Triboelectric Intelligence
Renyun Zhang
Abstract
The innovation of triboelectric nanogenerators and their application in self-powered sensors [1-3] provides a new strategy for sensor development. Such a development is becoming an important part of IoT as a large number of sensors are needed to sense different things and communicate over networks. Among the sensors, triboelectric nanogenerator (TENG) based sensors are attracting rising attention during the last 10 years. A unique feature of the TENG sensors is the self-powering, which eliminates the need for batteries that are normally required of other types of sensors. In the early years of TENG sensors, researchers focused on the sensors' feasibility, flexibility, and sensitivity [4-7]. Lately, TENG sensing systems [8, 9] have been developed to obtain information from different places and times, which provides more data to be analyzed to describe a specific scenario. Moreover, the data could be communicated over a cloud. The utilization of artificial intelligence (AI) algorithms [10] for processing the data collected on the sensors has been found to upgrade the application of the TENG sensors to a new level because it allows us to analyze data without clear features. The application of AI provides more information for researchers to study a complex situation. For example, the development of a long-term disease over a long period. Figure 1 summarizes some of the recent cases that applied AI algorithms for data analysis. (A) Recognition of dance ground-jumping techniques based on KNN algorithm. Reused under CC BY 4.0. (B) The application of the CSF-TENG in defect detection [11]. Reused under CC BY 4.0. (C) Decoding lip language using triboelectric sensors with deep learning [12]. Reused under CC BY 4.0. (D) Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy [13]. Reused under CC BY 4.0. The combination of TENG sensors, cloud techniques, and AI algorithms could have a better use in the future. Therefore, we propose a new topic of TI based on the three cores. To better explain the concept, we first summarized the current advances of TENG sensors, including the application of cloud and AI, and then gave some examples of the feature application scenarios of TI. Human body related, including motion sensors [17, 24-40], health care sensors [41-45], etc. Smart home [45-53], including security sensors, activity sensors, and safety sensors Environment-related sensors, such as weather sensors [23, 54-60] Human–machine interaction [17, 27, 61-69] TENG sensors that apply to the human body directly or indirectly are generally used for two purposes: one purpose is to sense body motion, and the other is to sense health care/medicine-related parameters. To sense body motion, TENG sensors can directly use the triboelectricity of human skin to generate detectable electrostatic signals. Due to the strong positive charge affinity of human skin (dry), triboelectric charges are generated while the human body is making motions. Triboelectric signals have been measured both directly by attaching an electrode on the skin [37, 70] and indirectly by electrostatic induction between the human body and measurement units [61, 63]. TENGs can also be indirectly attached to different parts of the human body to sense the body's motion or parameters but do not involve the human skin's triboelectricity. Figure 1 shows information that can be obtained by using TENG sensors attached to different parts of the human body. This type of information reflects the human body's condition, as further information can be extracted by analyzing the information. Further discussion is given below. Due to the flexibility in operation modes, it is possible for TENGs to be applied at different places in a house or apartment. This kind of application contributes to the construction of a smart home system because it allows people to control appliances [69, 71], secure the house [46], sense activities, and harvest energy from indoor activities. One of the most studied applications of TENGs in a smart home is to secure the door using a TENG-based lock system [72]. A smart lock can be made similar to a normal keyboard-based interface [53] but TENGs also operate in contact-separation mode or a disk-like system [48] that can be performed by sliding a finger on it. These types of smart locks use two kinds of information to secure a door. One is the code generated by the hand/finger motion. The other is the homeowner's behavioral biometric information. More complex information can be obtained by a specific combination of TENGs that provides higher security. Controlling appliances is another application that has been studied in the past several years. A better way of controlling appliances enhances the convenience and comfort of our daily life. Taking advantage of TENGs, control has been achieved both wired and wirelessly. The TENGs can be fixed at a particular place or made portable. Few studies on monitoring indoor activities [73] have also been reported in the past few years. A floor or carpet embedded with TENG sensors could tell where a person was walking, sitting, or doing other activities. By analyzing the features of the signals from the sensors, it is possible to tell who is performing the activities. Monitoring climate and weather helps us understand more about nature on Earth. Methods and devices exist to measure the parameters of climate and weather, such as anemometers, rain gauges, and tides. These methods use various techniques that produce different types of data. In the past several years, TENG-based sensors have been applied to measure climate and weather parameters. An example of this is to measure wind speed [74] using TENGs operated in sliding or contact-separation mode. Another example is to sense raindrops [75] based on contact electrification at a solid-liquid interface. Communicating with and controlling machines have also been realized by using TENG-based sensors. In contrast from other types of human-computer