Objective

To design and develop a wearable health activity tracker that lets individuals monitor their health and fitness metrics in real time. The device captures heart rate, SpO₂, body temperature and ECG, and analyses this data using AI/ML so that users gain meaningful insight into their overall health and can make informed decisions to improve their well-being.

1. Research and analysis. Review existing wearable health and activity trackers to understand current technology and user needs; identify the features users value most — accuracy, comfort, durability, battery life and data synchronisation.
2. Hardware development. Design the wearable with attention to form factor, ergonomics and materials. Integrate an accelerometer, heart-rate monitor and gyroscope for accurate data capture, and select wireless protocols for seamless connectivity with smartphones and other devices.
3. Software development. Build a user-friendly mobile application that syncs with the device and presents health and activity data through an intuitive interface. Apply algorithms and machine learning to interpret the data and deliver actionable, personalised recommendations.
4. Testing and validation. Rigorously test accuracy and reliability across physical activities, sleep patterns and environmental conditions, and refine the design using feedback from a diverse user group.

The project delivers a wearable that empowers individuals to take control of their health. Through careful research, hardware and software development, rigorous testing and user feedback, it aims to be an innovative solution that fosters healthier lifestyles and improved overall fitness.

A cloud-based 360-degree view platform gives a comprehensive, immersive experience of the institute’s amenities with fast, seamless navigation. Stakeholders can virtually explore libraries, laboratories, sports complexes and student accommodation, gaining a complete picture of the campus infrastructure — a convenient way for prospective students, faculty and visitors to explore Thapar Institute remotely.

• Prof. Maninder Singh — Professor, CSED (Dean of Academic Affairs, TIET)
• Dr. Vinay Arora — Associate Professor, CSED (Associate Dean of Academic Affairs, TIET)
• Shubhankar Sharma and Lakshya Gupta — 3rd Year, Batch 2021–2025

SureWash is part of GLANTA, founded in 2011, which has developed new techniques in gesture recognition and augmented reality since its inception. GLANTA’s mission is to deliver working applications for camera-based algorithms — SureWash’s hand-hygiene training technology is a prime example.

SureWash teaches hand hygiene through interactive training devices with a live camera that assesses the user’s technique and provides real-time feedback. The software captures the live camera feed containing the user’s hands, performs hand detection in the frame, classifies hand-hygiene gestures using deep learning, and displays feedback to the user. The product is installed in hospitals around the world to maintain hygiene and reduce infections.

A portable, lightweight and precise fully automatic wearable blood-extraction device worn on the arm. An embedded system built around a pre-programmed Arduino LilyPad microcontroller controls every action automatically. High-definition thermal cameras image the veins; the vein with the highest blood flow closest to the skin is selected automatically, after which a mover carrying a syringe travels around the arm, locates the venipuncture point and extracts the required quantity of blood. No assistance is needed — the sample is drawn automatically.

Diagnostic blood testing is the most common medical routine in the world — performed around 2 billion times a year in the U.S. alone. Yet blood-draw success depends heavily on practitioner skill and patient physiology, and results are generated in centralised labs from large-volume samples using labour-intensive techniques. This work proposes a portable, precise, fully automatic alternative.

1. Prepare a new image database of Indian patients for the proposed work.
2. Develop a portable, lightweight, wearable automated blood-extraction device.
3. Develop the in-device framework that prioritises veins and extracts blood in a consistent, robust and efficient manner.
4. Test and validate the device’s effectiveness clinically.

The microcontroller, mounted on the device, actuates and controls every mechanical component so the process runs automatically; inputs and outputs pass through it. The workflow:

1. The device is worn on the patient’s arm; a palm switch detects the arm and switches the circuit on.
2. The user enters the required blood-sample volume via the keypad.
3. Three LED panels illuminate and the high-definition thermal cameras scan the arm.
4. The captured images are sent to the Arduino LilyPad, which processes them and detects the best vein for venipuncture.
5. The vein with the highest priority — maximum blood flow, clearest image and closest to the skin — is selected to minimise the risk of injury.
6. If no suitable vein is found on the upper arm, the same criteria are checked on the forearm near the palm.
7. The device checks syringe availability; if syringes are present the mover is directed to puncture the vein, otherwise the syringe reserve channel is refilled.
8. The microcontroller determines the X–Y coordinates of the puncture point and drives the mover there via an X-axis ring and Y-axis shaft; a bearing-coupled ring transfers motion friction-free to the parallel ring.
9. The mover extracts blood from the arm autonomously.
10. At the venipuncture point a stepper motor pushes a syringe from the reserve channel down an inclined channel (≤30°); a permanent magnet seats it in the syringe holder.
11. On a signal from the processing unit the electromagnet turns on and moves magnet M2 towards M1.
12. M2 joins M1, connecting the syringe holder to the capillary tube.
13. Blood is extracted through the capillary tube by the critical vacuum pressure created in the vacuum chamber.
14. Suction carries the blood through the capillary tube into the test tube.
15. The vacuum valve closes and reverse current through the electromagnet repels the syringe into the disposal section.
16. After all stages, a proof-of-concept model based on 30 patients will be prepared.