Human Interface Research Articles

Effect of 3D Printing on the Mechanical Properties of FDM Printed Electrical Components

The Additive manufacturing (AM) technology, also known as 3D printing, reduces production time, enhances the production process and quality, lowers costs, and achieves several other goals when producing solid, three-dimensionally created structures for the application of digital information, medical equipment, automobile, airplane, and many more. By giving the necessary instruction through a human-machine interface, the techniques used to build three-dimensional structures allow for adding material in successively thinner layers, enabling manufacturing objects in almost any shape or geometry. The polylactic acid filament was used as the feedstock to create a range of 3D-printed electrical parts. The components were created using an FDM 3D printer after being designed in Solidworks. Samples of printed materials were subjected to experimental testing to ascertain their densities, tensile strengths, flexural strengths, thermalgravimetric analyses, and Fourier transform infrared spectroscopy (FTIR) values. Heating PLA polymers change their mechanical properties. The experiment run utilizes the printed samples to compare the findings with the standard values for each parameter. The production of locally produced 3D printing feedstock and waste management are the two biggest obstacles to 3D printing in Nigeria that this innovation will overcome. This strategy, which would reduce the importation of spare parts, will benefit the industrial sector. Less equipment will need to be discarded because there will be fewer components. Thus, it may be possible to achieve sustainable development goals for infrastructure, innovation, and business.

Real-time hardware emulation of neural cultures: A comparative study of in vitro, in silico and in duris silico models

Biological neural networks are well known for their capacity to process information with extremely low power consumption. Fields such as Artificial Intelligence, with high computational costs, are seeking for alternatives inspired in biological systems. An inspiring alternative is to implement hardware architectures that replicate the behavior of biological neurons but with the flexibility in programming capabilities of an electronic device, all combined with a relatively low operational cost. To advance in this quest, here we analyze the capacity of the HEENS hardware architecture to operate in a similar manner as an in vitro neuronal network grown in the laboratory. For that, we considered data of spontaneous activity in living neuronal cultures of about 400 neurons and compared their collective dynamics and functional behavior with those obtained from direct numerical simulations (in silico) and hardware implementations (in duris silico). The results show that HEENS is capable to mimic both the in vitro and in silico systems with high efficient-cost ratio, and on different network topological designs. Our work shows that compact low-cost hardware implementations are feasible, opening new avenues for future, highly efficient neuromorphic devices and advanced human–machine interfacing.

Unsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs.

Human-Machine Interfaces (HMIs) have gained popularity as they allow for an effortless and natural interaction between the user and the machine by processing information gathered from a single or multiple sensing modalities and transcribing user intentions to the desired actions. Their operability depends on frequent periodic re-calibration using newly acquired data due to their adaptation needs in dynamic environments, where test-time data continuously change in unforeseen ways, a cause that significantly contributes to their abandonment and remains unexplored by the Ultrasound-based (US-based) HMI community. In this work, we conduct a thorough investigation of Unsupervised Domain Adaptation (UDA) algorithms for the re-calibration of US-based HMIs during within-day sessions, which utilize unlabeled data for re-calibration. Our experimentation led us to the proposal of a CNN-based architecture for simultaneous wrist rotation angle and finger gesture prediction that achieves comparable performance with the state-of-the-art while featuring 87.92% less trainable parameters. According to our findings, DANN (a Domain-Adversarial training algorithm), with proper initialization, offers an average 24.99% classification accuracy performance enhancement when compared to no re-calibration setting. However, our results suggest that in cases where the experimental setup and the UDA configuration may differ, observed enhancements would be rather small or even unnoticeable.