interactions, TENG-based communication is self-powered, requiring no external power supply. Examples of TENG devices for human–machine interaction are keyboards, touchpads, etc. Body signals, such as hand gestures [76] and finger movement [63], can be translated to commands for controlling machines. By applying machine learning algorithms, finger movements have been used for text and graphical inputs. The TI we proposed in the paper integrates TENG sensors, cloud technologies, and AI to create a seamless and efficient system for monitoring, decision-making, and adaptive response (Figure 2). This approach leverages the unique capabilities of each component to form a synergistic framework that surpasses traditional methods of data collection and processing. A simple network of triboelectric intelligence. TI in the paper is based on combining the TENG sensors, cloud technologies, and AI. Briefly, an activity sensed by a TENG sensor generates data to be processed by AI technologies and communicated via a cloud. The cloud integrated with AI gives suggestions to a user who makes decisions and the outcomes of the decisions will be sent back to the cloud and processed with AI. Enhanced data collection and communication: The TENG sensors are designed to detect and capture specific activities or environmental changes with high sensitivity. These sensors generate data that is subsequently transmitted to the cloud for processing. By utilizing cloud technologies, the system enables a robust communication network among multiple actors. This includes devices, users, and external systems, ensuring real-time updates and collaborative functionality across the network. AI-driven decision support: Once the data is collected, AI algorithms analyze it to extract meaningful patterns, detect anomalies, or predict outcomes. This processing is conducted in the cloud, where computational resources can be efficiently allocated. The AI not only processes the raw data but also provides actionable recommendations to the end user. For example, it might suggest an optimal course of action, adjustments to system parameters, or preventive measures based on predictive analytics. Feedback loop for continuous improvement: After the user makes decisions based on the AI-generated suggestions, the outcomes of these decisions are fed back into the system. This feedback is invaluable for refining the AI models and improving their accuracy and reliability over time. This closed-loop mechanism ensures that the TI remains adaptive, learning continuously from real-world scenarios to enhance its performance. Scalability: The integration of cloud technologies allows the system to scale effortlessly as more sensors or users are added. Interoperability: The architecture supports communication among heterogeneous devices and platforms, ensuring compatibility and ease of integration. Real-time insights: The combination of high-frequency data collection with cloud-based AI processing ensures that users receive timely and relevant information. User-centric design: By involving users in the decision-making process and incorporating their feedback, the system prioritizes usability and practical impact. Body movements directly or indirectly reflect a person's emotions and health. Many of these movements can activate TENG sensors, generating measurable signals. In most cases, TENG sensors designed to monitor body movements serve a relatively simple purpose: sensing individual motions. Sensor placement: TENG sensors are strategically placed on the starting blocks and within the athlete's shoes. Data collection: Data is gathered as the athlete begins to run. Signals from the starting blocks capture information about the athlete's launch, while the sensors in the shoes record step intensity, timing, and frequency. AI analysis: The collected data is processed using AI, which builds and optimizes a model over time. This model establishes correlations between the collected data and the athlete's performance scores. Personalized feedback: The model provides actionable guidance tailored to the individual, suggesting specific actions or adjustments to enhance physical performance. The recommendations can be further personalized based on unique characteristics of the user. TI smart sports. The illustration shows a general process from data collection to data process, analysis, decision-making, and strategies. This type of triboelectric intelligence is applicable not only to athletes but also to the general population and individuals with medical conditions. One significant advantage of this system is that the data collected from various TENG sensors shares the same dimensions and time coordinates. This uniformity facilitates efficient feature extraction and the development of robust and accurate models. In addition to monitoring body parameters, TI has significant potential in medicine and healthcare-related areas. One promising application is in studying the pharmacokinetics of medication administered to patients, particularly individuals with neurological diseases such as Parkinson's disease. The integration of TI with wearable and wireless TENG sensors offers a novel approach to enhancing patient care and improving health outcomes. Figure 4 illustrates how TI could be utilized for the long-term monitoring and care of a person with Parkinson's disease. In this scenario, wearable or wireless TENG sensors are deployed to monitor the patient's motions and generate real-time data. These sensors can track subtle movements, tremors, or other physiological changes associated with Parkinson's. The real-time data is transmitted to the TI system, which processes the information, extracts relevant features, and conducts an in-depth analysis of the patient's physical condition. TI smart healthcare. The illustration shows a working process of TI smart healthcare. For the patient: The system could send reminders to take prescribed medications or perform therapeutic exercises. These prompts are essential