Real-time reaction

Real-time reaction

MorphCast AI allows for real-time adaptation of content based on the emotions detected. This is because there is not the latency caused by sending data to a server and waiting for a response. This creates a more engaging and personalized user experience. Also, this enables immediate actions based on the emotional state of the user.

No bandwidth consumption or server latency for real-time reaction

MorphCast AI’s client-side processing allows for real-time adaptation of content based on the emotions detected. Since we process facial data on the user’s device, there is no need to wait for a server to process the data and send it back. This eliminates latency, which is the delay caused by sending data to a server, waiting for it to be processed, and receiving a response. In addition to this, there is no bandwidth consumption due to the streaming of the camera to be sent to the server for analysis.

This means that emotional data can drive in real time the adaptation of content on the user’s device. There is no lack of continuity, and it’s crucial for creating a more personalized and engaging experience.

MorphCast Emotion AI Advantages: Real-time reaction

Use cases examples

Gaming. You can create more immersive and personalized gaming experiences by detecting and responding to player emotions in real-time. Additionally, since MorphCast processes emotional data locally, you can use it to trigger immediate actions that depend on the emotional state of the user.

eLearning. You can detect and respond to students’ emotional states, adapt content and teaching style for better engagement and retention. Or you can monitor student engagement and attention levels during an on demand course and adjust scenes with alternative teaching methods accordingly. Also, you can create personalized learning paths based on the student’s emotional state, learning style, and pace. And you can provide gamified and interactive learning experiences that respond to students’ emotions and preferences.

Job interview coaching. You can analyze the emotional state of candidates during mock interviews, providing real-time feedback on body language and nonverbal cues. Or you can monitor the candidate’s stress levels and provide relaxation exercises to reduce anxiety before the real interview. You can also provide data-driven insights on the candidate’s emotional strengths and areas for improvement to guide their coaching. Or create a simulation of a real-life job interview scenario that adapts to the candidate’s emotional state and provides personalized feedback and suggestions.

In other use cases, you can detect the emotions felt while watching a video. And use these data to trigger the user’s tastes or lifestyles with a certain approximation, to propose products or services.