Wireless Patrol Sign-In System with Mental Fatigue Detection

Abstract

Current sign-in methods of patrolling security guards mainly comprise signature, image identification, and fingerprint identification; notably, none of these methods indicate the physical and mental conditions of such guards. In particular, when patrolling security guards perform their duties consecutively for a long period of time, adequate attention should be directed toward their levels of mental fatigue. When a handwriting sign-in system is adopted, security guards may not record their sign-in time accurately, or they may fake signatures during long shifts. In addition, image identification systems cannot comprehensively reflect the physical and mental statuses of on-duty security guards, particularly their levels of fatigue. Monitor fatigue in patrolling security guards is important to avoid burnout and stress in the workplace. Therefore, in this study, a patrolling sign-in system that integrates physiological signals and images was designed. A thermometer, hand dynamometer, and electromyography sensor were combined to measure physiological signals. Results showed that hand grip strength and the median frequency of electromyography signals gradually reduced when muscle fatigue occurred. The system determined whether a security guard had signed in punctually and whether this person should stay on duty. Overall, this system was verified to operate effectively, and it is therefore applicable for monitoring the sign-in of patrolling security guards who work long shifts. This case series study proposed a conceptual prototype of the system; large-scale testing should be performed in subsequent research.

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

“…We used a wearable smart T-shirt to record the researchers’ ECG signals, and this device can easily record the signals without any problem. Previously, many researchers have used different wearable smart devices to record the physiological signals to detect the mental stress of the assembly-line operators, brain-injured patients, drivers, equipment operators, pilots, security guards, and traffic controllers [ 28 , 31 , 102 , 103 , 104 , 105 ]. They employed various techniques to find a more accurate detection system using wearable smart devices such as SYMTOP NT9200, NI USB-6008, ST-BTA, Storm 3G Ranger X, ADS1292R, Emotiv EPOC, and HD-BTA ( Table 5 ).…”

“…Pang et al [ 27 ] recorded the EEG signal for 25 h and concluded that the auditory vigilance task could easily recognize mental stress. Chang et al [ 28 ] used the wireless EMG, a thermometer, and a hand dynamometer to analyze the data and detect the mental stress of security guards. Li et al [ 29 ] used 21 features of the single-channel EEG signal to detect mental stress.…”