+ 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

Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach

Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach

Conference Proceedings 2016: 3712-3715

Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.

Please choose payment method:

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

Accession: 059731314

Download citation: RISBibTeXText

PMID: 28269098

DOI: 10.1109/embc.2016.7591534

Related references

Temporal and kinematic variables for real-world falls harvested from lumbar sensors in the elderly population. Conference Proceedings 2015: 5183-5186, 2016

Transfer learning approach for fall detection with the FARSEEING real-world dataset and simulated falls. Conference Proceedings 2018: 3509-3512, 2018

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, 2016

Evaluation of accelerometer-based fall detection algorithms on real-world falls. Plos One 7(5): E37062, 2013

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

Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection. Sensors 19(1), 2018

The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls. European Review of Aging and Physical Activity 13: 8, 2016

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection. Sensors 18(1), 2018

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, 2013

Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6): 10691-10708, 2015

Machine Learning Algorithms for Liquid Crystal-Based Sensors. Acs Sensors 3(11): 2237-2245, 2018

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

Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms. Sensors 18(9), 2018

Reading from the Black Box: What Sensors Tell Us about Resting and Recovery after Real-World Falls. Gerontology 64(1): 90-95, 2017

Real-time detection of potable-reclaimed water pipe cross-connection events by conventional water quality sensors using machine learning methods. Journal of Environmental Management 238: 201-209, 2019