Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public Dataset

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 activ...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 19; no. 4; p. 774
Main Authors: Ahn, Soonjae, Kim, Jongman, Koo, Bummo, Kim, Youngho
Format: Journal Article
Language:English
Published: Switzerland MDPI 13.02.2019
MDPI AG
Subjects:
ADLs
fall detection algorithm
falls
IMU
lead time
public dataset
IMU
public dataset
falls
ADLs
fall detection algorithm
lead time
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary: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°/s, and 24.7°, respectively, for a pre-impact fall detection algorithm using vertical angles (VA algorithm); and 0.9 g, 47.3°/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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19040774