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Hierarchical Feature Recovery for Robust Human Activity Rec..:

, In: Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers,

Oishi, Nobuyuki ; Lago, Paula ; Birch, Phil. - p. 364-370 , 2022

Link: https://dl.acm.org/doi/10.1145/3544793.3560392

RT 
T1 <cTitle>Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers</cTitle>: T1 <Title>Hierarchical Feature Recovery for Robust Human Activity Recognition in Body Sensor Networks</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3560392&Exemplar=1&LAN=DE
				
A1 Oishi, Nobuyuki
A1 Lago, Paula
A1 Birch, Phil
A1 Roggen, Daniel
PB ACM
YR 2022
K1 Body Sensor Networks
K1 Fault-tolerance
K1 Robustness
K1 Sensor-based Human Activity Recognition
K1 Human-centered computing
K1 Ubiquitous and mobile computing
K1 Ubiquitous and mobile computing design and evaluation methods
K1 Computer systems organization
K1 Embedded and cyber-physical systems
K1 Sensor networks
K1 Dependable and fault-tolerant systems and networks
K1 Fault-tolerant network topologies
SP 364
OP 370
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3544793.3560392
DO https://dl.acm.org/doi/10.1145/3544793.3560392
SF ELIB - SuUB Bremen

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