AI-Powered Employee Engagement System
Project Highlights
Client Overview
The client wanted to enhance employee engagement and create a personalized experience for their workforce by utilizing AI and machine learning technologies. The system needed to track employees' behavior in the office, monitor their moods, and deliver personalized greetings upon entering and leaving the premises. Additionally, the solution needed to integrate with the existing HRMS tool to streamline attendance, mood analysis, and overall employee satisfaction.
Challenges
The client required a system that could enhance employee engagement, track employee moods, and integrate with their existing HRMS tool for automated attendance and mood analysis.
- ✓Employee Tracking via CCTV
- ✓Mood Detection
- ✓Personalized Greetings
- ✓Tracking Office Absences
- ✓HRMS Integration
- ✓Facial Recognition
- ✓Audio Greetings
- ✓Integration with HRMS
- ✓Cloud Infrastructure
- ✓Real-time Communication
Solution
We developed an AI-powered employee engagement system integrating CCTV-based facial recognition, real-time emotion analysis, personalized audio greetings, and HRMS integration to streamline attendance and mood tracking.
- ✓We integrated the existing office CCTV system with an AI-based facial recognition model to detect and identify employees as they enter and exit the office.
- ✓The system records the exact time an employee arrives at or leaves the office, feeding this data into the HRMS tool for automated attendance tracking.
- ✓Using facial recognition and emotion analysis, the system tracks the mood of employees when they enter and exit the office.
- ✓The mood data is analyzed to identify patterns that may help HR in understanding employee well-being.
- ✓A smart speaker setup at the office entrance is connected to the AI system. and As the employee is recognized via CCTV, the AI system triggers a personalized greeting (e.g., 'Good morning, [Employee Name]!') using voice synthesis.
- ✓The greeting is tailored based on the employee's mood and arrival time, making it more personalized.
- ✓The system also tracks how many times an employee leaves the office during the day, logging the information along with the detected mood during each exit.
- ✓The HR team can analyze this data to better understand break habits and employee engagement.
- ✓The entire system is seamlessly integrated with the client's HRMS tool. and The entire system is seamlessly integrated with the client's HRMS tool.
- ✓OpenCV and Dlib for facial detection and tracking using CCTV footage. and DeepFace library for real-time emotion analysis, extracting moods such as happy, sad, neutral, etc.
- ✓Custom trained Convolutional Neural Networks (CNNs) for accurate mood detection.
- ✓Google Text-to-Speech (TTS) API for voice synthesis, converting text (greetings) into audio. and Smart speaker integration with Raspberry Pi and Node.js for handling audio output.
- ✓REST APIs built using Python (Flask) for communication between the AI tool and the HRMS system.
- ✓AWS Lambda for serverless event triggers, ensuring the system is scalable and cost-effective.
- ✓AWS S3 for storing CCTV footage and facial recognition data. and AWS Rekognition for backup facial recognition in case of local model failure.
- ✓MQTT protocol for low-latency communication between the CCTV system, AI server, and smart speakers.
- ✓WebSockets for real-time employee mood updates and interactions displayed on HR dashboards.
Conclusion
our AI-powered equine movement and health monitoring system for Molenkoning successfully integrates advanced video analysis and heart rate tracking, providing trainers with real-time, non-invasive insights into a horse's gait and overall health. This solution helps prevent injuries, optimize training, and enhance performance by offering data-driven feedback. Exclusive to Molenkoning clients, the system delivers high value by leveraging cutting-edge AI technology, ensuring both the horse's well-being and the efficiency of training routines. Future improvements like predictive analytics will further elevate its impact on equine care.
