Abstract
Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.
Highlights
With the world’s population ageing, there is an increasing focus on age-related health issues.Fall-related injuries are the primary cause of injury and mortality in the elderly
A few ADLs were detected as falls, especially highly dynamic motions such as jogging and quickly sitting on a low-height mattress and getting up
A complementary filter with a proportional integral (PI) controller was used to calculate accurate vertical angle values which resulted in 100% sensitivity and 97.54% specificity with our experimental data, which included highly dynamic motions
Summary
With the world’s population ageing, there is an increasing focus on age-related health issues. Fall-related injuries are the primary cause of injury and mortality in the elderly. Reduced levels of bone density, decreased muscle mass, as well as slow reflexes increase the risk of fall-related injuries in the elderly [2,3]. There is a social dimension to this issue, because it mandates special care for the elderly. Fall-related medical expenses in the elderly were reported to have accounted for as much as USD 50 billion for nonfatal falls and 754 million for fatal falls in the year 2015 alone [5]. Falls and fall-related injuries are major sources of physical, social, and economic problems among the elderly
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