Innovation Portfolio

Dense fog, smoke, and haze reduce visibility and make object identification difficult in surveillance, rescue, and navigation environments. This system enhances real-time video to restore clarity and enables detection of objects even under low-visibility conditions.

3D-printed airframe; hybrid multirotor/fixed-wing configuration; electric propulsion; flight-control system; transition-capable control architecture. A lightweight VTOL (Vertical Take-Off and Landing) drone was developed to combine the vertical take-off and hovering capability of a multirotor with the efficient forward flight of a conventional aeroplane. The airframe was produced using 3D printing to enable rapid prototyping, easy modification and reduced structural weight. The project focused on integrating the propulsion and flight-control elements required for stable vertical flight and controlled transition into forward flight, providing a practical platform for studying hybrid UAV design, aerodynamic behaviour and autonomous aerial systems.

Incorrect body posture during daily activities, sports, and training can lead to injury and long-term musculoskeletal problems. This system monitors body posture in real time and provides immediate corrective feedback, helping users maintain proper alignment and improve movement safety and performance.

Traditional streetlight systems rely on manual inspection, causing delayed fault detection, energy loss, and safety risks. This system enables real-time monitoring and automatic fault detection of streetlights, ensuring efficient operation and improved public safety.
It provides remote control, precise fault localization, and reliable communication without internet dependency, making it suitable for smart and sustainable urban infrastructure.

Operating heavy machinery in snow, fog, desert, or disaster environments is difficult due to poor visibility and lack of clear road guidance. This system provides real-time augmented visual navigation, helping operators track road alignment, turns, slopes, and obstacles safely.
It improves situational awareness, reduces accident risk, and enhances operational efficiency in challenging terrains and low-visibility conditions.

Missing or incorrect medication timing can lead to serious health risks, especially for elderly and long-term patients. This system ensures timely medicine intake through intelligent reminders, compartment-level alerts, and real-time monitoring.

It improves medication adherence, supports caregivers through remote alerts, and maintains safe storage conditions for reliable healthcare assistance.

Identifying correct legal sections from incident descriptions is difficult for non-legal users and often leads to misclassification or delays. This system analyzes user-described incidents and automatically maps them to relevant statutory provisions, providing instant legal guidance.
It improves legal accessibility, supports faster preliminary assessment, and helps ensure accurate classification of incidents in real-world scenarios

The IoT-Based Smart Polypharmacy Medicine Dispenser is an intelligent healthcare device designed to help patients take multiple medicines on time and reduce medication errors. It uses an ESP32 microcontroller, DS3231 Real-Time Clock (RTC), DC motors, and a 3D-printed enclosure to automate medicine dispensing. Each medicine is stored in a separate drawer and can be scheduled through a web application. The ESP32 continuously checks the programmed schedule and dispenses the correct medicine at the right time. Firebase Realtime Database enables real-time communication between the hardware and the web application for remote monitoring and control. The TM1637 display shows the current time and the drawer number during medicine reminders. The system also allows caregivers to monitor and manage medication schedules remotely. Overall, the project provides an affordable, reliable, and user-friendly solution that improves medication adherence and supports smart healthcare.

The system captures the player’s kicking action from multiple camera angles, such as front, side, and rear views. These video streams are synchronized using an audio-based clap signal, allowing the system to analyze the same moment of action from different perspectives. The key event in the shot, the exact foot-ball contact frame, is identified by tracking the ball movement and the position of the player’s ankle.

Once the contact frame is detected, the system applies markerless pose estimation to extract important body keypoints, including the shoulder, hip, knee, ankle, and foot. These keypoints are then used to calculate biomechanical posture parameters such as knee angle, trunk angle, and ankle angle. The calculated posture values are compared with predefined assessment ranges to generate rule-based feedback and an overall posture score.

This innovation aims to make sports performance analysis more accessible, practical, and interpretable. It can help players understand their body alignment, balance, joint positioning, and technique during a curve shot. For coaches, the system provides a visual and data-supported method to identify posture errors and guide performance improvement.

The technology represents a step toward low-cost, AI-based sports biomechanics solutions that can be used in training environments, academic research, sports coaching, and performance evaluation. By combining computer vision, artificial intelligence, and biomechanical analysis, the system demonstrates how modern digital tools can contribute to smarter and more scientific sports training.