Design of a robotic gripper for casting sorting robots with rigid–flexible coupling structures

AbstractIn order to solve the problem of the insufficient adaptability of the current small- and medium-sized casting sorting robot gripper, we have designed a casting sorting robot bionic gripper with rigid–flexible coupling structures based on the robot topology theory. The second-order Yeoh model was used to statically model the clamping belt in the gripper to derive the relationship between the external input air pressure and the bending angle of the driving layer, and the feasibility of multiangle bending of the driving layer was verified by finite element analysis. The maximum gripping diameter of the gripper is 140 mm, and in order to test the adaptive gripping ability of the gripper, a prototype of the casting sorting robot gripper is prepared, and the pneumatic control system and human–machine interface of the gripper are designed. After several experimental analyses, the designed casting sorting robot gripper is characterized by strong adaptability and high robustness, with a maximum load capacity of 930 g and a maximum wrap angle of 296°, which can complete the gripping operation within 1 s, and the comprehensive gripping success rate reaches 96.4%. The casting sorting robot gripper designed in the paper can provide a reference for the design and optimization of various types of shaped workpiece gripping manipulators.

Ultrahigh Sensitivity for Thermographic Human-Machine Interface via Precious Metals Atomic Layer Deposition on V-MXene: Computational and Experimental Exploration.

Global healthcare based on the Internet of Things system is rapidly transforming to measure precise physiological body parameters without visiting hospitals at remote patients and associated symptoms monitoring. 2D materials and the prevailing mood of current ever-expanding MXene-based sensing devices motivate to introduce first the novel iridium (Ir) precious metal incorporated vanadium (V)-MXene via industrially favored emerging atomic layer deposition (ALD) techniques. The current work contributes a precise control and delicate balance of Ir single atomic forms or clusters on the V-MXene to constitute a unique precious metal-MXene embedded heterostructure (Ir-ALD@V-MXene) in practical real-time sensing healthcare applications to thermography with human-machine interface for the first time. Ir-ALD@V-MXene delivers an ultrahigh durability and sensing performance of 2.4%°C-1 than pristine V-MXene (0.42%°C-1), outperforming several conventionally used MXenes, graphene, underscoring the importance of the Ir-ALD innovative process. Aberration-corrected advanced ultra-high-resolution transmission/scanning transmission electron microscopy confirms the presence of Ir atomic clusters on well-aligned 2D-layered V-MXene structure and their advanced heterostructure formation (Ir-ALD@V-MXene), enhanced sensing mechanism is investigated using density functional theory (DFT) computations. A rational design empowering the Ir-ALD process on least explored V-MXene can potentially unfold further precious metals ALD-process developments for next-generation wearable personal healthcare devices.

Self-powered and degradable humidity sensors based on silk nanofibers and its wearable and human–machine interaction applications

Humidity sensors have received increasing attention due to their broad potential application in wearable devices, health monitoring, intelligent control, and other fields. However, traditional humidity sensors are prone to produce various toxic substances to pollute the environment after discarding. Also, they are prone to damage during use, which shortens their service life. Therefore, the development of eco-friendly, degradable, and repairable humidity sensors is of great significance for reducing e-waste and protecting the environment. Here, we started with the selection of materials by using silk nanofiber (SNF) film as the substrate material, copper/aluminum as electrodes, and calcium chloride as the electrolyte, combined with the working mechanism of the primary battery, to construct a self-powered humidity sensor. The humidity sensor exhibits excellent humidity sensing performance and good linearity within the range of 30–90 % relative humidity (RH). A single humidity sensor can output 346 mV at 90 % RH. After being damaged, the humidity sensor can be repaired with the help of water. When the ruptured SNF film encounters water molecules, the water molecules can prompt the hydrogen bonds between the SNFs to rearrange and connect, connecting the broken area. Young’s modulus retention rate of the repaired SNF film is over 85 %. The repaired humidity sensor can also output 328 mV. Additionally, an alkaline solution can completely degrade the humidity sensor in less than 80 min. The proposed humidity sensors have been applied to wearable self-powered wristbands, wireless monitoring systems, and non-contact human–machine interaction systems. Hence, we provide comprehensive research from the material level (SNFs) to the functional device level (the self-powered humidity sensor), and then to the system level (wearable and human–machine interface system). The research will open a new way for the design of the self-powered humidity sensor and have great potential applications in wearable electronics and human–machine interface applications.

Unveiling the human fetal-maternal interface during the first trimester: biophysical knowledge and gaps.

