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Deep learning-based classification of work-related physical load levels in construction.

, , и . Adv. Eng. Informatics, (2020)

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Capturing Environmental Distress of Pedestrians Using Multimodal Data: The Interplay of Biosignals and Image-Based Data., , , , , , , и . J. Comput. Civ. Eng., (2022)Lessons learned from utilizing discrete-event simulation modeling for quantifying construction emissions in pre-planning phase., , , и . WSC, стр. 3170-3176. IEEE, (2010)Predicting workers' inattentiveness to struck-by hazards by monitoring biosignals during a construction task: A virtual reality experiment., , и . Adv. Eng. Informatics, (2021)The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study., , , , и . Sensors, 19 (5): 983 (2019)Comprehensive Fall-Risk Assessment of Construction Workers Using Inertial Measurement Units: Validation of the Gait-Stability Metric to Assess the Fall Risk of Iron Workers., , и . J. Comput. Civ. Eng., (2016)Environmental Distress and Physiological Signals: Examination of the Saliency Detection Method., , , и . J. Comput. Civ. Eng., (2020)Deep learning-based classification of work-related physical load levels in construction., , и . Adv. Eng. Informatics, (2020)Toward Sensor-Based Early Diagnosis of Cognitive Impairment using Poisson Process Models., , и . EMBC, стр. 2839-2843. IEEE, (2022)MPSBL: Multiple Transmit Power Assisted Sequence-Based Localization in Wireless Sensor Networks., , , и . ICC, стр. 1-6. IEEE, (2018)Deciphering Workers' Safety Attitudes by Sensing Gait Patterns., , , , и . HCI (17), том 10287 из Lecture Notes in Computer Science, стр. 397-405. Springer, (2017)