Paper Info
Title
Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public Dataset.
Abstract
In this study, pre-impact fall detection algorithms were developed based on data gathered by a custom-made inertial measurement unit (IMU). Four types of simulated falls were performed by 40 healthy subjects (age: 23.4 +/- 4.4 years). The IMU recorded acceleration and angular velocity during all activities. Acceleration, angular velocity, and trunk inclination thresholds were set to 0.9 g, 47.3 degrees/s, and 24.7 degrees, respectively, for a pre-impact fall detection algorithm using vertical angles (VA algorithm); and 0.9 g, 47.3 degrees/s, and 0.19, respectively, for an algorithm using the triangle feature (TF algorithm). The algorithms were validated by the results of a blind test using four types of simulated falls and six types of activities of daily living (ADL). VA and TF algorithms resulted in lead times of 401 +/- 46.9 ms and 427 +/- 45.9 ms, respectively. Both algorithms were able to detect falls with 100% accuracy. The performance of the algorithms was evaluated using a public dataset. Both algorithms detected every fall in the SisFall dataset with 100% sensitivity). The VA algorithm had a specificity of 78.3%, and TF algorithm had a specificity of 83.9%. The algorithms had higher specificity when interpreting data from elderly subjects. This study showed that algorithms using angles could more accurately detect falls. Public datasets are needed to improve the accuracy of the algorithms.
Year
DOI
Venue
2019
10.3390/s19040774
SENSORS
Keywords
Field
DocType
ADLs,fall detection algorithm,falls,IMU,lead time,public dataset
Inertial frame of reference,Angular velocity,Algorithm,Acceleration,Inertial measurement unit,Engineering
Journal
Volume
Issue
ISSN
19
4.0
1424-8220
Citations 
PageRank 
References 
2
0.42
4
Authors
4
Name
Order
Citations
PageRank
Soonjae Ahn120.76
Jongman Kim221.77
Bummo Koo321.43
Youngho Kim45311.10