TY - GEN
T1 - Evaluating the Efficiency of Several Machine Learning Algorithms for Fall Detection
AU - Banda, Parimala
AU - Mohammadian, Masoud
AU - Gudur, Raghu
N1 - Funding Information:
Acknowledgment. The author would like to acknowledge the support provided by the Faculty of Science and Technology at the University of Canberra.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/3/20
Y1 - 2022/3/20
N2 - Elderly falls are a growing phenomenon observed within the world. According to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed Machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the cur-rent state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.
AB - Elderly falls are a growing phenomenon observed within the world. According to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed Machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the cur-rent state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.
KW - Fall Detection, Elderly Fall Detection, Machine Learning Algorithms, Wearable Fall Detection System, SisFall Dataset, MobiFall Dataset, K-Nearest Neighbors, KNN, Support Vector Machines, SVM, Decision Tree, Random Forest, Logistic Regression, Naïve Bayes
KW - Elderly fall detection
KW - Machine learning algorithms
KW - Naïve Bayes
KW - SisFall dataset
KW - KNN
KW - Random forest
KW - SVM
KW - Decision tree
KW - Fall detection
KW - Support vector machines
KW - Logistic regression
KW - MobiFall dataset
KW - K-nearest neighbors
KW - Wearable fall detection system
UR - https://www.ihci.cs.kent.edu/
UR - https://www.ihci.cs.kent.edu/wp-content/uploads/2021/12/IHCI-2021_Proceeding_V1_2.pdf
UR - http://www.scopus.com/inward/record.url?scp=85127130939&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-98404-5_56
DO - 10.1007/978-3-030-98404-5_56
M3 - Conference contribution
SN - 9783030984038
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 610
EP - 620
BT - Intelligent Human Computer Interaction - 13th International Conference, IHCI 2021, Revised Selected Papers
A2 - Kim, Jong-Hoon
A2 - Khan, Javed
A2 - Singh, Madhusudan
A2 - Tiwary, Uma Shanker
A2 - Sur, Marigankar
A2 - Singh, Dhananjay
PB - Springer
CY - Netherlands
T2 - IHCI2021: 13th International Conference on Intelligent Human Computer Interaction
Y2 - 20 December 2021 through 22 December 2021
ER -