in managing Parkinson's symptoms and maintaining quality of life. For healthcare providers: If the TI system detects any abnormal patterns or potential health risks, it can alert the patient's doctor, caregiver, or hospital in real-time. This enables timely medical intervention and reduces the risk of complications. For data-driven insights: Over time, the system can compile longitudinal data, providing valuable insights into the progression of the disease and the effectiveness of treatment plans. The success of such applications relies heavily on two critical components: the sensing technology and the AI model. Unlike signals generated by TENG sensors in other scenarios (e.g., sports or environmental monitoring), the signals from sensors detecting body motions in healthcare settings are more complex because there are many signals from the human body that generated by body motions. Variations in signal intensity are common, influenced by factors such as the patient's activity level, sensor placement, and the operational mode of the TENG sensors. AI algorithms play a crucial role in handling this complexity. Advanced models can extract features, adapt to variability, and provide predictive insights. The potential of TI extends beyond Parkinson's disease management. It can be applied to a range of healthcare scenarios, such as chronic disease monitoring, rehabilitation, and elderly care. TI smart healthcare represents a transformative approach to healthcare, offering new possibilities for patient monitoring, disease management, and personalized care because TI can sense the body motions generating triboelectric signals (Figure 5). By bridging the gap between advanced sensing technologies and intelligent data processing, TI has the potential to redefine the future of medical practice and improve the lives of countless individuals. Triboelectric intelligence for smart health care/medicine. The right side shows information that can be extracted from TENG sensors. The left side shows physiological information that can be read from the information. Bp, blood pressure; E, emotion; Hf, heart function; Lf, lung function; ND, neural diseases; Pf, physical function; T, tiredness. As we transition into the era of the Internet of Things (IoT) and blockchain, the demand for a vast number of sensors to collect information from our surroundings is rapidly increasing. Numerous studies have explored the application of IoT technologies in home environments, commonly referred to as smart homes. The three fundamental functions of a smart home include security, appliance control, and indoor activity monitoring. These functions are typically achieved using various technologies, such as infrared (IR) sensors and remote controllers. However, integrating these functions into a unified system remains a challenge due to the complexities involved in consolidating information from different sensors. TENG sensors, when applied to smart homes, offer a promising solution for achieving these functionalities within a single application. Figure 6 illustrates a TENG-based smart home concept. TENGs can be strategically placed in various locations, such as floors, beds, doors, or even incorporated into shoes. These sensors can serve dual purposes: harvesting energy from human activities and sensing those activities. Unlike traditional IR-based systems, which can only detect the presence of movement, TENG sensors possess the unique capability to identify who is moving. This is made possible by the rich biometric information captured by the sensor, such as weight, motion frequency, gait, and other distinctive characteristics. Triboelectric intelligence for the smart home. TENG sensors can be placed at different places, such as floors, doors and shoes, to sense indoor activities. Wearable TENG sensors can be used to enhance home security and control appliances. The personal characteristics embedded in the data enable advanced security applications, such as smart lock systems, which can identify individuals based on their unique movement patterns. This adds an extra layer of security and personalization to smart home systems. Moreover, the ability to harvest energy from everyday activities aligns with sustainable energy practices, reducing the reliance on external power sources for sensors. By integrating TENG sensors into smart homes, we can bridge the gap between functionality and efficiency, paving the way for more intelligent and eco-friendly living environments. Human–machine interactions encompass a wide spectrum of applications, ranging from daily activities to advanced industrial operations. TI has shown remarkable potential to enhance these interactions by improving user experiences and optimizing efficiency, as demonstrated in previous studies. The integration of TI into various systems, such as vehicles, highlights its ability to significantly benefit both the system's functionality and the user's experience. In the context of transportation (Figure 7), TI technologies can offer innovative solutions for monitoring, sensing, and optimizing vehicle performance and user safety. TENG sensors embedded within vehicles can monitor key operational parameters such as speed, vibration levels, and temperature, while also assessing external conditions like wind speed, ambient temperature, and precipitation. Additionally, wearable TENG sensors can track drivers' physiological states and behaviors. This dual-layered sensing capability enables the TI system to provide comprehensive insights into the car's condition, the environment, and the driver's status. Triboelectric intelligence for smart transportation. TENG sensors can be used to monitor drivers' tiredness, passengers' movement, and the flow of passengers at different stations. For instance, TI systems can display real-time data on vehicle performance and weather conditions, enabling drivers to make informed decisions and adjust their behavior accordingly. 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