Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features

Abstract

Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active field of research. In this work, a novel pipeline for fall detection based on wearable accelerometer data has been proposed. Three publicly available datasets have been used to validate our proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets. After following a series of feature reduction techniques such as mutual information, removing highly correlated features using the Pearson correlation coefficient, Boruta algorithm, we have obtained the dominant features for each dataset. Different classical machine learning (ML) algorithms were utilized to detect falls based on the obtained features. For individual datasets, the simple ML classifiers achieved very good accuracy. We have also experimented with the proposed pipeline's efficacy by training and testing it with different datasets. A set of 39 high-performing features is selected, and the classifiers were trained with them. For all the cases, the proposed pipeline showed excellent efficiency in detecting falls. This architecture performed better than most of the existing works in all the used publicly available datasets, proving the supremacy of the proposed data analysis pipeline.

Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices

“…The proposed method aims to detect falls using a computer-vision approach. The major advantage of using a camera to monitor a person is overcoming the problem of background noise in the environment that is observed when using wearable sensors [ 8 , 9 , 10 , 11 , 12 , 13 , 15 , 34 , 35 , 36 ]. In addition, a computer-vision approach is very flexible because it does not depend on the particular scenario, it is not specific, it does not consume much time and it is simple to set up [ 4 ].…”

“…Wearable sensors : Wearable device-based approaches use triaxial gyroscopes [ 12 , 13 ], accelerometers [ 8 , 9 , 10 , 11 , 15 , 34 , 35 ] or both types of sensors [ 36 ] to monitor the person and detect posture changes and inactivity. In these solutions, data acquired by the sensors are used to compute different features, such as angles [ 9 , 12 ], differences and derivatives of the sum X , Y and directions [ 8 , 9 ], maximum acceleration and fluctuation frequency [ 12 ], decreasing of heat rates [ 10 ], variation of different parts of the body [ 11 ], the acceleration of the body parts [ 13 ], mutual information and removing highly correlated features using Pearson correlation coefficient and Boruta algorithm [ 35 ], etc.In addition, they distinguish fall and non-fall events by using thresholds [ 8 , 9 , 10 ], machine learning [ 11 , 12 , 13 , 14 , 35 ] and deep learning algorithms [ 15 , 34 ].…”

“…However, existing solutions show several limitations. For example, systems that use Wearable [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 34 , 35 , 36 ] and ambient [ 16 , 17 ] features have some disadvantages, which are related to the inconceivability of visually checking object information. In addition, systems that use computer vision techniques [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] are flexible.…”

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

“…On the other hand, AI and Machine Learning (ML) techniques are notable for their predictive abilities in a number of fields such as anomaly detection [30]- [33], biological data mining [34], [35], cyber security [36], disease detection [37]- [45], earthquake prediction [46], elderly care [47], [48], elderly fall detection [49]- [51], financial prediction [52], safeguarding workers in workplaces [53], text analytics [54], [55], and urban planning [56].…”

A Hybrid Deep Learning Model to Predict the Impact of COVID-19 on Mental Health form Social Media Big Data

“…In recent years machine learning (ML) and artificial intelligence (AI) based models have been applied in diverse applications including anomaly detection [7][8][9], biological data mining [10,11], cyber security [12], disease detection [13][14][15], earthquake prediction [16,17], financial prediction [18], fall detection [19][20][21], text analytics [22,23], optical character recognition [24][25][26], monitoring systems [27] and urban planning [28].…”