Article
The Performance of Post-Fall Detection Using the Cross-Dataset:
Feature Vectors, Classifiers and Processing Conditions
Bummo Koo , Jongman Kim , Yejin Nam and Youngho Kim *
Citation: Koo, B.; Kim, J.; Nam, Y.;
Kim, Y. The Performance of Post-Fall
Detection Using the Cross-Dataset:
Feature Vectors, Classifiers and
Processing Conditions. Sensors 2021,
21, 4638. https://doi.org/10.3390/
s21144638
Academic Editors: Pietro Picerno,
Andrea Mannini and Clive D’Souza
Received: 13 May 2021
Accepted: 3 July 2021
Published: 6 July 2021
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4.0/).
Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea; bmk726@ybrl.yonsei.ac.kr (B.K.);
jmkim0127@ybrl.yonsei.ac.kr (J.K.); namyj1007@ybrl.yonsei.ac.kr (Y.N.)
* Correspondence: younghokim@yonsei.ac.kr; Tel.: +82-033-760-2859
Abstract:
In this study, algorithms to detect post-falls were evaluated using the cross-dataset accord-
ing to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different
processing conditions (normalization, equalization, increase in the number of training data, and addi-
tional training with external data). Three-axis acceleration and angular velocity data were obtained
from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior
iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public
dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and
discrete data, respectively. The classification performance generally decreased, and thus, specific
feature vectors from the raw data were necessary when untrained motions were tested using a public
dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization
increased the sensitivity, even though it did not improve the overall performance. The increase in
the number of training data also improved the classification performance. Machine learning was
vulnerable to untrained motions, and data of various movements were needed for the training.
Keywords: fall detection; artificial neural network; support vector machine; cross-dataset
1. Introductions
Falls are one of the leading causes of death among the elderly [
1
]. Approximately
28–38% of people over 65 suffer a fall each year [
2
]. Falls can result in bruises and swellings,
as well as fractures and traumas [
3
]. In addition to the physical consequences, the fear
of falling can impact on the elderly’s quality of life. A fear of falling is associated with a
decline in physical and mental health, and an increased risk of falling [
4
]. Therefore, falls
and fall-related injuries are major healthcare challenges to overcome.
Many studies have tried to improve the physical performance of the elderly by per-
forming rehabilitation programs to help prevent falls. Røyset et al. [
5
] conducted a fall
prevention program using the Norwegian version of the fall risk assessment method,
“STRATIRY” (score 0–5), but achieved no significant improvement when compared to the
control group during a short stay in an orthopedic department. Gürler et al. [
6
] proposed
a recurrent fall prevention program including assessment of fall risk factors, education
on falls and home modification. This program was effective in reducing fall-related risk
factors and increasing fall knowledge. Palestra et al. [
7
] presented a rehabilitation system
based on a customizable exergame protocol (KINOPTIM) to prevent falls in the elderly. As
a result of training for 6 months, the performance of the postural response was improved
by an average of 80%.
Prevention of falls through long-term rehabilitation programs is important to improve
the quality of life for the elderly, but preparation for the situation of a fall is also important.
Falls may have serious consequences; however, most of the consequences of these falls
are not directly attributed to the falls themselves, but to the lack of timely assistance and
treatment [
8
]. Vellas et al. [
9
] reported that 70% of older adults who had fallen at home
were unable to get up unaided, and that more than 20% of patients admitted to hospital
Sensors 2021, 21, 4638. https://doi.org/10.3390/s21144638 https://www.mdpi.com/journal/sensors
