User Profiling Based Proactive Interaction Manager
This interactive dashboard synthesizes the findings from the research paper by Hoashalarajh Rajendran et al. (University of Moratuwa). The study explores how service robots can transition from reacting to explicit commands to proactively offering services (specifically, reading suggestions) by analyzing user context, posture, and emotion.
Moving Beyond "Service on Demand"
Traditional robots wait for users to ask for help. This research implements a system where the robot observes the user (e.g., checking if they are relaxing or working), reads their emotional state, considers the environment (time, weather), and proactively initiates interaction to suggest a book. This approach aims to reduce user disturbance and enhance human-robot bonding.
Perceive
Vision & Voice inputs to gather context.
Process
Decision Trees & Online Learning adaptation.
Proact
Initiate service at the optimal moment.
Key Achievements
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High Perception Accuracy
Emotion and Posture models achieved ~90% accuracy.
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✓
Proven Adaptability
System successfully learned and shifted reading preferences over a 4-day trial.
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Statistical Significance
User satisfaction rejected the null hypothesis (p < 0.05), proving proactive superiority.
System Architecture
This section illustrates the data pipeline of the robot. The architecture is divided into Perception (gathering data), Cognition (making decisions), and Action. Explore the interactive blocks below to understand how raw sensor data is transformed into a proactive service.
1. Perception Module
Vision Inputs
• MTCNN (Face ID)
• VGG19 EmotionNet
• StateNet (Posture)
Voice Inputs
• Speech-to-Text
• Sentiment Analyzer
• User Feedback
Context Data
• Weather API
• Time of Day
• Google Calendar
2. Cognition Module
Proactive Interaction Manager
State Module: Work vs Relax
Fusion Module: Vision + Voice
3. Action Module
Service Execution
Robot initiates conversation via text-to-speech, offering a highly tailored book genre recommendation based on the cognitive analysis.
Click on any module above to view detailed information about its role in the proactive interaction pipeline.
The Cognitive Engine
How does the robot actually decide what to suggest? This section unpacks the core intelligence of the system. It utilizes a Decision Tree for baseline suggestions and an Online Learning Algorithm to continuously adapt to the user's changing preferences based on their feedback.
1 Decision Tree Attributes
The initial genre suggestion is derived by traversing a decision tree built on five key contextual variables. Click to highlight attributes.
Demographic
Gender
Psychological
User Emotion
Temporal
Time of Day
Environmental
Weather Condition
Scheduling
Remaining Time
2 Preference Adaptation
The Online Learning Weight Updating Formula ensures the robot learns from its mistakes. If a user rejects a "Fiction" book, the system dynamically lowers the weight of Fiction for that specific user profile in real-time.
Experimental Validation
The system was evaluated in two main phases. First, ensuring the perceptual models could accurately identify human states. Second, measuring how well the proactive manager adapted to user preferences over a 4-day trial period compared to a baseline.
Perception Accuracy
Performance of individual AI models
StateNet (Posture detection) achieved the highest accuracy, crucial for distinguishing between "Working" and "Relaxing" states to prevent unwanted interruptions.
System Adaptability
Mean user satisfaction scores over 4 days
The upward trend demonstrates the effectiveness of the Online Learning Algorithm. By Day 4, the robot was highly accurate in predicting the desired book genre.
Hypothesis Testing Results
A one-sample t-test was conducted against a baseline satisfactory score of 3.0. The extremely low p-value (p < 0.05) indicates that user satisfaction with the proactive system was statistically significant and vastly superior to baseline expectations. Users felt the robot understood their needs without being prompted.