TY - GEN
T1 - AI-Based Fall Detection Using Contactless Sensing
AU - Taha, Ahmad
AU - Taha, Mohammad M.A.
AU - Barakat, Basel
AU - Taylor, William
AU - Abbasi, Qammer H.
AU - Ali Imran, Muhammad
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Falls are a major health concern for the elderly as it threatens their livelihood and independence. Nearly 50% of the older adults, aged over 65 years old, fall in a span of 5 years, with 62% sustaining injuries and 28% extensive protracting injuries. This paper presents a high accuracy contactless falls detection framework based on channel state information extracted from software-defined radios. The aim is to develop a system capable of detecting whether an individual subject is present within the sensing area, or if the subject is falling, and, finally, if the subject is performing one of three other activities, including sitting, standing, and walking. The results showed a promising detection accuracy of 95.6% and 98%, using the 10-fold cross-validation and train-test split methods, based on the Random Forest classifier, respectively. Furthermore, we present a real-time analysis of the system to highlight its capability to detect, analyze, and report falls in real-time.
AB - Falls are a major health concern for the elderly as it threatens their livelihood and independence. Nearly 50% of the older adults, aged over 65 years old, fall in a span of 5 years, with 62% sustaining injuries and 28% extensive protracting injuries. This paper presents a high accuracy contactless falls detection framework based on channel state information extracted from software-defined radios. The aim is to develop a system capable of detecting whether an individual subject is present within the sensing area, or if the subject is falling, and, finally, if the subject is performing one of three other activities, including sitting, standing, and walking. The results showed a promising detection accuracy of 95.6% and 98%, using the 10-fold cross-validation and train-test split methods, based on the Random Forest classifier, respectively. Furthermore, we present a real-time analysis of the system to highlight its capability to detect, analyze, and report falls in real-time.
KW - Channel State information
KW - Falls detection
KW - Machine learning
KW - Random Forest
UR - https://www.scopus.com/pages/publications/85123606440
U2 - 10.1109/SENSORS47087.2021.9639715
DO - 10.1109/SENSORS47087.2021.9639715
M3 - Conference contribution
AN - SCOPUS:85123606440
T3 - Proceedings of IEEE Sensors
BT - 2021 IEEE Sensors, SENSORS 2021 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE Sensors, SENSORS 2021
Y2 - 31 October 2021 through 4 November 2021
ER -