness based on pre-defined algorithms or user-specific training data, eliminating the need for manual settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of lighting geometry and brightness is used for different activities, then the headlamp can be controlled, but it is time-consuming and unintuitive
Solution Approach 1:
The headlamp automatically detects user activities through sensors (accelerometer, gyroscope, ambient light sensor) and autonomously adjusts lighting geometry and brightness without requiring manual user configuration. The system serves itself by classifying activities and applying appropriate lighting profiles automatically.
Solution Approach 2:
Multiple activity-specific lighting profiles are pre-configured in the headlamp's memory, each containing optimized geometry and brightness settings for different activities (running, cycling, camping, hiking). When an activity is detected, the corresponding pre-prepared profile is immediately applied, eliminating configuration time.
2Adaptability or versatility
If activity-specific lighting profiles are pre-configured, then lighting can be optimized for specific activities, but manual configuration for each activity is required
Solution Approach 1:
The headlamp automatically detects user activities through sensors (accelerometer, gyroscope, ambient light sensor) and autonomously adjusts lighting geometry and brightness without requiring manual user configuration. The system serves itself by classifying activities and applying appropriate lighting profiles automatically.
Solution Approach 2:
The headlamp continuously monitors sensor data (motion patterns, ambient light levels) and uses this feedback to automatically determine the current activity and adjust lighting settings accordingly. The system adapts in real-time based on feedback from the environment and user behavior.
3Adaptability or versatility
If multiple light sources with different beam geometries are used, then lighting can be adapted to different activities, but the device complexity increases
Solution Approach 1:
The headlamp uses multiple independent light sources (first and second light sources), each capable of producing different beam geometries (focused or wide beam). This segmentation allows the system to select and combine specific light sources based on activity requirements, achieving versatility through modular composition.
Solution Approach 2:
The headlamp dynamically selects and adjusts which light sources are active and at what intensity levels based on the detected activity. The system can switch between focused beam modes (for running/cycling) and wide beam modes (for camping/hiking), and can also combine multiple light sources to create intermediate lighting patterns.
Data Source
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AI summary
A portable lamp 100, preferably a headlamp 100, which is adapted to be worn or carried by a user, comprising: at least one light source 114, an Al unit 120, wherein the Al unit 120 comprises an activity classification unit 122 and a control unit 124, wherein said activity classification unit 122 is able to automatically classify an activity which the user is currently carrying out without any manual setting by the user, wherein said control unit 124 is adapted to control the beam of said at least one light source 114 at least based on the classified activity of the user.