Table 1.
List of included studies with the respective number and citation information, starting from studies addressing mental fatigue (#1–#8), followed by studies addressing vigilance detection (#9–#11), drowsiness (#12–#44), physical fatigue (#45–#56) and, lastly, muscle fatigue (#57–#60).
| No. | Citation | Title of Paper |
|---|---|---|
| 1 | Zeng et al., 2020 | Nonintrusive monitoring of mental fatigue status using epidermal electronic systems and machine-learning algorithms |
| 2 | Li et al., 2020 | Identification and classification of construction equipment operators' mental fatigue using wearable eye-tracking technology |
| 3 | Lamti et al., 2019 | Mental fatigue level detection based on event related and visual evoked potentials features fusion in virtual indoor environment |
| 4 | Zhang Y. et al., 2018 | A deep temporal model for mental fatigue detection |
| 5 | Lee et al., 2019b | Emotion and fatigue monitoring using wearable devices |
| 6 | Huang et al., 2018 | Detection of mental fatigue state with wearable ECG devices |
| 7 | Choi et al., 2018 | Wearable device-based system to monitor a driver's stress, fatigue, and drowsiness |
| 8 | Al-Libawy et al., 2016 | HRV-based operator fatigue analysis and classification using wearable sensors |
| 9 | Samima et al., 2019 | Estimation and quantification of vigilance using ERPs and eye blink rate with a fuzzy model-based approach |
| 10 | Wang et al., 2019 | Detecting and measuring construction workers' vigilance through hybrid kinematic-EEG signals |
| 11 | Chen et al., 2018 | Developing construction workers' mental vigilance indicators through wavelet packet decomposition on EEG signals |
| 12 | Ko et al., 2020 | Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task |
| 13 | Sun et al., 2020 | Recognition of fatigue driving based on steering operation using wearable smart watch |
| 14 | Foong et al., 2019 | An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue |
| 15 | Wen et al., 2019 | Recognition of fatigue driving based on frequency features of wearable device data |
| 16 | Zhang M. et al., 2018 | An application of particle swarm algorithms to optimize hidden markov models for driver fatigue identification |
| 17 | Zhang et al., 2017 | Design of a fatigue detection system for high-speed trains based on driver vigilance using a wireless wearable EEG |
| 18 | Fu et al., 2016 | Dynamic driver fatigue detection using hidden markov model in real driving condition |
| 19 | Boon-Leng et al., 2015 | Mobile-based wearable-type of driver fatigue detection by GSR and EMG |
| 20 | Ko et al., 2015 | Single channel wireless EEG device for real-time fatigue level detection |
| 21 | Kundinger and Riener, 2020 | The potential of wrist-worn wearables for driver drowsiness detection: a feasibility analysis |
| 22 | Kundinger et al., 2020a | Assessment of the potential of wrist-worn wearable sensors for driver drowsiness detection |
| 23 | Kundinger et al., 2020b | Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups |
| 24 | Gielen and Aerts, 2019 | Feature extraction and evaluation for driver drowsiness detection based on thermoregulation |
| 25 | Mehreen et al., 2019 | A hybrid scheme for drowsiness detection using wearable sensors |
| 26 | Kim and Shin, 2019 | Utilizing HRV-derived respiration measures for driver drowsiness detection |
| 27 | Kartsch et al., 2019 | Ultra low-power drowsiness detection system with BioWolf |
| 28 | Lee et al., 2019a | Using wearable ECG/PPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots |
| 29 | Dhole et al., 2019 | A novel helmet design and implementation for drowsiness and fall detection of workers on-site using EEG and random-forest classifier |
| 30 | Ogino and Mitsukura, 2018 | Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram |
| 31 | Nakamura et al., 2018 | Automatic detection of drowsiness using in-ear EEG |
| 32 | Zhou et al., 2018 | Vigilance detection method for high-speed rail using wireless wearable EEG collection technology based on low-rank matrix decomposition |
| 33 | Lemkaddem et al., 2018 | Multi-modal driver drowsiness detection: a feasibility study |
| 34 | Li and Chung, 2018 | Combined EEG-gyroscope-tDCS brain machine interface system for early management of driver drowsiness |
| 35 | Li et al., 2015 | Smartwatch-based wearable EEG system for driver drowsiness detection |
| 36 | Li and Chung, 2015 | A context-aware EEG headset system for early detection of driver drowsiness |
| 37 | Lee et al., 2016 | Standalone wearable driver drowsiness detection system in a smartwatch |
| 38 | Leng et al., 2015 | Wearable driver drowsiness detection system based on biomedical and motion sensors |
| 39 | Zhang S. et al., 2018 | Low-power listen based driver drowsiness detection system using smartwatch |
| 40 | Cheon and Kang, 2017 | Sensor-based driver condition recognition using support vector machine for the detection of driver drowsiness |
| 41 | Rohit et al., 2017 | Real-time drowsiness detection using wearable, lightweight brain sensing headbands |
| 42 | Niwa et al., 2016 | A wearable device for traffic safety - a study on estimating drowsiness with eyewear, JINS MEME |
| 43 | Ha and Yoo, 2016 | A multimodal drowsiness monitoring ear-module system with closed-loop real-time alarm |
| 44 | Lee et al., 2015 | Smartwatch-based driver alertness monitoring with wearable motion and physiological sensor |
| 45 | Sedighi Maman et al., 2020 | A data analytic framework for physical fatigue management using wearable sensors |
| 46 | Nasirzadeh et al., 2020 | Physical fatigue detection using entropy analysis of heart rate signals |
| 47 | Torres et al., 2020 | Detection of fatigue on gait using accelerometer data and supervised machine learning |
| 48 | Khan et al., 2019 | A novel method for classification of running fatigue using change-point segmentation |
| 49 | Ameli et al., 2019 | Quantitative and non-invasive measurement of exercise-induced fatigue |
| 50 | Zhang et al., 2019b | Automated monitoring of physical fatigue using jerk |
| 51 | Tsao et al., 2019 | Using non-invasive wearable sensors to estimate perceived fatigue level in manual material handling task |
| 52 | Wang et al., 2018 | A heterogeneous ensemble learning voting method for fatigue detection in daily activities |
| 53 | Sedighi Maman et al., 2017 | A data-driven approach to modeling physical fatigue in the workplace using wearable sensors |
| 54 | Aryal et al., 2017 | Monitoring fatigue in construction workers using physiological measurements |
| 55 | Li et al., 2017 | A neuro-fuzzy fatigue-tracking and classification system for wheelchair users |
| 56 | Buckley et al., 2017 | Binary classification of running fatigue using a single inertial measurement unit |
| 57 | Karvekar et al., 2019 | A data-driven model to identify fatigue level based on the motion data from a smartphone |
| 58 | Papakostas et al., 2019 | Physical fatigue detection through EMG wearables and subjective user reports - a machine learning approach toward adaptive rehabilitation |
| 59 | Nourhan et al., 2017 | Detection of muscle fatigue using wearable (MYO) surface electromyography based control device |
| 60 | Mokaya et al., 2016 | Burnout: a wearable system for unobtrusive skeletal muscle fatigue estimation |