In a paper published by the International Journal of Medical Engineering and Informatics, a team from the Amrita School of Engineering, in Coimbatore, India, provided details of what they refer to as a “frugal and affordable system” that can monitor a person’s movements. The system uses motion sensors and data analytics to determine whether a particular motion of an old person is indicative of a fall. The paper was  authored by Kowshik, G., Anudeep, J., Krishna, P.V., Vasudevan, S.K. and Shah, I. (2020) and titled, ‘An Inventive and Innovative System to Detect Fall of Old Aged Persons – a Novel Attempt with IoT, Sensors and Data Analytics to Prevent the Post Fall Effects’, Int. J. Medical Engineering and Informatics, Vol. 12, No. 1, pp.1–18.

The system can alert a carer, friend, or relative to come and assist after a fall. One of the biggest problems in a fall is sustaining a hip fracture and it is often the hospitalisation and ensuing complications that lead to a fatality. Attending quickly to the person who has fallen is often critical in reducing morbidity and the ongoing risk of mortality. 

Falls account for a lot of morbidity and mortality among older people. According to the World Health Organisation, almost 40 million falls are recorded each year with around 650,000 of those ultimately leading to the person’s death.  The team explained that 20 to 30 percent of older people who have a fall suffer moderate to severe physical injuries such as broken bones, cuts, and bruises. There are often ongoing mental health issues caused by the embarrassment and loss of self-esteem associated with a fall, as well as the mobility problems that arise and decreased physical activity. 

The team’s system utilises various sensors, an accelerometer, piezo sensor, infrared sensor, and a gyroscopic motion sensor. The output from these is fed to a microcontroller and a wireless transmission module (Bluetooth in the prototype, but Wi-Fi would be plausible) to transmit the output to a receiver, which quickly ports the data to a server and the data analytics to generate an answer regarding whether or not the user has fallen. The server-side system can then trigger an alert if the person has fallen. The team suggests that the same device might also incorporate a heart-rate monitor to add an extra layer of useful data for carers and emergency healthcare. The team has demonstrated efficacy with the prototype and describes it as “foolproof”. 

Cross posted from InterScience: Reducing the impact of a fall

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