Key Features

• Multi-view football shot video analysis

• Audio-based video synchronization

• Markerless pose estimation without wearable sensors

• Automatic foot-ball contact frame detection

• Knee, trunk, and ankle angle calculation

• Rule-based posture evaluation

• Visual feedback dashboard and posture scoring

• Practical application in sports training and biomechanics research

Potential Applications

• Football coaching and player training

• Sports biomechanics research

• AI-based performance assessment

• Injury-risk awareness and posture correction

• Academic research and product-based innovation

• Development of low-cost sports analytics tools

Developed under the innovation and research ecosystem of Amity Innovation and Design Centre (AIDC), Amity University Uttar Pradesh, this system reflects AIDC’s focus on transforming interdisciplinary research ideas into practical, product-oriented solutions.

This project  presents two complementary pieces of work carried out in the domain of embedded systems and Internet of Things (IoT) security, representing a progression from a foundational hardware prototype to an advanced, research-grade security framework. The first system, MoodBot: Robot Desk Buddy, is an Arduino UNO-based embedded desk companion that continuously senses ambient light intensity through a Light Dependent Resistor (LDR) and reports real-time lighting conditions — "BRIGHT", "DIM", or "DARK" — on a 16×2 I2C LCD display. It addresses the everyday problem of poor desk lighting that causes eye strain and reduced productivity for students, office workers, and hobbyists learning embedded systems, and establishes core competencies in sensor interfacing, I2C communication, and real-time feedback loops. Building on this embedded-systems foundation, the second and principal contribution of this report, MediShield, is a severity-aware adaptive security framework for energy-efficient healthcare IoT data protection. Unlike conventional wearable health monitors that apply a single strong cipher — typically AES-256 — to every physiological record regardless of clinical significance, MediShield derives a composite risk score (0–12) from four vital parameters (heart rate, blood-oxygen saturation, body temperature, and respiratory rate), classifies each incoming record into one of four severity tiers (Normal, Mild, Moderate, Critical), and binds each tier to a proportionate encryption algorithm (None, SPECK 64/128, AES-128, AES-256) and transmission policy. Evaluated on a labeled dataset of 25,493 real physiological records and demonstrated end-to-end through a browser-based dashboard, an ESP32 battery drain simulator, and a Python/Raspberry Pi sensor relay streaming data through a Google Sheets cloud pipeline, MediShield reduces cumulative encryption overhead by 88.96%, cloud transmissions by 46.41%, and total energy consumption by 65.52% — equivalent to nearly 2.9× longer battery operating life — while guaranteeing that every Critical-tier record continues to receive AES-256 protection. The severity classification engine achieves 100% rule-validation accuracy across the full dataset. Taken together, MoodBot and MediShield illustrate a coherent development trajectory: from a simple, sensor-driven embedded prototype that builds fundamental hardware and firmware skills, to a rigorously evaluated, clinically motivated adaptive security framework that addresses a real and pressing problem in resource-constrained healthcare IoT deployments.

AI Adaptive Placement Preparation & Learning Intelligence Ecosystem was created to help students in having a customized and effective learning process in preparation for placements. This system makes use of the concepts of adaptive learning, performance management, AI recommendations and assessment.

The objective of this project is to design and develop a Physical Smart Grid Energy Trading Diorama that demonstrates the generation, storage, monitoring, and sharing of electrical energy using renewable energy sources. 

 Advanced Educational Platform is a web-based application designed to provide an interactive, user-friendly, and efficient digital learning environment for students and educators. The platform aims to overcome the limitations of traditional learning systems by offering centralized access to educational resources, structured courses, and progress tracking through a modern and responsive interface. The application enables students to register, log in securely, browse available courses, enroll in learning programs, access educational content, and monitor their learning progress. Administrators can efficiently manage users, courses, educational materials, and other platform-related activities through a dedicated administration panel. The system emphasizes ease of use, accessibility, and an improved user experience while ensuring secure authentication and effective data management. The project has been developed using modern web technologies, including HTML, CSS, JavaScript, Java, JSP/Servlets, Apache Tomcat, and MySQL. These technologies provide a reliable, scalable, and maintainable architecture capable of supporting future enhancements and additional functionalities. The primary objective of this project is to create a comprehensive educational platform that simplifies online learning, enhances student engagement, and enables efficient management of educational resources. The proposed system offers a practical solution for educational institutions and online learning environments by integrating essential learning features into a single, centralized platform.

