Wearables

As part of HTIC’s vision to develop clinical grade, accurate, reliable and non-invasive continuous vital sign monitoring solutions, Wearables team comprising of Engineers, MS & PhD research scholars from IIT Madras and undergraduate & post-graduate engineering interns was devised in 2014. Wearables team has collaborations with leading industries as well as universities around the world. All the devices developed by Wearables team undergoes two stages of validation which involves an in-house preclinical before clinical validation in hospitals. Currently, Wearables is a 15-member team with expertise in hardware design, embedded software design, signal processing & algorithm development, mechanical design, interface design involving android application, web application, prototype testing and clinical validation. The team develops embedded solutions for continuous vital signs monitoring in a wearable form factor equipped with wireless connectivity.

VitalSens EAS based ECG patch

VitalSens EAS based ECG patch is a platform technology to obtain clinically relevant ECG using EAS lead system with wireless connectivity, along with activity and temperature monitoring. The device connects wirelessly to a gateway device (smartphone/tablet) via Bluetooth Low Energy(BLE) technology and streams raw ECG, accelerometer data and temperature to the gateway. The gateway device pushes data on to the cloud application which runs algorithm for detection of abnormal behaviour and provides visualisation options for clinicians.

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Wrist worn optical heart rate monitor

A wrist-worn reflectance pulse oximeter capable of user initiated blood oxygen saturation level and pulse rate measurement from finger, which works along with a gateway device and pushes data onto a cloud application for analysis. Motion corrupted sections of PPG are cancelled out using the three axes digital accelerometer. Finger based reflectance blood oxygen saturation level monitor is based on Toshiba platform and has a dual PCB stack up architecture. MAX30100 from Maxim Integrated is the reflectance SpO2 sensor used in the design.

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Wrist Worn SpO₂ Monitor

Continuous monitoring of blood oxygen saturation (SpO2) level and heart rate is critical in surgery, ICUs and patients suffering from Chronic Obstructive Pulmonary Diseases. Presence of motion artifacts in PPG signals is a major obstacle in the extraction of reliable cardiovascular parameters, in real time and continuous monitoring applications. HTIC has developed a wrist worn device with a custom finger probe with an integrated accelerometer to remove motion artifacts.

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Pressure ulcer wearable sensor

Hospital acquired pressure ulcers (HAPUs) is a major problem that affects around one in twenty patients who are admitted in hospital with sudden illness. These ulcers often occur when patients have limited mobility and cannot change positions in bed on their own. The proposed wearable device continuously monitors the patient’s position and communicates wirelessly with a tablet which enables alerts to be sent to the caregiver when a patient turn is due in accordance with the protocol adopted by the hospital.

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Respiratory Rate Monitor for Neonates

Visual observation of expansions and contractions of the abdomen or diaphragm of the neonate is still the widely accepted measure of respiratory rate in most clinical settings. The wearable device will be attached to the subject’s sternum or abdomen, the respiratory rate is continuously monitored using a 3-axis accelerometer. The device works in conjugation with a gateway device for cloud connectivity. The data is sent to a cloud application where it can be visualized and analysed by physicians and caretakers.

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Stress Monitoring

Electrodermal Activity (EDA) is a measure of stress response from the skin which is exclusively innervated by the sympathetic nervous system. EDA obtained from participants in an induced stress test was used to train a machine learning model. Stress and non-stress regions along with identification of ‘motion affected’ regions with high accuracy. To establish the effectiveness of this algorithm in detecting stress in a naturalistic environment, a clinical validation study on preoperative anxiety and stress was done.

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ECG: Deep Learning for Arrhythmia Classification

Electrocardiography signals are manually interpreted for the diagnosis of cardiac arrhythmias. For efficient screening of arrhythmia from long term ECG data, an automated ECG interpretation is required. However, the existing automated ECG interpretation devices require extensive pre-processing and knowledge to determine relevant features. The proposed networks are end to end networks which can be directly trained without any pre-processing. The network would also be easily adaptable to multiple datasets requiring minimal training only on the final three layers through use of transfer learning.

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Publications:

 

  1. Amalan S, Shyam A, Anusha A S, Preejith Sp, Tony Akl, Jayaraj Joseph, Mohanasankar Sivaprakasam. “Electrodermal Activity based Classification of Induced Stress in a Controlled Setting”, 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy (Accepted)

  2. Balamurali Murugesan, Vignesh Ravichandran, Sharath M Shankaranarayana, Keerthi Ram, Preejith SP, Jayaraj Joseph, Mohanasankar Sivaprakasam. “ECGNet: Deep Network for Arrhythmia Classification”, 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy (Accepted)

