In view of the serious accidents caused by the fatigue operation of tower crane drivers in construction, this study puts forward a novel type of portable semi-dry electrode to detect the mental fatigue state of tower crane drivers in real time and reduce the occurrence of unsafe operation behavior. The electrode not only possesses the advantages of low contact impedance and convenient use as traditional dry and wet electrodes but also has a more stable performance compared with the semi-dry electrodes designed by previous scholars. It is suitable for experimental occasions such as fatigue detection of tower crane drivers and collecting electroencephalogram (EEG) signals for a long time. In addition, combined with the advantages of the Gramian angular difference field-convolutional neural network (GADF-CNN) algorithm that can extract multiple features of EEG signals for comprehensive fatigue determination, this study chose the GADF-CNN algorithm to analyze the driving fatigue features of tower crane drivers. The results show that the combination of the novel semi-dry electrode and GADF-CNN algorithm can detect the driving fatigue features of tower crane drivers in real time and conveniently, thus improving the safety of building construction.

Study on a Portable Electrode Used to Detect the Fatigue of Tower Crane Drivers in Real Construction Environment

Advancing EEG-based brain-computer interface technology via PEDOT:PSS electrodes

In view of the serious harm caused by driving fatigue, this paper investigated driving fatigue detection based on electroencephalogram (EEG) features, designed a novel semi-dry electrode for acquisitioning drivers' EEG signals, and performed refined composite multiscale fluctuation dispersion entropy (RCMFDE) feature extraction of the θ and β bands rhythm signals in the acquisitioned EEG signals. The research results showed that the novel semi-dry electrode designed in this paper had the advantages of convenient replenishment of conductive liquid, early warning of insufficient conductive liquid, comfortable to wear compared with the traditional wet and dry electrodes under the premise of being able to collect EEG signals with qualified quality. And compared with traditional methods such as multiscale sample entropy (MSE), multiscale permutation entropy (MPE), multiscale symbolic dynamic entropy (MSDE), multiscale dispersion entropy (MDE), and refined composite multiscale dispersion entropy (RCMDE), the EEG features extracted by the RCMFDE method in this paper have a more obvious fatigue feature tendency, and thus can identify the driving fatigue state more effectively. 

EEG-Based Detection of Driving Fatigue Using A Novel Electrode

Wearable dry electrodes are the foundation for the development of long-term, real-time biopotential monitoring devices. However, the physiological signals collected by common electrodes often accompany high noise and motion artifacts due to difficulties in conforming to skin deformations and poor contact between electrodes and skin. In contrast to them, PEDOT:PSS-based dry electrodes become a promising material with adjustable stretchability, conductivity, and adhesion properties to achieve high-quality signal delivery. Herein, we report a highly stretchable self-adhesive film prepared by the conductive polymers PEDOT:PSS, ethylene glycol (EG), poly(vinyl alcohol) (PVA), and d-sorbitol (SOR). In the blending system, SOR will disrupt the dense hydrogen bond interactions within PVA and PSS chains, while PEDOT:PSS/PVA physically cross-linked networks can dissipate strain energy, providing toughness to the films. In addition, the hydroxyl groups on the surface enable the film to be self-adhesive by forming hydrogen bonds with the N and O atoms of the skin stratum corneum. The blended electrode exhibits a conductivity of 200 S/cm and an elongation at break of 120%. Moreover, the electrical properties are still maintained after cyclic stretching (R/R0 ≈ 1.09). The maximum adhesion force of the film on glass and skin is 1.20 and 0.36 N/cm, respectively. The electrode has good biocompatibility, and its excellent stretchability and adhesiveness are conducive to comply with the skin deformation under different conditions. The contact impedance between dry electrodes and skin is only 77 kΩ cm2, which ensures the accurate monitoring of physiological signals including electromyogram (EMG) and electrocardiogram (ECG) during rest or exercise. The signal quality is significantly higher than that of standard Ag/AgCl electrodes. This highly stretchable self-adhesive dry electrode provides a feasible idea for the research of next-generation health monitoring devices. 

Highly Stretchable Self-Adhesive PEDOT:PSS Dry Electrodes for Biopotential Monitoring

Wearable sensors have emerged as transformative tools, enabling real-time monitoring of human health and activities. Within this field, miniaturized and flexible devices have attracted significant attention due to their compact size, ease of use, and non-invasive operation. These sensors function by detecting biological activities and converting bio-signals such as electrophysiological, mechanical, and biochemical information into quantifiable data. Such data can be obtained through various sensing approaches, including the detection of electrolytes, ions, and gases. In many cases, wearable sensors are fabricated by integrating the sensing element into a polymer matrix, with nanomaterials playing a particularly important role in enhancing performance. Health monitoring remains the primary application area for these devices. Emerging technologies, including AI-assisted sensing and cloud-based data processing, are expected to drive future advancements, while also introducing challenges related to data privacy. Looking ahead, key areas of development for nanomaterial-based wearable sensors include non-contact monitoring, textile-integrated devices, and improvements in security and regulatory frameworks. 

pdf/wearable-biosensors-incorporating-nanocomposites-dt87mbbnbwbt.pdf

Wearable biosensors incorporating nanocomposites: advancements, applications, and future directions

Research on health management and risk warning based on sensor data

Urban vitality is defined as the zing of cities providing citizens with the ability to live, and it is important to offer an essential basis for estimating urban growth and spatial balance. Shenzhens rapid development has made remarkable achievements in a brief period of forty years as a special economic zone since reform and openness. This study takes Shenzhen as an example, uses 21 POI data obtained from Baidu Maps, and discusses the relationship between urban vitality and time and space and the influence factors of urban vitality. Results show that urban vitality has a close relationship with time and space and high-vitality areas are judged based on the production activities of urban people. Besides, amusement activities do not have a clear influence on urban vitality. Furthermore, convenient transportation and ordered life-supporting services play a stressful role in pushing urban vitality. Based on the above three elements, it is necessary to pay attention to taking production activities, creating convenient transportation, and providing ordered life-supporting services to increase a citys urban vitality.

The comment trial and comparison of urban vitality based on POI data: A case study of Shenzhen

Continuous and accurate acquisition of electrophysiological signals is critical in health monitoring and disease diagnosis. However, degradation of electrophysiological electrodes can occur during long-term and continuous monitoring, leading to poor...

A Reversible Gel-Free Electrode for Continuous Noninvasive Electrophysiological Signals Monitoring

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Research on health management and risk warning based on sensor data

The comment trial and comparison of urban vitality based on POI data: A case study of Shenzhen

A Reversible Gel-Free Electrode for Continuous Noninvasive Electrophysiological Signals Monitoring