Health Polymedic is a family healthcare management application designed to store and organize medical information in a single secure platform. It simplifies healthcare management by providing quick access to records, prescriptions, medications, and appointments for every family member. Multi-profile family management Digital medical record storage Prescription & report upload Medication and appointment tracking Doctor directory with diagnosis management Dashboard for quick health summary Track Medicines & Appointments

• VeinScan is a passive, non-invasive vein detection device that uses red LED transillumination (620–680 nm) to help clinicians locate subcutaneous veins for painless IV insertion. The device shines red LED light through the skin; hemoglobin in the veins absorbs this light more than surrounding tissue, making veins visible as darker lines against the illuminated skin.
• It is a fully passive electronic design — an array of red LEDs, current-limiting resistors, and AA battery power — with no camera, no image processing, and no software/ML pipeline involved.
• The wand-style enclosure was modeled in CAD and produced through 3D printing, giving an ergonomic, lightweight handheld form factor for use at the bedside.
• By improving first-attempt vein visibility, VeinScan aims to reduce repeated needle pricks, patient discomfort, and time spent during blood draws and IV cannulation.
• The project was developed as part of the NTCC Internship-III at the Amity Innovation and Design Centre (AIDC Lab), covering design, prototyping, and testing phases.

Basically, 'No Fluff' is a Game Boy for future engineers. Researcher realized students love gaming but find textbooks boring. So, he built a handheld console where students don't just play games—students  learn how to build them. Students play a 'Story Mode' to learn electronics, and once Students  pass the levels, the console unlocks and becomes a remote control for a real robot car. We designed everything from scratch—the hardware, the code, and the car—to show that learning science can be as addictive as playing video games."

The proposed ESP32-based drone flight controller is designed as a low-cost, highly connected, and customizable alternative to conventional flight-control platforms. The system leverages the ESP32’s integrated dual-core processing, Wi-Fi, Bluetooth, SPI, I²C, UART, and PWM interfaces to combine flight control, wireless communication, telemetry, configuration, and system monitoring within a single compact platform.

Unlike conventional flight controllers that may require additional communication modules for wireless connectivity, the proposed architecture utilizes the ESP32’s built-in connectivity to reduce system complexity, component count, and overall cost. This enables easier integration with mobile applications, IoT platforms, ground-control systems, and cloud-based monitoring solutions.

The proposed system is not intended to directly replace high-end Pixhawk-class flight controllers, which are optimized for advanced autonomous, industrial, and safety-critical UAV applications. Instead, it targets the rapidly growing cost-sensitive, educational, experimental, and connected-UAV segment.

• Technical Feasibility: The core electronic setup pairs the ESP32 microcontroller (handling Wi-Fi telemetry and sensory data processing) with the BotWing F722 flight controller (managing real-time flight telemetry and stabilization via BLHeli ESCs and 2212 motors). Ground mobility is reliably isolated and driven by an L298N motor driver coupled with BO motors, eliminating data cross-contamination between the aerial and terrestrial drive systems.
• Manufacturing & Fabrication Feasibility: The structural mainframe utilizes rapid-prototyping workflows. Components are rendered via standard CAD software and manufactured using high-tensile, low-weight 3D printing materials (like PETG or Carbon Fiber PLA). Assembly utilizes standard electronic fabrication methods including localized soldering, isolated power distribution boards (PDB), and custom wiring harnesses to ensure durability under field stresses.
• Operational Feasibility: Field operators can seamlessly monitor and pilot the platform via a universal, browser-based monitoring system built using HTML, CSS, and JavaScript. The dual-mode command interface (long-range FlySky iBUS radio signals paired with local 2.4 GHz Wi-Fi telemetry) ensures redundant, low-latency control in dense urban environments or isolated terrain.

Autonomous Mothership -Child UAV

• Top Bar: A wide, green-bordered horizontal box at the top left featuring a gear and bar chart icon.
• Three Main Sections (Middle): A horizontal sequence of three rounded rectangular boxes, likely representing steps in a workflow or categories:
  • First Box: Features a stacked layer icon in the top left and an "ekAgracit" logo in the bottom right.
  • Second Box: Features a sliders/settings icon in the top left.
  • Third Box: Features a location pin icon in the top left.
• Bottom Section: A large container holding a list mapping gray rounded rectangles to green-outlined rounded rectangles via dark blue arrows. This is structured like a multi-item key-value mapping or input-to-output workflow.

