User State Inference via Sensor Aggregation
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Solution Overview
Problem
Mobile device applications lack the ability to effectively infer user engagement and context without direct user feedback, limiting their capacity to provide context-sensitive services and adjust interactions dynamically.
Innovation Solution
Implementing a non-verbal feedback system that aggregates inputs from various sensors like cameras, accelerometers, and biometric sensors to infer user states, allowing applications to adjust their behavior based on user engagement levels and environmental context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If applications use direct user feedback to determine user state, then accuracy of user state determination is improved, but user interaction complexity and time consumption increase
Solution Approach 1:
The system enables self-service by automatically determining user state through sensor data aggregation and analysis without requiring direct user feedback. The processor autonomously interprets sensor inputs from cameras, accelerometers, and biometric sensors to infer engagement levels, allowing the device to adapt to user needs without additional user actions or inputs.
2Adaptability or versatility
If applications aggregate data from multiple sensors to infer user state, then context sensitivity is improved, but device complexity increases
Solution Approach 1:
The system implements multi-functionality by using a single processor to perform multiple functions: aggregating data from diverse sensors (cameras, accelerometers, biometric sensors), analyzing the aggregated data to determine user state, and adjusting application behavior accordingly. This universal approach allows one component to handle various sensor types and processing tasks, reducing overall system complexity despite the diversity of sensors involved.
Solution Approach 2:
The processor acts as an intermediary that mediates between multiple sensors and applications. It aggregates raw sensor data, interprets it to determine user state, and provides this information to applications that need to adapt their behavior. This intermediary layer simplifies the interaction complexity by providing a unified interface between sensors and applications, shielding applications from the complexity of direct sensor integration.
3Adaptability or versatility
If applications adjust behavior in real-time based on user state, then user experience personalization is improved, but processing time and energy consumption increase
Solution Approach 1:
The system implements continuous monitoring and adjustment by continuously aggregating sensor data, determining user state, and adjusting application behavior in real-time. This continuous operation allows the system to maintain personalized user experience without interruption, adapting to changing user states as they occur rather than requiring periodic updates or user-initiated adjustments.
Data Source
AI summary
Measurement data is correlated to one or more probable user states. The measurement data is associated with a user of the computing device and received from sensors communicatively coupled to the computing device. Execution of an application running on the computing device is altered based on the probable user states. In another embodiment, probable user states for a user of a computing device are processed. The probable user states are indicative of a level or type of interaction of the user with the computing device. The probable user states are also determined based on analysis of measurement data received from sensors communicatively coupled to the computing device. Indications of the probable user states are provided during execution of an online application on a second computing device.