TY - GEN
T1 - Will Poppy Fall? Predicting Robot Falls in Advance Based on Visual Input
AU - He, Borui
AU - Katz, Garrett E.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Falling is a critical problem for both people and robots, which may cause bodily harm to the elderly or prevent robots from executing issued orders. This motivates applications of machine learning to recognize and detect falls. Many datasets have been collected for this purpose, but primarily for detecting human falls after they occur. In this paper, we contribute simulated and real training data for robotic fall prediction in advance, based on egocentric video. We also compare an existing fall recognition model with a custom deep architecture we designed, to establish baseline performance on our datasets. We find that our architecture performs well for various prediction spans that can shift between training and testing.
AB - Falling is a critical problem for both people and robots, which may cause bodily harm to the elderly or prevent robots from executing issued orders. This motivates applications of machine learning to recognize and detect falls. Many datasets have been collected for this purpose, but primarily for detecting human falls after they occur. In this paper, we contribute simulated and real training data for robotic fall prediction in advance, based on egocentric video. We also compare an existing fall recognition model with a custom deep architecture we designed, to establish baseline performance on our datasets. We find that our architecture performs well for various prediction spans that can shift between training and testing.
KW - fall
KW - prediction
KW - robots
UR - http://www.scopus.com/inward/record.url?scp=85190089587&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190089587&partnerID=8YFLogxK
U2 - 10.1109/ICMLA58977.2023.00039
DO - 10.1109/ICMLA58977.2023.00039
M3 - Conference contribution
AN - SCOPUS:85190089587
T3 - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
SP - 226
EP - 232
BT - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
A2 - Arif Wani, M.
A2 - Boicu, Mihai
A2 - Sayed-Mouchaweh, Moamar
A2 - Abreu, Pedro Henriques
A2 - Gama, Joao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Y2 - 15 December 2023 through 17 December 2023
ER -