The intricate interplay between the developing placenta and fetal-maternal interactions is critical for pregnancy outcomes. Despite advancements, gaps persist in understanding biomechanics, transport processes, and blood circulation parameters, all of which are crucial for safe pregnancies. Moreover, the complexity of fetal-maternal interactions led to conflicting data and methodological variations. This review presents a comprehensive overview of current knowledge on fetal-maternal interface structures, with a particular focus on the first trimester. More in detail, the embryological development, structural characteristics, and physiological functions of placental chorionic plate and villi, fetal membranes and umbilical cord are discussed. Furthermore, a description of the main structures and features of maternal and fetal fluid dynamic exchanges is provided. However, ethical constraints and technological limitations pose still challenges to studying early placental development directly, which calls for sophisticated in vitro, microfluidic organotypic models for advancing our understanding. For this, knowledge about key in vivo parameters are necessary for their design. In this scenario, the integration of data from later gestational stages and mathematical/computational simulations have proven to be useful tools. Notwithstanding, further research into cellular and molecular mechanisms at the fetal-maternal interface is essential for enhancing prenatal care and improving maternal and fetal health outcomes.

Research on Information Fusion Method for Human Computer Interaction Interface of Command and Control System in MR Environment

In response to the challenge of high cognitive pressure and difficulty for commanders in cross-layer operations during the transition from traditional two-dimensional command and control system interfaces to mixed reality human-computer interaction interfaces, this paper conducts research on the information fusion display method for the human-computer interaction interface of the mixed reality command and control system. First, based on the findings of previous research, we developed a five-layer human-computer interaction interface for a mixed reality command and control system in typical combat scenarios. An evaluation experiment was designed and the resulting data analyzed. This analysis revealed that there are problems with low work efficiency and high cognitive load in the information acquisition process in cross-layer interaction operations. Consequently, we conducted research into the methods of integrating and displaying interactive interface information, integrating and displaying interactive and display interface information, and integrating and displaying complex situation interface information. The research findings indicate that: (1) The optimal display ratio for mixed reality 2D interface integration is 3:2. (2) The optimal display scheme for mixed reality 2D interface fusion is a combination of the main and secondary interfaces, with the main interface table displaying global information and the secondary interface displaying accurate information. (3) The optimal display scheme for mixed reality 2D and 3D interface fusion is the display of overall class information in 2D and the accurate identification of class information in 3D. The findings of this research can be applied to the design of human-computer interaction interfaces for mixed reality command and control systems in complex battlefield environments.

Temperature Monitoring System for 3 Phase Electric Arc Furnace (EAF) Transformer Cooling

The Electric Arc Furnace (EAF) transformer is a key component in the steel melting system using a furnace. The transformer functions to convert the electrical voltage from the main electrical network into the electrical voltage needed to form an electric arc in the melting furnace. In this research, the EAF transformer configuration uses a delta-wye configuration with a furnace melting capacity of 10 kg nickel ore. In its working system, this transformer will produce a very high current when the melting system is running. So this process will produce excessive heat on the EAF transformer side. As a result of the significant heat generated in the melting process, this transformer is equipped with a cooling system in the form of transformer oil which is a heat release medium and maintains the correct operating temperature. An appropriate temperature monitoring system will increase the work efficiency of the transformer and prevent excessive damage to the system. The sensor used in the design is the RTD PT 100 sensor which is installed at 7 measurement points. The main controller for data collecting and data processing used is a Siemens PLC. Data that has been processed on the main control device is transmitted to the IoT cloud server using a V-box device. As a human machine interface (HMI) web SCADA is used to read data in real time. From the results of the tests that have been carried out, the highest error presentation value obtained in measurements on the T phase is 0.05%. Meanwhile, when measuring the transformer oil sensor, an error value of 0% was obtained.

