IIT Jodhpur Develops Human Breath Sensor for Health Monitoring

The development of this human breath sensor stems from the growing need for a quick, affordable, and non-invasive health monitoring device.

Researchers at the Indian Institute of Technology Jodhpur have developed a human breath sensor, claimed to be the first of its kind "Made in India" product based on metal oxides and nanosilicon.

The primary purpose of this device is to measure alcohol content in the breath, particularly in cases of drunk driving. However, with adjustments in sensing layers and the incorporation of an array of sensors (resembling an electronic nose), the sensor can also prove invaluable for characterizing various diseases.

This includes asthma, diabetic ketoacidosis, chronic obstructive pulmonary disease, sleep apnea, and cardiac arrest, where volatile organic compounds (VOCs) in the breath are monitored.

Addressing the Need for Non-Invasive Health Monitoring

The development of this human breath sensor stems from the growing need for a quick, affordable, and non-invasive health monitoring device. Given the increasing concerns about the adverse impact of air pollution on human health and the environment, the researchers sought to create a device that addresses these challenges.

While existing sensors are based on fuel cell technology or metal oxide technology, the IIT Jodhpur team aimed to develop a breath VOC sensor that is not only cost-effective but also operates at room temperature.

In addition to the alcohol content measurement for drunk driving cases, the team has also developed a breath monitoring sensor based on partially reduced graphene oxide, showcasing the versatility of the technology.

The research, conducted by Nikhil Vadera, a PhD student, and Dr Saakshi Dhanekar, an associate professor, was published in IEEE Sensors Letters.

Key Features

The device is an electronic nose with a room-temperature operable heterostructure, combining metal oxide with nanosilicon. Further, the sensors react with alcohol in the breath sample, causing a change in resistance proportional to the alcohol concentration.

The breath monitoring sensor has ML algorithms that process the data collected from the sensor array, identifying patterns of different breath components and segregating alcohol from the mixture of VOCs.

Additionally, the sensor operates at room temperature and functions as a plug-and-play device.

Potential Applications Beyond Alcohol Detection

The electronic nose developed at IIT Jodhpur holds promise for broader applications beyond alcohol detection. It can be adapted for VOC monitoring in the environment and the detection and measurement of other breath biomarkers for various diseases.

The research received funding from the Biotechnology Ignition Grant Scheme (BIG), the Biotechnology Industry Research Assistance Council (BIRAC), the Science and Engineering Research Board (SERB), and the Ministry of Micro, Small, and Medium Enterprises (MSME).

Regarding the future scope of the research, Dr Dhanekar said, "Continued research and development in these directions could lead to the practical implementation of the breath diagnostics in various fields, ranging from healthcare and wellness to wearable technology and IoT applications. The output of the sensors can be connected to Raspberry Pi and the data can be sent to a doctor or phone."

Dr Dhanekar, associated with the start-up Sensekriti Technology Solutions, emphasized the commitment to innovation for the benefit of society. According to him, the team's approach involves utilizing creativity, perseverance, and exceptional teamwork to address challenges and contribute to advancements in healthcare technology.

The IIT Jodhpur researchers' development of the human breath sensor offers a versatile and accessible solution with far-reaching implications for disease diagnostics and environmental monitoring.

In another development, in November, researchers from the IIT Jodhpur introduced an innovative computer-aided diagnosis (CAD) system for the early detection of hypertensive retinopathy (HR).

The CAD-based approach utilizes deep learning technology, offering a cost-effective, portable, non-invasive, and time-efficient solution for HR diagnosis, as detailed in the biomedical signal processing and control journal.