Fall detection using accelerometer-based smartphones: Where do we go from here?

Abstract

According to World Health Organization statistics, falls are the second leading cause of unintentional injury deaths worldwide. With older people being particularly vulnerable, detecting, and reporting falls have been the focus of numerous health technology studies. We screened 267 studies and selected 15 that detailed pervasive fall detection and alerting apps that used smartphone accelerometers. The fall datasets used for the analyses included between 4 and 38 participants and contained data from young and old subjects, with the recorded falls performed exclusively by young subjects. Threshold-based detection was implemented in six cases, while machine learning approaches were implemented in the other nine, including decision trees, k-nearest neighbors, boosting, and neural networks. All methods could ultimately achieve real-time detection, with reported sensitivities ranging from 60.4 to 99.3% and specificities from 74.6 to 100.0%. However, the studies had limitations in their experimental set-ups or considered a restricted scope of daily activities—not always representative of daily life—with which to define falls during the development of their algorithms. Finally, the studies omitted some aspects of data science methodology, such as proper test sets for results evaluation, putting into question whether reported results would correspond to real-world performance. The two primary outcomes of our review are: a ranking of selected articles based on bias risk and a set of 12 impactful and actionable recommendations for future work in fall detection.

Technological Assessment of Smart Wearables and 5G-Integrated Edge Computing for Real-Time Health Monitoring

“…The phones connected to the same tower for only circa 80 % of the time. Smart wearables providing more sensors could simulate higher data loads and enable other use cases like activity recognition [9], fall detection [10,11], or detecting deteriorating clinical conditions using the respiration sensors [12]. Finally, 44 patients should not cause high data loads on the mobile network as 5G will support up to 10 6 connections per m 2 [13], but experiments with more volunteers were not possible because of lacking number of devices.…”

An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection

“…Falls pose a significant risk to the elderly, leading to hospitalizations, increased morbidity, and substantial healthcare expenses. According to the World Health Organization (WHO), falls are the second leading cause of accidental or unintentional injury deaths worldwide, with adults over 65 being the most vulnerable group [1]. The statistics are alarming: more than 37.3 million falls requiring medical attention occur annually, and this number is expected to rise with the global increase in the elderly population [2].…”

The use of deep learning for smartphone-based human activity recognition

“…Human activity recognition (HAR) is an emerging field in health research that seeks to better understand human movements and behaviors (1). Researchers are increasingly working on HAR systems to translate measurements from wearable devices and smartphones into physical activity (2, 3).…”