Harmonic enhancement to optimize EOG based ocular activity decoding: A hybrid approach with harmonic source separation and EEMD

Intelligent robotic systems for patients with motor impairments have gained significant interest over the past few years. Various sensor types and human-machine interface (HMI) methods have been developed; however, most research in this area has focused on eye-blink-based binary control with minimal electrode placements. This approach restricts the complexity of HMI systems and does not consider the potential of multiple-activity decoding via static ocular activities. These activities pose a decoding challenge due to non-oscillatory noise components, such as muscle tremors or fatigue. To address this issue, a hybrid preprocessing methodology is proposed that combines harmonic source separation and ensemble empirical mode decomposition in the time-frequency domain to remove percussive and non-oscillatory components of static ocular movements. High-frequency components are included in the harmonic enhancement process. Next, a machine learning model with dual input of time-frequency images and a vectorized feature set of consecutive time windows is employed, leading to a 3.8% increase in performance as compared to without harmonic enhancement in leave-one-session-out cross-validation (LOSO). Additionally, a high correlation is found between the harmonic ratios of the static activities in the Hilbert-Huang frequency spectrum and LOSO performances. This finding highlights the potential of leveraging the harmonic characteristics of the activities as a discriminating factor in machine learning-based classification of EOG-based ocular activities, thus providing a new aspect of activity enrichment with minimal performance loss for future HMI systems.

Prevalence of intestinal parasites in humans and domestic animals in Jirel community, Dolakha, Nepal.

Gastrointestinal (GI) parasites are major health concerns in both humans and domestic animals. Livestock farming is one of the common livelihood practices in rural Nepal. The proximity at human and domestic animal interface increases the chances of dissemination of enteric parasites, especially those of zoonotic importance. This study was aimed at finding the parasite prevalence and risk factors in both humans and their domestic animals in Jirel community. A field survey was conducted on the Jirel ethnic people and their domestic animals in Dolakha district, where a total of 152 fresh fecal samples from humans and domestic animals (cow, pigs, goats, chickens, ducks, and pigeons) were collected. The feces were examined by wet mounts and concentration techniques. A structured questionnaire survey was carried out among the local people and owners of the domestic animals to gather sociodemographic information, awareness, and hygienic practices in relation to parasite transmission. The enteric parasite prevalence was found to be highest in goats (80.0%;12/15), followed by pigs (55.55%;5/9), cows (45.45%;6/11), chickens (11.7%;4/34), and humans (1.41%;1/71), while the fecal samples of ducks and pigeons did not contain any parasites. The only parasite identified in humans was Ascaris lumbricoides. Similarly, three genera of GI parasites (Eimeria sp., Strongyloides sp, and Trichuris sp.) from goats, two genera each from cow (Eimeria sp. and Strongyloides sp.), pigs (Entamoeba sp. and A. suum), and chickens (Eimeria sp. and Ascaridia galli), were detected. Based on the direct field observation, questionnaire survey and laboratory analysis, it is concluded that the Jirel community people are aware of health and hygiene; however, intervention measures are necessary to prevent parasitic infection in their domestic animals.

High stretchable and self-adhesive multifunctional hydrogel for wearable and flexible sensors

Ionic conductive hydrogel has recently garnered significant research attention due to its potential applications in the field of wearable and flexible electronics. Nonetheless, the integration of multifunctional and synergistic advantages, including reliable electronic properties, high swelling capacity, exceptional mechanical characteristics, and self-adhesive properties, presents an ongoing challenge. In this study, we have developed an ionic conductive hydrogel through the co-polymerization of 4-Acryloylmorpholine (ACMO) and sodium acrylate using UV curing technology. The hydrogel exhibits excellent mechanical properties, high conductivity, superior swelling capacity, and remarkable self-adhesive attributes. The hydrogel serves as a highly sensitive strain sensor, enabling precise monitoring of both substantial and subtle human motions. Furthermore, the hydrogel demonstrates the capability to adhere to human skin, functioning as a human-machine interface for the detection of physiological signals, including electromyogram (EMG) signals, with low interfacial impedance. This work is anticipated to yield a new class of stretchable and conductive materials with diverse potential applications, ranging from flexible sensors and wearable bio-electronics to contributions in the field of artificial intelligence.

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

Abstract September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.

An Innovative Device Based on Human-Machine Interface (HMI) for Powered Wheelchair Control for Neurodegenerative Disease: A Proof-of-Concept.

