Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review
Top Cited Papers
Open Access
- 29 July 2021
- Vol. 21 (15), 5134
- https://doi.org/10.3390/s21155134
Abstract
Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.Keywords
Funding Information
- National Research Foundation of Korea (NRF-2019R1F1A1058951)
This publication has 92 references indexed in Scilit:
- Detecting Falls with Wearable Sensors Using Machine Learning TechniquesSensors, 2014
- Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical ApplicationsSensors, 2014
- Familial or Sporadic Idiopathic Scoliosis – classification based on artificial neural network and GAPDH and ACTB transcription profileBioMedical Engineering OnLine, 2013
- A survey on fall detection: Principles and approachesNeurocomputing, 2013
- Challenges, issues and trends in fall detection systemsBioMedical Engineering OnLine, 2013
- Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometryBioMedical Engineering OnLine, 2011
- Support Vector Machines for classification and regressionThe Analyst, 2009
- Development of a Common Outcome Data Set for Fall Injury Prevention Trials: The Prevention of Falls Network Europe ConsensusJournal of the American Geriatrics Society, 2005
- Knowledge-Based Nonlinear Kernel ClassifiersLecture Notes in Computer Science, 2003