The technology of Triboelectric Nanogenerators (TENG) has promising potentials in pulse detection. By harnessing the mechanical motion of human pulse, TENG generates electricity (Chen et al., 2020). Its unique wearability and portability, coupled with its independence from external power sources, render it an optimal selection for real-time pulse detection and monitoring (Zhang et al., 2014). Han et al. introduced a flexible self-powered ultra-sensitive pulse sensor (SUPS) that employs frictional-electric active sensor technology (Ouyang et al., 2017), demonstrating superior output performance (1.52 V), high peak signal-to-noise ratio (45 dB), long-term endurance (107 cycles), and affordability. The TENG placed on the wrist detects changes in wrist pulse pressure that correspond to human health, which it senses through the electrical signals produced (Lai et al., 2017). Ying-Chih Lai et al. developed an SE-TENG that has a simple structure and enhanced flexibility, making it highly convenient for harvesting energy from human skin. The device comprises a basic groove structure with PDMS film and ITO-coated PET film merged together. The efficiency of the apparatus relies on the magnitude and profundity of the groove structure. Increasing the depth of the grooves would cause the output signal of the pulse sensor to decrease linearly (Meng et al., 2019). Conversely, reducing the groove’s depth would increase the contact area between the PDMS film and the ITO electrode, resulting in substantial charge variation (Li et al., 2023b). When the groove structure feels pulse pressure, the bottom PDMS layer interacts more with the ITO electrode, transmitting charge from the ground electrode. Nonetheless, in the event of an electrostatic equilibrium between the frictional-electric films, charge transfer remains dormant in the absence of pulse pressure. The wrist pulse acts constantly on the PDMS film, creating a back-and-forth current flow. The schematic diagram and output pulse wave of the single-electrode pulse sensor are depicted in B1-B2 of Figure 1 (Suguna and Veerabhadrappa, 2019). Meng et al. developed a self-powered, flexible, woven pressure sensor (WCSPS) to non-invasively diagnose hypertension-related diseases through pulse measurement (Wu et al., 2019), as depicted in C of Figure 1. The device comprises interlaced polymer nanowires (Seo et al., 2016) in a frictional electric material. The WCSPS offers an exceptional sensitivity, with a response time of <5 ms, and a weaving structure that is 1.64 times greater in effective contact area and electrical output compared to an unwoven structure (Ardila et al., 2019). The WCSPS demonstrated reliable performance, maintaining sensor capabilities after 40,000 repeated motion cycles (Su et al., 2016). The WCSPS was applied to measure blood pressure from 100 participants representing diverse ages and health statuses ranging from 24 to 82 years old. Differences between the results from the WCSPS and those from cuff-based devices ranged from 0.87% to 3.65%. Liu et al. proposed a straightforward and cost-effective approach for monitoring subtle biological signals, such as respiration and pulse, to achieve a highly sensitive, self-powered, and flexible pressure sensor based on TENG. This was achieved by synergizing the characteristics of PDMS and thermally expandable microspheres (Li et al., 2019). By spin-coating a mixture of thermally expandable microspheres and PDMS on a planar substrate, a large-area patterned friction-charged thin layer was created (Li et al., 2019). Upon heating, the microspheres expanded, forming microstructures on the original flat PDMS surface. As the weight percentage of added thermally expandable microspheres increased, the sensor’s sensitivity also increased, achieving a maximum sensitivity of 1 mV/Pa at a weight percentage of 150%. A theoretical model for analyzing the output voltage was proposed, which exhibited good agreement with experimental results, highlighting substantial potential in pulse monitoring.

This study revealed that utilizing TENG for pulse measurement is an effective approach to health monitoring (Li et al., 2019), which could provide a competing alternative to currently existing complex monitoring systems (As depicted in D of Figure 1). Looking ahead to medical applications, enhancing the electrostatic charge effect of the device and augmenting the dielectric constant of functional materials can be achieved through the chemical incorporation of nanoparticles within an electrostatic composite layer, encompassing nanoparticles, nanotubes, and nanowires (Yao et al., 2022). Material-wise, Combining nylon nanofibers with composite materials such as polyvinylidene fluoride-silver nanowires and polytetrafluoroethylene (PTFE) is a promising method to enhance the electrostatic charge of TENG devices. Additionally, material selection can be optimized based on specific application requirements (Wang, 2017). Nevertheless, notwithstanding the promising attributes of the TENG-based pulse detection device, it confronts specific limitations during the data processing phase. These limitations consist of a large quantity of data, interference from noise, and signal complexity. As a result of these limitations, the extraction of valuable pulse information using traditional data processing methods is arduous (Zhou et al., 2014). It is imperative to surmount these constraints by implementing robust machine learning algorithms and models, that will in turn, enhance the efficiency, and precision of data processing (Yuan et al., 2021). By intelligently analyzing, filtering, and identifying the pulse signal, the performance of the pulse detection device can be considerably improved.

