Description:Summary of the Invention:
The invention provides a method and device for wireless emotion detection using IoT-based speech recognition, facial expression analysis from images, and video data processing. The device is an IoT-enabled system comprising sensors (microphones, cameras), a multimodal AI processing unit, a wireless communication module, and a feedback interface. The method involves collecting speech, image, and video data from IoT devices, processing them using AI models (e.g., convolutional neural networks for images/video, recurrent neural networks for speech), and classifying emotions (e.g., happy, sad, angry) in real-time. The system operates in a wireless IoT network, ensuring scalability and low latency, and incorporates privacy-preserving techniques such as federated learning and data encryption.
, Claims:claim:
1. A device for wireless emotion detection using IoT, comprising:
a) A sensor module configured to capture speech data via microphones and visual data via cameras in an IoT environment;
b) A multimodal AI processing unit configured to analyze speech, facial expressions from images, and video data to classify emotions;
c) A wireless communication module configured to transmit data and classification results using IoT protocols;
d) A feedback interface for providing emotion classification outputs to users or applications;
e) A privacy module implementing federated learning and data encryption for secure processing; and
f) A processor coordinating the operation of said modules for real-time emotion detection.
2. The device of claim 1, wherein the multimodal AI processing unit employs convolutional neural networks for facial expression analysis, recurrent neural networks for speech recognition, and an attention-based fusion layer to combine multimodal features for emotion classification.
3. The device of claim 1, wherein the wireless communication module uses MQTT or CoAP protocols for low-latency, secure data transmission in IoT networks.
4. A method for wireless emotion detection using IoT, comprising the steps of:
a) Capturing speech data and visual data (images and video) using IoT-enabled sensors;
b) Preprocessing the captured data to normalize audio and enhance visual inputs;
c) Extracting features from speech, facial expressions, and video data;
d) Fusing multimodal features using an attention-based neural network;
e) Classifying emotions based on fused features to generate an emotion label and confidence score; and
f) Transmitting classification results to users or applications via wireless IoT protocols.
5. The method of claim 4, further comprising updating the AI model using federated learning to incorporate new data while preserving user privacy.
6. The device of claim 1, wherein the sensor module is embedded in IoT devices such as smart speakers, cameras, or wearables, and supports real-time data capture in distributed environments.