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Feasibility of Tree-Based Machine Learning Models to Discri..:

, In: 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE),

Prisco, Giuseppe ; Romano, Maria ; Esposito, Fabrizio... - p. 870-875 , 2023

Link: https://doi.org/10.1109/MetroXRAINE58569.2023.10405830

RT 
T1 <cTitle>2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)</cTitle>: T1 <Title>Feasibility of Tree-Based Machine Learning Models to Discriminate Safe and Unsafe Posture During Weight Lifting</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10405830&Exemplar=1&LAN=DE
				
A1 Prisco, Giuseppe
A1 Romano, Maria
A1 Esposito, Fabrizio
A1 Cesarelli, Mario
A1 Santone, Antonella
A1 Donisi, Leandro
YR 2023
K1 Weight measurement
K1 Musculoskeletal system
K1 Measurement units
K1 Sternum
K1 Feature extraction
K1 Time-domain analysis
K1 Random forests
K1 weight lifting
K1 feature extraction
K1 health monitoring
K1 lifting
K1 safe/unsafe posture
K1 inertial measurement unit
K1 wearable sensors
K1 occupational ergonomics
K1 machine learning
K1 work-related musculoskeletal disorders
SP 870
OP 875
LK http://dx.doi.org/https://doi.org/10.1109/MetroXRAINE58569.2023.10405830
DO https://doi.org/10.1109/MetroXRAINE58569.2023.10405830
SF ELIB - SuUB Bremen

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