A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

Abstract

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

Machine Learning for Healthcare Wearable Devices: The Big Picture

“…While the reported accuracy in most of the research done for fall detection is above 90% [ 28 – 30 ], the practicality of these techniques is still questionable as the experiments were done in a controlled environment with a limited number of participants and have the limitation of a high false alarm rate [ 77 ]. Another study to simulate fall data [ 31 ] was done to generate forward and syncope accelerometer data to form a larger dataset for fall detection training.…”

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

“…The RNN based fall detecting method integrated knowledge from the wearable or smartphone and camera deployed on the ceiling and wall. Villaverde et al [13] designed an open-source fall simulator which could recreate accelerometer fall samples of 2 standard kinds of fall: forward and syncope. This simulated sample is like real fall recorded by the real accelerometer for using them as input for ML application.…”

Ensemble-Based Machine Learning for Predicting Sudden Human Fall Using Health Data

“…Villaverde et al [13] proposed a game theory-based efficient data collection for syncope and forward fall. ey emphasised that the usual way of data collection for falls using a dummy is not an efficient option since it will lack natural human behaviour and cost more because of its excessive man-hour requirements.…”

“…ese approaches are good, but do not consider the physiological parameters of the end users, and are therefore unable to predict a fall due to abnormal health conditions. Some works tried to use machine learning approaches to detect human falls [10,12,13], but their work area is limited to several parameters and is based only on a few chosen algorithms. e works based on fall prediction [14,15] also have genuine concerns.…”