In the global context, advancements in technology and science have rendered virtual, augmented, and mixed-reality technologies capable of transforming clinical care and medical environments by offering enhanced features and improved healthcare services. This paper aims to present a mixed reality-based system to control a robotic wheelchair for people with limited mobility. The test group comprised 11 healthy subjects (six male, five female, mean age 35.2 ± 11.7 years). A novel platform that integrates a smart wheelchair and an eye-tracking-enabled head-mounted display was proposed to reduce the cognitive requirements needed for wheelchair movement and control. The approach's effectiveness was demonstrated by evaluating our system in realistic scenarios. The demonstration of the proposed AR head-mounted display user interface for controlling a smart wheelchair and the results provided in this paper could highlight the potential of the HoloLens 2-based innovative solutions and bring focus to emerging research topics, such as remote control, cognitive rehabilitation, the implementation of patient autonomy with severe disabilities, and telemedicine.

Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform

This paper presents our innovative approach in the field of Internet of Things (IoT). Our focus was on enhancing the research environment at Cheng Shiu University's laboratory through the creation of a dynamic network monitoring setup and lighting control system. Our aim was to simplify equipment management and promote energy efficiency. The core of our development is the Raspberry Pi 4B embedded Linux platform, which serves as a host for both a Message Queuing Telemetry Transport (MQTT) broker and a Node-RED server. Python programs, developed in PyCharm IDE, utilize multiple threads for efficient data exchange via Modbus TCP and MQTT protocols, facilitating seamless interaction between PLCs and MQTT broker servers. The Node-RED IoT development tool was instrumental in crafting a cross-platform Human-Machine Interface (HMI) web server with MQTT Publish/Subscribe capabilities, ensuring smooth communication with the MQTT broker server. By configuring the Apache HTTP server to direct to the Node-RED web directory, we can effectively monitor laboratory conditions and manage the lighting system. The experimental results validate the cost-effectiveness of our approach in transforming traditional control systems into a web-based supervisory control system, thereby enhancing the overall functionality of existing equipment. This project underscores our commitment to advancing IoT solutions in practical settings.

Photo-programmable hydrogel iontronics for electrically and chromatically rewritable circuits

Hydrogel-based iontronics is emerging as a promising frontier in healthcare and human-machine interfacing (HMI), offering excellent compatibility with biological systems in terms of electrical, chemical, and mechanical properties. However, conventional hydrogel systems have limitations in dynamically regulating their electrical and optical properties, which restricts their use in adaptive electronics and responsive interfaces. In this study, we present a new hydrogel system with UV photochemistry-induced reversible conductivity, enabling reversible changes in conductivity. Unlike typical photo-responsive hydrogels that revert to their original states upon removal of the light source, the new hydrogel can maintain its activated states without continuous light exposure, facilitating practical applications. By leveraging the photobase triphenylmethane leucohydroxide and photoacid n-nitrobenzaldehyde, we achieve a significant increase in photo-induced conductivity compared to existing photo-ionic hydrogels. Combining the effective photo-induced conductivity and the accompanied photochromatic effect, we demonstrate a full hydrogel-based stylus pad capable of tracking motion and strokes, and a soft calculator keypad with programmable conductivity and imprinted patterns. These advancements underscore the importance of actively controlling localized conductivity and processing light inputs in hydrogels, exhibiting their potential for diverse applications in bioelectronics and HMI.

Modeling risk potential fields for mandatory lane changes in intelligent connected vehicle environment