The integration of biosensors with machine learning (ML) in medical applications enhances the ability of healthcare systems and decision makers to process information, insights, and environmental data, enabling personalized medicine that minimizes misdiagnosis or late diagnosis (Zhang et al., 2017). For example, Google and Northwestern University have developed an AI model for lung cancer detection in which more than 42,000 CT scan images were used for training (Zhou et al., 2022). The model is able to detect cancer in a single CT scan—with 5% higher accuracy than human experts and exhibits a 9.5% higher detection rate than radiologists in predicting cancer risk 2 years in advance (Riera et al., 2012). Specifically, machine learning has heightened the precision of pulse detection devices, empowering their algorithms to discern subtle pulse signal nuances through the assimilation and analysis of extensive pulse data. This, in turn, culminates in refined detection outcomes. Secondly, machine learning further enhances pulse detection devices’ real-time capability by enabling its algorithm to process and instantly analyze data from TENG sensors to swiftly detect and recognize pulse signals, permitting real-time monitoring and feedback. Lastly, machine learning offers adaptability by allowing pulse detection devices to adaptively analyze and process pulse signals through individual physiological characteristics and environmental modifications, resulting in more personalized and precise results.

Yao et al. proposed the TENG-Cat-System, a human self-propelled catalytic promotion system that improves cancer treatment by studying the electrostatic preorganization effect in natural enzymatic catalysis processes (Ardila et al., 2019; Liu et al., 2023). Enhancing the generation of reactive oxygen species (ROS) using nanocatalysts is essential to improve cancer therapeutic efficacy. The TENG-Cat-system utilizes a one-dimensional porphyrin covalent organic framework (COF) on carbon nanotubes (CNTs) to generate ROS, and markedly enhances its peroxidase-like activity under the human inherent electric field. Employing machine learning for data analysis in conjunction with physiological parameters, the system assists physicians in predicting patient disease progression, therapeutic response, and prognosis. Yuan et al. produced an additive-manufactured, low-cost, and disposable 3D-printed acoustic triboelectric nanogenerator (A-TENG) that collects low-frequency acoustic energy in academic and medical fields (Li et al., 2019; Zhang et al., 2022). Under a 100 dB sound pressure level, the system generates a power output of 4.33 mW. The acoustic resonator system has the capacity to continuously power up to 72 LEDs and a commercial calculator, highlighting its potential as a device power source. Augmented with an artificial intelligence chip that leverages pre-trained neural networks to identify and process the converted electrical signals of A-TENG, the system showcases substantial potential for an intelligent Internet of Things (Zeng et al., 2020).

Currently, the monitoring of high-risk or diseased patients’ health primarily depends on clinical observations and laboratory diagnostic tools that can be expensive and inconvenient (Lin et al., 2020). To overcome these limitations, individual sensing data such as pulse rate, respiratory rate, or blood pressure signals are integrated into an “early risk rating” that aids in health management. Machine learning plays a significant role in this integration process (Zhang et al., 2014). For instance, Riera and colleagues proposed a method that combines EEG and EMG signals for stress detection, which considerably increases the classification accuracy from 79% to 92% through data fusion. This method utilizes periodic reuse and quantitative sensing, providing a more comprehensive understanding of the relationship between medical conditions and physical and mental health, leading to more valuable diagnosis and predictions (Su et al., 2016; Ouyang et al., 2017). Furthermore, Zeng and his team used diverse machine learning techniques to develop their epidermal electronic system (EES) to monitor and predict levels of mental fatigue, achieving an impressive 89% prediction accuracy, which highlights the vast potential of machine learning in the medical field (Lai et al., 2017).

These instances underscore the prowess of machine learning in efficiently processing data and discerning patterns. Possessing the ability to use diverse training models and optimization algorithms, the pulse detection device employing TENG holds great promise as an essential tool in the health monitoring industry (Meng et al., 2019). By analyzing the basic properties of pulse waves and considering clinical requirements and physiological significance, we recommend utilizing the wavelet transform modulus maxima detection algorithm alongside morphology operations to improve the performance of the pulse detection device (Li et al., 2023b). Moreover, optimizing the algorithm through wavelet threshold denoising can enhance the detection and location of extreme points of pulse signals, even in situations with severe noise interference (Wu et al., 2019), baseline drift, or other forms of interference (Seo et al., 2016). Furthermore, in the context of pulse signal analysis, Recurrent Neural Networks (RNNs) process each step of pulse data, learning temporal patterns to predict future features or detect anomalies. RNNs build long dependencies in sequential data, but traditional RNNs suffer from vanishing gradients when handling long sequences. To overcome this, variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have been introduced, using gating mechanisms to effectively capture and retain information in lengthy sequences (Leng et al., 2023). This enhances RNNs’ performance in analyzing pulse signals and other sequential data.