Remote Physiological Sensing Robot for Emotion-Aware Interaction
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Solution Overview
Problem
Robots struggle to effectively detect and respond to the emotional states of humans and animals in their vicinity, leading to potential frustration or aggression due to misclassification of emotions using traditional sensors like cameras and radars.
Innovation Solution
A robot equipped with physiological data collecting sensors and a trained machine learning algorithm to remotely measure physiological functions such as heart rate, body temperature, and respiratory activity, correlating this data with image data to determine emotional states and adjust its actions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors like cameras and radars are used to detect emotional states, then the robot can maintain simple hardware configuration, but the emotional detection accuracy deteriorates leading to misclassification
Solution Approach 1:
The patent combines multiple sensing modalities (cameras, radars, and physiological data sensors) into an integrated sensor system. This merging allows the robot to collect diverse data types simultaneously, improving emotional state detection accuracy by cross-validating signals from different sources while maintaining a unified processing architecture.
Solution Approach 2:
The patent employs a composite sensing approach that integrates traditional sensors (cameras, radars) with physiological data sensors. This composite sensor system leverages the strengths of each sensor type, creating a more robust and accurate emotional detection system than any single sensor could provide alone.
2Reliability
If the robot uses physiological data sensors to accurately detect emotional states, then user acceptance and safety improve, but the device complexity increases
Solution Approach 1:
The patent introduces a machine learning algorithm as an intermediary that processes physiological data and translates it into actionable emotional state classifications. This intermediary layer handles the complexity of interpreting raw sensor signals, allowing the robot to make reliable emotional detections without requiring complex decision-making hardware.
Solution Approach 2:
The system implements feedback mechanisms where the robot continuously monitors physiological data and adjusts its behavior based on detected emotional states. This feedback loop improves interaction reliability by enabling real-time adaptation to user emotional responses, while the automated processing keeps system complexity manageable.
3Adaptability or versatility
If the robot continuously monitors physiological functions to detect emotional states, then the ability to respond appropriately improves, but the energy consumption increases
Solution Approach 1:
The patent implements periodic sampling of physiological data rather than continuous monitoring. The robot collects physiological measurements at intervals, which maintains the ability to detect emotional state changes while significantly reducing energy consumption compared to continuous data acquisition.
Solution Approach 2:
The system selectively processes physiological data based on detected relevance. Instead of analyzing all sensor data continuously, the robot focuses processing on periods when emotional state changes are detected or when interaction contexts suggest monitoring is necessary, reducing overall energy usage while maintaining adaptability.
Data Source
AI summary
Techniques described in this application are directed to an autonomous robot that is configured to interact with its environment through identifying the presence and emotional state (e.g., mood) of live subjects in the scene. The autonomous robot includes one or more remote physiological data collecting sensors capable of remotely collecting physiological data such as heart rate, blood circulation, or respiratory activity of the live subjects within close proximity of the robot.