Audio Interest Detection Through Time-Aligned Body Movement
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Solution Overview
Problem
Existing electronic devices inefficiently utilize resources to determine user interest in audio content based on body movements, leading to unnecessary processing, storage, and power consumption.
Innovation Solution
Devices analyze a time-based relationship between audio and body movements, such as head bobbing, to identify user interest, enabling selective performance of audio analysis and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous audio analysis and body movement monitoring are performed to determine user interest, then user interest detection accuracy is improved, but processing power and energy consumption increase
Solution Approach 1:
The system performs preliminary body movement detection to trigger audio analysis only when movement is detected, rather than continuously analyzing audio. This preliminary action filters out unnecessary processing and reduces energy consumption while maintaining detection accuracy.
Solution Approach 2:
The system uses periodic sampling of body movement data and triggers analysis only at relevant moments (when movement occurs), rather than continuous processing. This periodic approach reduces computational load while maintaining effective user interest detection.
2Measurement precision
If audio analysis is performed continuously to identify music and sounds, then content identification accuracy is improved, but processing resources and time are wasted when no audio content is present
Solution Approach 1:
The system detects body movement as a preliminary trigger before initiating audio analysis. This ensures audio analysis only occurs when the user is potentially interested in audio content, avoiding wasted processing time when no content is present or when the user is not engaged.
Solution Approach 2:
The system uses body movement detection as feedback to control the audio analysis process. When movement is detected, audio analysis is activated; when movement stops, analysis is suspended. This feedback mechanism optimizes processing resources based on real-time user state.
3Productivity
If selective audio analysis is performed based on body movement detection, then resource efficiency is improved, but the complexity of coordinating multiple sensors and triggers increases
Solution Approach 1:
The system merges body movement detection and audio analysis into a coordinated system where movement triggers audio processing. This integration allows efficient resource usage while managing complexity through unified processing logic that handles multiple sensors and triggers systematically.
Data Source
AI summary
Various implementations disclosed herein include devices, systems, and methods that determine that a user is interested in audio content by determining that a movement (e.g., a user's head bob) has a time-based relationship with detected audio content (e.g., the beat of music playing in the background). Some implementations involve obtaining first sensor data and second sensor data corresponding to a physical environment, the first sensor data corresponding to audio in the physical environment and the second sensor data corresponding to a body movement in the physical environment. A time-based relationship between one or more elements of the audio and one or more aspects of the body movement is identified based on the first sensor data and the second sensor data. An interest in content of the audio is identified based on identifying the time-based relationship. Various actions may be performed proactively based on identifying the interest in the content.