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According to the World Health Organization (WHO), the number of individuals aged 60 or older is projected to reach around 2 billion by 2050, a significant increase from 900 million in 2015, constituting approximately 22% of the global population. Nevertheless, aging is commonly associated with a decline in physical, sensory, and cognitive abilities, which elevates the risk of experiencing falls. It has been reported that around 28-35% of elderly individuals aged 65 and above require hospitalization due to falls. The risk of falling further escalates with advancing age. For elderly adults aged 70 or older, the likelihood of falling stands at 32-42%, with the added burden of sustaining moderate to severe injuries, including fractures of the wrist, arm, ankle, and hip. 6 To prevent such issues, the author presents a machine learning method exploiting Gait Analysis. The proposed machine learning method uses walking patterns to test falls and non-falls. Ultimately, the model undergoes testing on publicly gathered datasets encompassing diverse activities and various types of falls. This holds considerable potential in addressing the issue of falls by promptly alerting emergency and relevant teams to take essential measures. It is expected to accelerate the assistance process, minimize the likelihood of prolonged harm, and potentially save lives.To achieve this, gait parameters such as human gait and balance, step time, stride time, step length, stride length, step velocity, and step count on heel and toe are gathered using Inertial Measurement Units (IMU) comprising accelerometer, gyroscope, and magnetometer. The real-time gathered data is then analyzed using the pattern recognition algorithm, Random Forest. On the other hand, health parameters such as body temperature, blood pressure, and pulse rate are checked.The real-time gait data is checked along with health parameters, and based on this information, an intimation is given through a mild alarm to the elderly persons. The article represents the novelty with six parameters and a mild alarm, contributing to fall prevention and improved safety for elderly individuals.
Anitha et al. (Fri,) studied this question.