• The Mobile Layer (AI Pod): Powered by an Nvidia Jetson Nano, this handheld device utilizes dual RGB and thermal cameras. It handles compute-heavy computer vision tasks directly on the edge to diagnose crop diseases and identify localized water stress.
• The Fixed Layer (Sensor Mast): A low-power ESP32 microcontroller acts as the field's always-on vitals monitor. It continuously logs ambient temperature, air humidity, ambient light, soil moisture, and pest activity via an integrated trap.
• The Decision Engine: A local Rules Engine ingests the environmental data from the mast and the visual data from the pod. It processes these inputs to generate a prioritized, ranked list of actionable insights, ensuring every recommendation is backed by a transparent technical reason.
• The Interface (Farmer App): Operating over a localized Wi-Fi network hosted by the hardware, the app delivers simple, high-utility advice in Hindi and English, bypassing the need for a cellular data plan.

• Early Detection: Catches pests and crop diseases early at the edge before they can spread.
• Nutrient Management: Flags pale or weak crop patches by analyzing changes in leaf color.
• Smart Irrigation: Calculates daily crop water requirements on-device using local weather data and the industry-standard FAO-56 Penman-Monteith equation.
• Microclimate Tracking: Monitors heat and drought indicators by recording temperature and humidity fluctuations throughout the day.

• 100% Offline Autonomy: Zero reliance on cloud servers or internet access, making it highly resilient in remote areas.
• Hybrid Sensing: Combines the targeted, high-resolution scouting of a mobile pod with the continuous, baseline tracking of a fixed mast.
• Honest AI Architecture: Designed to output "uncertain" rather than hallucinating or guessing when data is ambiguous, building deep trust with the farmer.
• Scalable Form Factor: A modular design that can start as a handheld unit and seamlessly transition to a tractor or drone mount as the farm scales.

• Targeted Application: Delivers precise micro-doses of pesticides directly to the crop canopy, drastically reducing chemical runoff.
• Autonomous Navigation: Traverses complex row crops independently using advanced GPS and obstacle-avoidance sensors.
• Real-Time Monitoring: Collects field data and crop health insights while executing its spraying routes.

12 degrees of freedom; four legs with three actuated joints per leg; servo-based actuation; gait-generation software; inverse-kinematics and coordinated leg-control capability. Control software was developed for a 12-DoF quadruped robot dog to coordinate the motion of its four independently actuated legs. The software manages individual joint positions while combining them into repeatable leg trajectories and walking sequences, forming the basis for stable locomotion. The implementation supports structured gait control and can be extended with inverse kinematics, body-position adjustment and sensor feedback, providing a reusable software foundation for walking, turning, posture control and further autonomous behaviour on a multi-legged robotic platform.

Specifications: F450 quadcopter platform; ESP32 microcontroller; external flight controller; serial/command interface; four-motor brushless propulsion system; programmable command translation. An F450 quadcopter was integrated with an ESP32-based command-processing layer in which the ESP32 receives or generates high-level control instructions, parses them and sends the corresponding commands to the drone's flight controller. This architecture separates communication and intelligent command handling from low-level flight stabilisation, allowing the ESP32 to act as an interface for wireless control, autonomous routines or higher-level applications. The project demonstrates how a conventional quadcopter can be enhanced with programmable embedded control without replacing the dedicated stabilisation functions of the flight controller.

Quadcopter UAV; onboard/streamed camera feed; computer-vision based person detection; AI-assisted scene analysis; event/flag generation; aerial surveillance workflow. An AI-enabled surveillance UAV was developed around a quadcopter platform to use aerial camera imagery for automated observation rather than relying only on continuous manual monitoring. The system processes the camera feed to identify people and analyse relevant visual situations, after which detected events can be flagged for operator attention. By combining aerial mobility with computer vision, the project demonstrates a scalable approach to intelligent surveillance for campus monitoring, restricted-area observation, search operations and other scenarios in which rapid visual coverage and automated detection can improve situational awareness.