Detecting and measuring construction workers' vigilance through hybrid kinematic-EEG signals
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Heng Li | Di Wang | Jiayu Chen | Heng Li | Jiayu Chen | Di Wang
[1] Ratna Sari Dewi,et al. Graphical fault tree analysis for fatal falls in the construction industry. , 2014, Accident; analysis and prevention.
[2] W. Does,et al. Do not look away! Spontaneous frontal EEG theta/beta ratio as a marker for cognitive control over attention to mild and high threat , 2018, Biological Psychology.
[3] Ioannis Brilakis,et al. Real-time simulation of construction workers using combined human body and hand tracking for robotic construction worker system , 2018 .
[4] Jingfeng Yuan,et al. Developing dimensions and key indicators for the safety climate within China’s construction teams: A questionnaire survey on construction sites in Nanjing , 2017 .
[5] X. Bornas,et al. Spontaneous EEG theta/beta ratio and delta–beta coupling in relation to attentional network functioning and self-reported attentional control , 2015, Cognitive, Affective, & Behavioral Neuroscience.
[6] Lanlan Chen,et al. Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning , 2015, Expert Syst. Appl..
[7] S. Luck. An Introduction to the Event-Related Potential Technique , 2005 .
[8] Yantao Yu,et al. The availability of wearable-device-based physical data for the measurement of construction workers' psychological status on site: From the perspective of safety management , 2017 .
[9] Rafiq M Choudhry,et al. Behavior-based safety on construction sites: a case study. , 2014, Accident; analysis and prevention.
[10] Hongtao Lu,et al. An integrated Gaussian mixture model to estimate vigilance level based on EEG recordings , 2014, Neurocomputing.
[11] Benjamin Brooks,et al. Measuring mental workload and physiological reactions in marine pilots: Building bridges towards redlines of performance. , 2018, Applied ergonomics.
[12] Evangelos Bekiaris,et al. Using EEG spectral components to assess algorithms for detecting fatigue , 2009, Expert Syst. Appl..
[13] J. Panksepp,et al. Human brain EEG indices of emotions: Delineating responses to affective vocalizations by measuring frontal theta event-related synchronization , 2011, Neuroscience & Biobehavioral Reviews.
[14] Dongping Fang,et al. A cognitive analysis of why Chinese scaffolders do not use safety harnesses in construction , 2013 .
[15] Yan Li,et al. Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques , 2012, Comput. Biol. Medicine.
[16] Qian Liu,et al. Prospective safety performance evaluation on construction sites. , 2015, Accident; analysis and prevention.
[17] Michael X Cohen,et al. Analyzing Neural Time Series Data: Theory and Practice , 2014 .
[18] Bonaventura H.W. Hadikusumo,et al. Structural equation model of integrated safety intervention practices affecting the safety behaviour of workers in the construction industry , 2017 .
[19] Y. H. Lee,et al. Fuzzy systems to process ECG and EEG signals for quantification of the mental workload , 2000, Inf. Sci..
[20] Houtan Jebelli,et al. EEG-based workers' stress recognition at construction sites , 2018, Automation in Construction.
[21] Dakota Evans,et al. A multi-measure approach for connecting cognitive workload and automation , 2017, Int. J. Hum. Comput. Stud..
[22] Wei Wang,et al. Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution , 2017 .
[23] Gregor Leicht,et al. EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement , 2009, NeuroImage.
[24] Xu Chen,et al. The vigilance-avoidance model of avoidant recognition: An ERP study under threat priming , 2016, Psychiatry Research.
[25] M. Akin,et al. Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals , 2002, Journal of Medical Systems.
[26] Abdulhamit Subasi,et al. Automatic identification of epileptic seizures from EEG signals using linear programming boosting , 2016, Comput. Methods Programs Biomed..
[27] Michelle N. Lumicao,et al. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. , 2007, Aviation, space, and environmental medicine.
[28] Xiaobing Wu,et al. Monitoring workers' attention and vigilance in construction activities through a wireless and wearable electroencephalography system , 2017 .
