Head-Mounted Display Head Movement Interpretation
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Solution Overview
Problem
Wearable head-mounted displays face challenges in accurately distinguishing between intended user interface (UI)-targeted head movements and non-UI-targeted head movements, especially in dynamic environments, leading to potential misinterpretation of user inputs.
Innovation Solution
The system employs sensors like gyroscopes and accelerometers to receive head-movement data and uses context signals such as location, time, and activity recognition to adjust sensitivity levels and filter out non-UI-targeted movements, allowing for precise interpretation of UI-targeted movements to control content on the display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses sensors to detect all head movements, then it can capture comprehensive user input data, but it cannot distinguish between UI-targeted movements and non-UI-targeted movements
Solution Approach 1:
The system performs preliminary classification of head movements into UI-targeted and non-UI-targeted categories before processing them as user input. By pre-filtering movements based on their characteristics and context, the system prevents false input interpretation from occurring in the first place
Solution Approach 2:
The system introduces an intermediary classification layer between the sensor detection and input processing stages. This intermediary component analyzes movement characteristics and determines whether each head movement should be interpreted as user input, acting as a mediator that filters out non-UI-targeted movements
2Reliability
If the system filters out non-UI-targeted movements, then false activations are reduced, but legitimate user inputs may be missed
Solution Approach 1:
The system dynamically adjusts its filtering criteria based on context signals such as current activity, location, and time. The classification thresholds and sensitivity levels are not fixed but adapt in real-time to maintain optimal balance between reliability and responsiveness across different usage scenarios
Solution Approach 2:
The system uses context signals as feedback to continuously refine its classification of head movements. By monitoring activity state, location, and temporal patterns, the system adjusts its interpretation of movements to maintain high reliability while preserving legitimate user inputs
3Adaptability or versatility
If the system adapts to different activities and environments, then usability is improved, but system complexity increases
Solution Approach 1:
The system uses a universal classification framework that handles multiple activities and environments through a single integrated process. The same core classification mechanism works across diverse contexts by incorporating activity, location, and temporal information, avoiding the need for separate processing systems for each scenario
Data Source
AI summary
Methods and systems involving a graphic display in a head mounted display (HMD) are disclosed herein. An exemplary system may be configured to: (1) at a computing system associated with a head-mountable display, receive head-movement data indicative of head movement; (2) use one or more context signals to determine a first activity associated with the head-mountable device; (3) determine a head-movement interpretation scheme corresponding to the first activity; (4) apply the determined head-movement interpretation scheme to determine input data corresponding to the received head-movement data; and (5) provide the determined input data for at least one function of the head-mountable display.