Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

Triboelectric Contact Localization Electronics: A Systematic Review

The growing demand from the extended reality and wearable electronics market has led to an increased focus on the development of flexible human-machine interfaces (HMI). These interfaces require efficient user input acquisition modules that can realize touch operation, handwriting input, and motion sensing functions. In this paper, we present a systematic review of triboelectric-based contact localization electronics (TCLE) which play a crucial role in enabling the lightweight and long-endurance designs of flexible HMI. We begin by summarizing the mainstream working principles utilized in the design of TCLE, highlighting their respective strengths and weaknesses. Additionally, we discuss the implementation methods of TCLE in realizing advanced functions such as sliding motion detection, handwriting trajectory detection, and artificial intelligence-based user recognition. Furthermore, we review recent works on the applications of TCLE in HMI devices, which provide valuable insights for guiding the design of application scene-specified TCLE devices. Overall, this review aims to contribute to the advancement and understanding of TCLE, facilitating the development of next-generation HMI for various applications.

Handwriting Declines With Human Aging: A Machine Learning Study

Background

Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.

Materials and Methods

One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.

Results

Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83).

Discussion

Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.

Emerging Iontronic Sensing: Materials, Mechanisms, and Applications

Iontronic sensors represent a novel class of soft electronics which not only replicate the biomimetic structures and perception functions of human skin but also simulate the mechanical sensing mechanism. Relying on the similar mechanism with skin perception, the iontronic sensors can achieve ion migration/redistribution in response to external stimuli, promising iontronic sensing to establish more intelligent sensing interface for human-robotic interaction. Here, a comprehensive review on advanced technologies and diversified applications for the exploitation of iontronic sensors toward ionic skins and artificial intelligence is provided. By virtue of the excellent stretchability, high transparency, ultrahigh sensitivity, and mechanical conformality, numerous attempts have been made to explore various novel ionic materials to fabricate iontronic sensors with skin-like perceptive properties, such as self-healing and multimodal sensing. Moreover, to achieve multifunctional artificial skins and intelligent devices, various mechanisms based on iontronics have been investigated to satisfy multiple functions and human interactive experiences. Benefiting from the unique material property, diverse sensing mechanisms, and elaborate device structure, iontronic sensors have demonstrated a variety of applications toward ionic skins and artificial intelligence.

Electron-Ion Coupling Mechanism to Construct Stable Output Performance Nanogenerator

Recently, triboelectric nanogenerators (TENGs) have been promoted as an effective technique for ambient energy harvesting, given their large power density and high energy conversion efficiency. However, traditional TENGs based on the combination of triboelectrification effect and electrostatic induction have proven susceptible to environmental influence, which intensively restricts their application range. Herein, a new coupling mechanism based on electrostatic induction and ion conduction is proposed to construct flexible stable output performance TENGs (SOP-TENGs). The calcium chloride doped-cellulose nanofibril (CaCl₂-CNF) film made of natural carrots was successfully introduced to realize this coupling, resulting from its intrinsic properties as natural nanofibril hydrogel serving as both triboelectric layer and electrode. The coupling of two conductive mechanisms of SOP-TENG was comprehensively investigated through electrical measurements, including the effects of moisture content, relative humidity, and electrode size. In contrast to the conventional hydrogel ionotronic TENGs that require moisture as the carrier for ion transfer and use a hydrogel layer as the electrode, the use of a CaCl₂-CNF film (i.e., ion-doped natural hydrogel layer) as a friction layer in the proposed SOP-TENG effectively realizes a superstable electrical output under varying moisture contents and relative humidity due to the compound transfer mechanism of ions and electrons. This new working principle based on the coupling of electrostatic induction and ion conduction opens a wider range of applications for the hydrogel ionotronic TENGs, as the superstable electrical output enables them to be more widely applied in various complex environments to supply energy for low-power electronic devices.

Conductive Porous MXene for Bionic, Wearable, and Precise Gesture Motion Sensors

Reliable, wide range, and highly sensitive joint movement monitoring is essential for training activities, human behavior analysis, and human-machine interfaces. Yet, most current motion sensors work on the nano/microcracks induced by the tensile deformation on the convex surface of joints during joint movements, which cannot satisfy requirements of ultrawide detectable angle range, high angle sensitivity, conformability, and consistence under cyclic movements. In nature, scorpions sense small vibrations by allowing for compression strain conversion from external mechanical vibrations through crack-shaped slit sensilla. Here, we demonstrated that ultraconformal sensors based on controlled slit structures, inspired by the geometry of a scorpion's slit sensilla, exhibit high sensitivity (0.45%deg-1), ultralow angle detection threshold (~15°), fast response/relaxation times (115/72 ms), wide range (15° ~120°), and durability (over 1000 cycles). Also, a user-friendly, hybrid sign language system has been developed to realize Chinese and American sign language recognition and feedback through video and speech broadcasts, making these conformal motion sensors promising candidates for joint movement monitoring in wearable electronics and robotics technology.