GRACE
Geriatric Robotic Assistance for Care and Engagement — a mobile, non-physical companion robot focused on intelligent monitoring, interaction, and emotional support for elderly users.
No arms. No lifting grandma. Just brains and vibes. 🧠✨
Meet GRACE
A friendly, non-threatening companion designed specifically for elderly care. GRACE doesn't have arms or lift anything — she's pure cognitive and emotional assistive technology.
The Unprecedented Care Crisis
With the global senior population projected to double by 2050, how can we develop a scalable, non-intrusive solution?
Companionship-First Robotics
Built for Elderly Care
GRACE autonomously navigates indoor spaces, follows humans, monitors health vitals via a smart wristband, detects posture anomalies, and provides verbal prompts for exercises, medication reminders, and daily routines.
She sends caretaker alerts when something seems off, and can even handle voice-controlled social media interaction for the elderly who may struggle with technology.
From Sketch to Reality
GRACE's design evolved from pencil sketches to 3D renders to a physical prototype — every iteration refined for elderly interaction.
She Sees Everything
A multi-sensor perception stack that maps, scans, follows, and understands the environment in real-time.
RPLidar A2M7
360° laser scanning for SLAM-based navigation and real-time obstacle avoidance. Maps the entire environment.
Intel RealSense D435i
RGB-D stereo camera with depth perception. Used for human detection, following, and spatial awareness.
Standard RGB Camera
General-purpose vision for pose detection, context awareness, and AI inference running on the Jetson GPU.
Ultrasonic Sensor
Close-range obstacle detection as a safety fallback. Works even in conditions where LiDAR may miss objects.
What Can GRACE Do?
SLAM Navigation
Autonomous mapping and navigation using SLAM Toolbox and Nav2. GRACE builds, saves, and navigates indoor maps independently.
Human Following
Detects and follows a person using depth + vision, maintaining a safe distance while moving through the environment.
Posture Detection
AI-powered pose estimation to detect falls, slouching, or unusual positions and alert caretakers immediately.
Health Monitoring
Integrates with a smart wristband to track SpO2, heart rate, and lifestyle metrics in real-time.
Verbal Interaction
Daily reminders, exercise suggestions, medication prompts, and voice-controlled social media access for the elderly.
Caretaker Alerts
When anomalies are detected — abnormal vitals, falls, or distress — GRACE notifies the remote caretaker instantly.
A Walking Nursing Station
GRACE collects and processes four categories of real-time data streams, turning raw sensor data into actionable care insights.
Robot Health
Battery voltage & current sensors, charging current, 3 IMUs, internal temperature, pressure, and humidity monitoring.
Human Health
Smart wristband integration for SpO2, heart rate, and lifestyle metrics — non-invasive vital sign tracking.
Environment
Temperature, humidity, pressure, CO₂, CO levels, and PMS5003 particulate matter (PM2.5/PM10) sensing.
AI Perception
Pose detection, human following, context awareness, and potential thermal-based anomaly detection streams.
About the Project
Final Year Project — UET Lahore
GRACE is a Final Year Project (FYP 2022) from the Department of Mechatronics and Control Engineering at the University of Engineering and Technology, Lahore.
Most student projects try to build a humanoid that barely works. GRACE focused on practical elderly independence — companionship-first robotics with a real-world application.
- Author: Muhammad Anss
- Email: muhammadanss0907@gmail.com
- Department: Mechatronics & Control Engineering
- University: UET Lahore
- Year: 2022