A Survey on Recent Advances in Wearable Fall Detection Systems

Abstract

With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls

“…Several researchers have already used machine learning for fall detection [ 29 ]. Different classifiers have been used to detect falls, such as support vector machines (SVM) [ 19 , 25 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], Naïve Bayes [ 33 , 36 , 37 ], logistic regression [ 30 , 34 , 36 ], k-nearest neighbors (KNN) [ 31 , 33 , 34 , 36 , 38 ], decision trees and random forests [ 33 , 36 , 39 ], artificial neural networks, and deep learning [ 40 , 41 , 42 ].…”

Vision-Based Fall Detection Using ST-GCN

“…The results were very good but the threshold method is generally not robust since thresholds themselves depend on the subject's characteristics such as sex and age. [21]. Also, using both a camera and an accelerometer adds complexity due to the additional problem of synchronization.…”

Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-Directional LSTM

“…A wearable sensor-based fall detection system determines their motion status or position information by sensors worn on the older person's body. Most of the researches are based on accelerometers in the study [38][39][40][41][42][43]. The acceleration sensor detection system determines whether or not to fall by analyzing and collecting the acceleration of multiple axes.…”