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Students of IIT Kharagpur Develop AI App to Lend Support for Elderly Care

While one of these apps can be installed on the phones of the elderly, the other can be attached to the smartphone

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IIT, AI, Elderly Care
The app named 'CARE4U', developed by an interdisciplinry team of 2nd year B.Tech students, connects the caregiver to the elderly. Pixabay

Aiming to lend support to geriatric care by aiding caregivers to reach out to the aged on time, students of the Indian Institute of Technology (IIT) Kharagpur have developed two interconnected android smartphone applications, an official said on Monday.

The app named ‘CARE4U’, developed by an interdisciplinry team of 2nd year B.Tech students, connects the caregiver to the elderly. While one of these apps can be installed on the phones of the elderly, the other can be attached to the smartphone of the caregiver.

“The neural network-based fall detection algorithm in the app installed on the phone of the elderly can detect whether the elderly has fallen down. If there is a fall, it automatically calls the caregiver and emergency services with the location of the elderly person,” a statement said.

The other features include detecting emotion by taking a picture and calculating the mood index. To make this feature more effective the team has developed a cognitive intelligent chatbot for the elderly person to engage with.

IIT, AI, Elderly Care
Aiming to lend support to geriatric care by aiding caregivers to reach out to the aged on time, students of the Indian Institute of Technology (IIT) Kharagpur have developed two interconnected android smartphone applications, an official said on Monday. Pixabay

“We customized it to recognize the current mood of the person and, accordingly, fine-tune its conversations with that of the person. For example, the chatbot can recommend a motivational quote or an old song when the person is sad,” said team member Kanishka Haldar from the Department of Electronics and Electrical Communication Engineering.

Moreover, the app can also do other activities like make a call, send a text, book a cab and so on. It also has a record of medical histories, an account of allergies, an SOS button, real-time location tracking, among others.

The ‘CARE4U’ app includes a ‘Medicine Reminder’ feature to remind both the elderly person as well as the caregiver that it is time for the former to take medicine.

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The app recently won the IIT Kharagpur team the first runners-up position at a nationwide hackathon called vesAIthon’19. (IANS)

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This AI can Detect Low-Sugar Levels Without any Fingerprick Tests

AI can spot low-glucose levels without fingerprick test

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Sugar test
Current methods to measure sugarrequires needles and repeated fingerpicks over the day. Pixabay

Researchers have developed a new Artificial Intelligence (AI)-based technique that can detect low-sugar levels from raw ECG signals via wearable sensors without any fingerprint test.

Current methods to measure glucose requires needles and repeated fingerpicks over the day. Fingerpicks can often be painful, deterring patient compliance.

The new technique developed by researchers at University of Warwick works with an 82 per cent reliability, and could replace the need for invasive finger-prick testing with a needle, especially for kids who are afraid of those.

Sugar test
Fingerpicks are never pleasant for a sugar-level test and in some circumstances are particularly cumbersome. Pixabay

“Our innovation consisted in using AI for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping,” said Dr Leandro Pecchia from School of Engineering in a paper published in the Nature Springer journal Scientific Reports.

Two pilot studies with healthy volunteers found the average sensitivity and specificity approximately 82 per cent for hypoglycaemia detection.

“Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in paediatric age,” said Pecchia.

Also Read- Researchers Find a New Mechanism to Prevent Obesity

The figure shows the output of the algorithms over the time: the green line represents normal glucose levels, while the red line represents the low glucose levels.

“Our approach enable personalised tuning of detection algorithms and emphasise how hypoglycaemic events affect ECG in individuals. Basing on this information, clinicians can adapt the therapy to each individual,” the authors wrote. (IANS)