[29] Joonwoo Son,et al. Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals. , 2017, Applied ergonomics.
[30] Brenda McCabe,et al. Impact of individual resilience and safety climate on safety performance and psychological stress of construction workers: A case study of the Ontario construction industry. , 2017, Journal of safety research.
[31] Ren-Jye Dzeng,et al. Accelerometer-based fall-portent detection algorithm for construction tiling operation , 2017 .
[32] Hong Zhang,et al. Wearable IMU-based real-time motion warning system for construction workers' musculoskeletal disorders prevention , 2017 .
[33] Amir Rastegarnia,et al. Methods for artifact detection and removal from scalp EEG: A review , 2016, Neurophysiologie Clinique/Clinical Neurophysiology.
[34] Yantao Yu,et al. An experimental study of real-time identification of construction workers' unsafe behaviors , 2017 .
[35] H. B. Riley,et al. Drowsy Driving Detection by EEG Analysis Using Wavelet Transform and K-means Clustering , 2014, FNC/MobiSPC.
[36] Abdulhamit Subasi,et al. Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients , 2005, Expert Syst. Appl..
[37] Xinyi Song,et al. Revealing the "invisible Gorilla" in construction: Estimating construction safety through mental workload assessment , 2016 .
[38] Abdulhamit Subasi,et al. EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..
[39] Ram Bilas Pachori,et al. Automatic classification of sleep stages based on the time-frequency image of EEG signals , 2013, Comput. Methods Programs Biomed..
[40] Takayoshi Kamada,et al. Analysis of Longitudinal Driving Behaviors During Car Following Situation by Driver's EEG Using PARAFAC , 2013, IFAC HMS.
[41] Fatma Latifoglu,et al. Investigation of the noise effect on fractal dimension of EEG in schizophrenia patients using wavelet and SSA-based approaches , 2015, Biomed. Signal Process. Control..
[42] A. R. Lind,et al. Evaluation of amplitude and frequency components of the surface EMG as an index of muscle fatigue. , 1982, Ergonomics.
[43] Arnaud Delorme,et al. Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition , 2018, NeuroImage.
[44] Burcin Becerik-Gerber,et al. Monitoring fatigue in construction workers using physiological measurements , 2017 .
[45] Ning-Han Liu,et al. Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors , 2013, Sensors.
[46] Alice J. Kozakevicius,et al. Automated drowsiness detection through wavelet packet analysis of a single EEG channel , 2016, Expert Syst. Appl..
[47] Min Zhu,et al. A new wavelet transform to sparsely represent cortical current densities for EEG/MEG inverse problems , 2013, Comput. Methods Programs Biomed..
[48] M. Chung,et al. Electroencephalographic study of drowsiness in simulated driving with sleep deprivation , 2005 .
[49] P. Rossini,et al. Alpha, beta and gamma electrocorticographic rhythms in somatosensory, motor, premotor and prefrontal cortical areas differ in movement execution and observation in humans , 2016, Clinical Neurophysiology.
[50] Zhenghang Lin,et al. Measuring the cognitive loads of construction safety sign designs during selective and sustained attention , 2018, Safety Science.
[51] Houtan Jebelli,et al. Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker , 2016 .
[52] David Menotti,et al. Evaluating the use of ECG signal in low frequencies as a biometry , 2014, Expert Syst. Appl..
[53] A R Duff,et al. Contributing factors in construction accidents. , 2005, Applied ergonomics.
[54] Christoph Mulert,et al. EEG-vigilance differences between patients with borderline personality disorder, patients with obsessive-compulsive disorder and healthy controls , 2008, European Archives of Psychiatry and Clinical Neuroscience.
[55] Bradley R. Postle. Essentials of Cognitive Neuroscience , 2020 .
[56] A. Roy Duff,et al. Development of Causal Model of Construction Accident Causation , 2001 .
[57] Yang Zhao,et al. Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods , 2015 .
[58] Heng Li,et al. Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers , 2017 .