  3. Antony Raj A, Preejith S P, Vijai Shankar Raja, Jayaraj Joseph and Mohanasankar Sivaprakasam, "Clinical Validation of a Wearable Respiratory Rate Device for Neonatal Monitoring", 2018 40th International Conference of the IEEE Engineering in Medicine and Biology. Honolulu, HI. (Accepted)

  4. Kiruthiga A, Annamol Alex, Balamugesh T, Dinesh Prabhu, Christopher D J, Preejith SP, Jayaraj Joseph, Mohanasankar Sivaprakasam, “Reflectance Pulse Oximetry for Blood Oxygen Saturation Measurement from Diverse Locations - A Preliminary Analysis”, 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy (Accepted)

  5. Ganesh Arvind C, Renganathan BS, Rajakumaran C, Preejith Sp , Shubham Khandelwal, Jayaraj Joseph, Mohanasankar Sivaprakasam, “Post-Stroke Rehabilitation Monitoring Using Wireless Surface Electromyography: A Case Study”, 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy (Accepted)

  6. P. Mohapatra, P. S. Premkumar and M. Sivaprakasam, "A Yellow-Orange Wavelength-Based Short-Term Heart Rate Variability Measurement Scheme for Wrist-Based Wearables," in IEEE Transactions on Instrumentation and Measurement, vol. PP, no. 99, pp. 1-11.

  7. A. S. Anusha, J. Joy, S. P. Preejith, J. Joseph and M. Sivaprakasam, “Differential Effects of Physical and Psychological Stressors on Electrodermal Activity,” 39th Annual. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC’17), Jeju Island, 11-15 July, 2017.

  8. Preejith S P, A. Jeelani, P. Maniyar, J. Joseph and M. Sivaprakasam, "Accelerometer based system for continuous respiratory rate monitoring," 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA, 2017, pp. 171-176.

  9. Ganesh Raam K, A. Jeelani, Preejith S P, S. Nagaiyan, J. Joseph and M. Sivaprakasam, "Design, development and clinical validation of a novel urine output monitor," 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA, 2017, pp. 188-192.

  10. Preejith S P, R. Hajare, J. Joseph and M. Sivaprakasam, "High altitude study on finger reflectance SpO2," 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA, 2017, pp. 198-203.

  11. Renganathan B S, Preejith S P, S. Nagaiyan, J. Joseph and M. Sivaprakasam, "System design to prevent Ventilator Associated Pneumonia," 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rochester, MN, USA, 2017, pp. 165-170.

  12. T. Vuorinen et al., "Printed, skin-mounted hybrid system for ECG measurements," 2016 6th Electronic System-Integration Technology Conference (ESTC), Grenoble, 2016, pp. 1-6.

  13. A. S. Anusha, S. P. Preejith, J. Joseph and M. Sivaprakasam, "Design and implementation of a hand-to-hand multifrequency bioimpedance measurement scheme for Total Body Water estimation," 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Torino, Italy, 2017, pp. 1-6.

  14. P. Mohapatra, S. P. Preejith and M. Sivaprakasam, "A novel sensor for wrist based optical heart rate monitor," 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Torino, Italy, 2017, pp. 1-6.

  15. S. P. Preejith, M. Sivaprakasam, and J. Venkatakrishnan, “An ocular compression device for reduction of elevated post anesthetic intraocular pressure,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp. 4819–4822.

  16. S. P. Preejith, A. Alex, J. Joseph, and M. Sivaprakasam, “Design, Development and Clinical Validation of a Wrist-based Optical Heart Rate Monitor,” in 2016 10th IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2016.

  17. A. S. Anusha, S. P. Preejith, J. Joseph, and M. Sivaprakasam, “Design and Preliminary Analysis of a Multifrequency Bioimpedance Measurement scheme,” in 2016 10th IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2016.

  18. S. Karthik, S. P. Preejith, J. Joseph, and M. Sivaprakasam, “A Reflectance Photoplethysmography Based Device to Detect Circulatory Disruptions,” in 2016 10th IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2016.

  19. S. P. Preejith, R. Dhinesh, J. Joseph, and M. Sivaprakasam, “Wearable ECG Platform for Continuous Cardiac Monitoring,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.

  20. S. P. Preejith, A. S. Ravindran, J. Joseph, and M. Sivaprakasam, “A Wrist Worn SpO2 Monitor with Custom Finger Probe for Motion Artifact Removal,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.

  21. B. S. Renganathan, S. P. Preejith, J. Joseph, and M. Sivaprakasam, “A Novel System to Address Hospital Acquired Pressure Ulcers,” in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016.

 

 

Healthcare Technology Innovation Centre
No. 1, 5th Floor, 'C' Block, Phase-II,

IIT Madras Research Park, Kanagam Road, 

Taramani Chennai-600113. 

Tamil Nadu, India.

 

Phone: +91-044-66469830

Email: contact@htic.iitm.ac.in

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