+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Machine learning-based pre-impact fall detection model to discriminate various types of fall

Machine learning-based pre-impact fall detection model to discriminate various types of fall

Journal of Biomechanical Engineering 2019:

Preimpact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust preimpact fall detection model was developed to classify various activities and falls as multi-class and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection algorithm, auto labeling of activities, application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multi-class showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multi-class preimpact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 066676737

Download citation: RISBibTeXText

PMID: 30968932

DOI: 10.1115/1.4043449

Related references

A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Medical and Biological Engineering and Computing 55(1): 45-55, 2017

Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm. Plos one 9(3): E92037, 2014

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection. Sensors 18(1):, 2017

Depth-based human fall detection via shape features and improved extreme learning machine. IEEE Journal of Biomedical and Health Informatics 18(6): 1915-1922, 2014

Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets. Plos one 12(7): E0180318, 2017

Fall risk probability estimation based on supervised feature learning using public fall datasets. Conference Proceedings 2016: 752-755, 2016

Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems. Biomedical Engineering Online 11: 9, 2012

Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches. Journal of Biomedical Informatics 90: 103103, 2019

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model. Sensors 17(2):, 2017

Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor. Sensors 18(7):, 2018

Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach. Conference Proceedings 2016: 3712-3715, 2016

Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors. Zeitschrift für Gerontologie und Geriatrie 45(8): 707-715, 2012

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications. Jmir Mhealth and Uhealth 5(10): E151, 2017

On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection. Sensors 18(2):, 2018

SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning. Sensors 18(10):, 2018