Camera-Based Full-Body Sensing for Casino Player Action Prediction
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Solution Overview
Problem
Existing systems struggle to provide real-time, dynamic, and personalized casino experiences for players based on their emotional and psychological states, making it challenging to tailor interactions and offerings effectively.
Innovation Solution
A camera-based system that uses biometric sensors and machine learning to analyze player attributes such as speed, gait, thermal profile, and facial expressions to predict player actions and emotions, enabling personalized casino experiences through targeted actions and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If camera-based biometric sensing and machine learning are implemented to analyze player attributes and predict actions, then player engagement and personalization are improved, but device complexity and cost increase
Solution Approach 1:
The system divides the complex analysis task into separate modules: camera capture, biometric feature extraction, player attribute determination, and action prediction. Each module processes specific data independently before integrating results, reducing overall system complexity while maintaining high personalization capability.
Solution Approach 2:
A player database serves as an intermediary between raw camera data and personalized casino actions. The database stores historical player data and serves as a mediator for machine learning models to predict actions, simplifying the real-time processing complexity while enabling sophisticated personalization.
2Reliability
If real-time analysis of player emotional and psychological states is performed, then player satisfaction and experience are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by capturing and storing biometric data during player interactions before making predictions. Historical data is pre-processed and stored in the player database, allowing rapid retrieval and analysis during real-time decision-making without requiring complex computations at the moment of prediction.
Solution Approach 2:
The system continuously refines its predictions by comparing predicted player actions with actual behavior and using this feedback to update the machine learning models. This feedback loop improves prediction accuracy over time while the model learns to make faster predictions based on accumulated knowledge from previous iterations.
3Measurement precision
If multiple cameras and biometric sensors are deployed to capture comprehensive player data, then measurement precision of player attributes is improved, but device complexity and infrastructure requirements worsen
Solution Approach 1:
The camera system is designed with multi-functionality, serving multiple purposes: capturing player identity, monitoring biometric attributes, tracking player movement, and analyzing emotional states. This universal approach reduces the need for separate specialized sensors for each function, thereby reducing overall infrastructure complexity while maintaining high measurement precision.
Data Source
AI summary
A gaming system, computer-implemented method and gaming device are operable enhance player experience using image data. A system includes a processor circuit and a memory including machine-readable instructions that, when executed by the processor circuit, cause the processor circuit to perform operations. Such operations include receiving, from a camera, image data that includes visual signals corresponding to a player and determining player action components from the image data. The player action components include multiple different player attributes that correspond to an emotional state of the player. Operations further include, based on the different player attributes, determining a predicted action of the player and causing a casino related action that corresponds to the predicted action.