Sanhudo et al., 2021 - Google Patents

Activity classification using accelerometers and machine learning for complex construction worker activities

Sanhudo et al., 2021

Document ID
8197776643612566511
Author
Sanhudo L
Calvetti D
Martins J
Ramos N
Meda P
Goncalves M
Sousa H
Publication year
Publication venue
Journal of Building Engineering

External Links

Snippet

Automated Construction worker activity classification has the potential to not only benefit the worker performance in terms of productivity and safety, but also the overall project management and control. The activity-level knowledge and indicators that can be extracted …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models

Similar Documents

Publication Publication Date Title
Sanhudo et al. Activity classification using accelerometers and machine learning for complex construction worker activities
Akhavian et al. Smartphone-based construction workers' activity recognition and classification
Yang et al. Deep learning-based classification of work-related physical load levels in construction
Akhavian et al. Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers
Joshua et al. Accelerometer-based activity recognition in construction
Fang et al. Assessment of operator's situation awareness for smart operation of mobile cranes
Antwi-Afari et al. Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data
Li et al. Automated post-3D visualization ergonomic analysis system for rapid workplace design in modular construction
US20220172594A1 (en) Tracking and alert method and system for worker productivity and safety
Joshua et al. Automated recognition of construction labour activity using accelerometers in field situations
Alwasel et al. Identifying poses of safe and productive masons using machine learning
Yu et al. Posture-related data collection methods for construction workers: A review
US11839496B2 (en) Monitors for movements of workers
Mekruksavanich et al. Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors.
Negrello et al. Humanoids at work: The walk-man robot in a postearthquake scenario
Akhavian et al. Integrated mobile sensor-based activity recognition of construction equipment and human crews
Duan et al. Risk events recognition using smartphone and machine learning in construction workers' material handling tasks
Jahanbanifar et al. Evaluation of wearable sensors to quantify construction workers muscle force: An ergonomic analysis
Gondo et al. Accelerometer-based activity recognition of workers at construction sites
Koskimäki et al. Behavior modeling in industrial assembly lines using a wrist-worn inertial measurement unit
Sun et al. Dynamic human systems risk prognosis and control of lifting operations during prefabricated building construction
WO2012138407A1 (en) Feature location and resource management system
Hou et al. HINNet: Inertial navigation with head-mounted sensors using a neural network
Khazen et al. Monitoring workers on indoor construction sites using data fusion of real-time worker's location, body orientation, and productivity state
Subedi et al. Mapping datafication in construction-worker safety research to minimize injury-related disputes