Artificial Reality Headset Don/Doff Detection With Sensor Fusion
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Solution Overview
Problem
Existing artificial reality device headset don/doff detection systems using proximity sensors, capacitance sensors, or mechanical switches are prone to false positives and negatives due to varying user and environmental conditions, and combining these systems often causes interference, affecting battery life and user experience.
Innovation Solution
A multi-sensor detection system utilizing a combination of proximity sensors, inertial measurement units (IMU), and eye tracking/face tracking units to accurately determine when a headset is donned or doffed by comparing sensor readings to specific thresholds, disabling unnecessary systems to minimize interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single detection system (proximity sensor, capacitance sensor, or mechanical switch) is used for don/doff detection, then the device structure remains simple, but false positives and false negatives occur due to varying user and environmental conditions
Solution Approach 1:
The patent combines multiple detection systems (proximity sensor, capacitance sensor, mechanical switch, motion sensor, temperature sensor) into an integrated don/doff detection system. These sensors work together to cross-validate detection results, significantly reducing false positives and false negatives caused by individual sensor limitations under varying user and environmental conditions.
Solution Approach 2:
The detection system is designed to perform multiple functions using a single integrated framework. The same sensor array can detect donning events, doffing events, and distinguish between different user conditions (e.g., wearing glasses, different skin tones, various hairstyles), making the system universally applicable across diverse usage scenarios.
2Reliability
If multiple detection systems are combined to improve detection accuracy, then false positives and false negatives are reduced, but system interference increases and battery life decreases
Solution Approach 1:
The system dynamically adjusts sensor activation based on operational context. During active use, the full sensor array remains monitored but only specific sensors are actively powered based on current detection needs. During idle periods, less power-intensive sensors are put into low-power modes while critical sensors remain active, optimizing the balance between detection reliability and power consumption.
Solution Approach 2:
The system implements feedback mechanisms where sensor readings continuously inform system state decisions. When sensors detect stable conditions indicating the headset is securely worn, the system reduces polling frequency and activates fewer sensors, thereby reducing power consumption while maintaining detection accuracy through periodic verification.
3Reliability
If multiple detection systems are combined to improve detection accuracy, then false positives and false negatives are reduced, but system interference increases
Solution Approach 1:
The system extracts and separates the detection functions of different sensors into independent processing channels. Each sensor type processes its data independently before integration, preventing cross-interference between sensor systems. This modular approach allows the system to eliminate conflicting signals while maintaining the benefits of multi-sensor detection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The multi-sensor system reduces false detections, optimizing battery life and user experience by accurately identifying don and doff events while minimizing power consumption and system interference.
Implementation Method 1
a current proximity reading set is above a proximity donned threshold
Implementation Method 2
a current IMU reading set is above an IMU donned threshold
Implementation Method 3
a current ET/FT reading set indicates a loss of eye or face tracking
Data Source
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AI summary
Aspects of the present disclosure are directed to a multi-sensor don/doff detection system for an artificial reality device headset. The multi-sensor don/doff detection system can use a combination of a proximity sensor, an inertial measurement unit (IMU), and an eye tracking / face tracking (ET/FT) unit to make these determinations. However, when both the ET/FT system and proximity sensor system are active, they can have system coexistence issues. Thus, only one of these systems can be used simultaneously. The multi-sensor don/doff detection system can more accurately identify don events by using input from the proximity sensor and the IMU. The multi-sensor don/doff detection system can also more accurately identify doff events by using input from the IMU and either A) the proximity sensor when the ET/FT system is disabled or B) the ET/FT system when the ET/FT system is enabled.