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1
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
Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers
: T1
Hierarchical Feature Recovery for Robust Human Activity Recognition in Body Sensor Networks
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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