IIT Hyderabad: Researchers Develop Device to Monitor Use of Mobile Phones

Indian Institute of Technology, Hyderabad, researchers have created a device to monitor the usage of mobile phones that are activated automatically.

IIT-H has released a statement stating that the researchers at the institute have developed a new technique to detect mobile phone usage through artificial intelligence (AI) based algorithms.

Dr Sparsh Mittal headed the research along with his team of scholars, including Poonam Rajput and Subhrajit Nag of the Computer Sciences Engineering department.

The team announced that the results of the work have been accepted in the peer-reviewed conference, "International Conference on Smart Objects and Technologies for Social Good 2020," organized at Belgium 2020.

While mentioning the importance of the research, Dr Sparsh Mittal explained that mobile phones have deeply penetrated people's lives. Mobile-phone addiction has become the main concern for many parents, lawmakers, and the authorities at educational institutes and offices. To manually detect the usage of mobile-phone at a large-scale is just impossible.

"We believe that our technique has immense potential. It can help improve productivity by preventing excess or untimely usage of the mobile phone. It can help in avoiding accidents due to distraction during driving,” said Dr Sparsh Mittal.

It is the need of the hour to create automated techniques for this purpose as it will be beneficial for everyone in the long run.

Mobile phones are prohibited in various places like petrol pumps, exam-halls, embassies, military bases, and courts of law. This device will help to find whether the mobile phone is being used in these prohibited areas.

“Finally, many financial transactions now happen on mobile phones, and the loss of mobile phones can have severe consequences. By allowing the tracking of mobile phones, our technique can help in detecting loss or theft of the mobile phone,” Dr. Sparsh Mittal stated.

Poonam Rajput, a research scholar at IIT-H, stated that mobile phones are relatively small objects when seen in photos or videos using surveillance or CCTV cameras.

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Following this, many existing object detection algorithms fail to detect it correctly. Moreover, mobile phones come in different sizes and shapes, such as feature phones and smartphones which makes the process more difficult.

“We have used algorithms based on deep-learning to detect mobile-phone usage. Our fastest algorithm runs at nearly 27 frames-per-second on a high-end GPU, which means it can process a video in real-time," she added.

Apart from this, Dr Sparsh Mittal said, “It is also known that the algorithms have achieved nearly 99 per cent accuracy on the Kaggle Driver dataset and 96 per cent accuracy on the IIT-H-DMU dataset. It is the first to detect mobile phone usage in both indoor and outdoor environments with a lot of clutter and other background objects.”

Subhrajit Nag, another scholar at the institute gave his observations on the dataset collection and said, "A crucial challenge we faced was that deep-learning algorithms require a massive amount of training data to make robust predictions. However, the research team has come over the challenges posed with the help of the datasets of IITH-DMU and Kaggle Driver.”

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