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.
Highlights
Intelligent IoT-based ambient assisted living systems (AALS) for the elderly have been a major research focus area in recent times
We have been working on the development of an FDS which applies the biological profile of a subject to classify him into a risk category pertaining to his fall probability. e three categories we have defined are high risk, medium risk, and low risk. e categorization derived, along with parameters from a wearable sensor, is applied to Machine learning (ML) algorithms to detect falls. e objective of this paper is to bring out an extensive literature survey of the recent work in the area of fall detection systems, with focus on the application of machine learning to wearable sensor-based approaches
We examine the desirable requirements of a wearable fall detection system. en we present an overview of FDSs based on environmental sensors, vision-based systems, and wearable sensors
Summary
Intelligent IoT-based ambient assisted living systems (AALS) for the elderly have been a major research focus area in recent times. E former is easier to implement, while the latter has the advantage that it can provide a clear picture of the impact to the observer before deciding a course of action Latency is another factor that should be considered in the design of FDSs. Delay in the detection of falls and that between the detection of falls and notification of the caregiver should be minimized for the FDS to be effective. It implies that the network design should provide high quality of service for data packets that are generated as a result of fall detection, in comparison with messages for keepalive or periodic reporting of sensor readings It is desirable for an FDS to keep track of a subject’s biological parameters and fall history so as to have the capability of predicting falls before their occurrence. It is desirable for an FDS to keep track of a subject’s biological parameters and fall history so as to have the capability of predicting falls before their occurrence. is would involve reporting of the biological parameters by the sensor nodes periodically and application of data analytics and machine learning techniques on the data collected over a period of time
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