In the environment of Intelligent Connected Vehicles (ICVs), the ICV system is capable of gathering, processing, and transmitting the majority of traffic information to each vehicle, thereby empowering drivers to make more informed and secure decisions. Nevertheless, ICVs are still incapable of completely avoiding traffic conflicts and accidents. Consequently, developing a risk assessment model for ICVs is crucial for their deployment. To investigate the dynamic patterns of lane-changing vehicle risks within ICV environments, this study developed a quantitative relationship model based on the Artificial Potential Field (APF) theory. The model correlates the Total Potential Field (TPF) of mandatory lane-changing (MLC) vehicle with the longitudinal/transverse gravitational potential field (LGPF/TGPF) and the longitudinal/transverse repulsion potential field (LRPF/TRPF). Subsequently, three sets of Human Machine Interface (HMI) systems were developed to delineate distinct ICV environments: baseline, warning group, and guidance group. The baseline furnishes fundamental data, the warning group supplements preceding vehicle icons and real-time headway information, whereas the guidance group additionally augments speed and voice guidance functionalities. Following this, a simulated driving experiment involving 43 participants was carried out to simulate MLC scenarios in highway merging sections. Using enumeration and gradient descent methods, the quantitative relationship between different types of risk fields was determined. The findings indicated that the ICV environment significantly impacts the mean values of LGPF, TGPF, LRPF (Leading Vehicle, LV), and TRPF, while insignificantly affecting the mean value of LRPF (Following Vehicle, FV). Nevertheless, a notable discrepancy in the mean value of LRPF (FV) was observed between the warning and guidance groups. Comparative results with lane positions demonstrate that the proposed TPF accurately depicts vehicle lane-changing behaviors. Comparative results with classical risk indicators indicate that TPF more accurately depicts the actual driving risks and variation trends faced by vehicles in different MLC processes. The research findings will be pivotal in improving real-time control and guidance strategies for vehicles in highway merging sections under ICV environments.

Development of Standalone Extended-Reality-Supported Interactive Industrial Robot Programming System

Extended reality (XR) is one of the most important technologies in developing a new generation of human–machine interfaces (HMIs). In this study, the design and implementation of a standalone interactive XR-supported industrial robot programming system using the Unity game engine is presented. The presented research aims to achieve a cross-platform solution that enables novel tools for robot programming, trajectory validation, and robot programming debugging within an extended reality environment. From a robotics perspective, key design tasks include modeling in the Unity environment based on robot CAD models and control design, which include inverse kinematics solution, trajectory planner development, and motion controller set-up. Furthermore, the integration of real-time vision, touchscreen interaction, and AR/VR headset interaction are involved within the overall system development. A comprehensive approach to integrating Unity with established industrial robot modeling conventions and control strategies is presented. The proposed modeling, control, and programming concepts, procedures, and algorithms are verified using a 6DoF robot with revolute joints. The benefits and challenges of using a standalone XR-supported interactive industrial robot programming system compared to integrated Unity–robotics development frameworks are discussed.

Ultra-broad sensing range, high sensitivity textile pressure sensors with heterogeneous fibre architecture and molecular interconnection strategy

Textile-based pressure sensors have garnered extensive attentions owing to their potentials in health monitoring, human–machine interactions, wearable human–machine interfaces (HMIs) and soft robotics. High sensitivity over a broad sensing range are highly desired yet challenging for textile pressure sensors due to the incompressibility of matrix materials and the stiffening of microstructures. Herein, we developed a high performance pressure sensor based on the silk/3-Aminopropyltriethoxy-silane/titanium carbide (Silk/APTES/MXene) film. The silk is designed with heterogeneous fiber architecture, which enables rich and multi-level contact pattern and could compensate for the effect of structural stiffening. Furthermore, APTES molecules are used to enhance the interface bonding between MXene and silk, promoting the mechanical stability of the sensor. Benefiting from these advantages, the Silk/APTES/MXene sensor device is achieved with high sensitivity of 17.1 KPa−1 over an ultra-broad sensing range up to 3.3 MPa (R2 = 0.997), ultra-low detection limit (0.25 Pa), and low fatigue toughness after 5000 cycles loading and unloading. With these merits, we have demonstrated its capability for a series of human motion detection (such as foot movement detection, arm/wrist/finger bending, etc) and an overall accuracy of 95 % is obtained with the help of CNN-based convolution neural architecture. More significantly, we have built an entire intelligent human–machine dialogue system and a patient-audience dialogue system, from lip/sign language recognition to real-time screen display and final voice output.

Piezoresistive sensors based on bacterial cellulose/ZnO/PPy composite materials for human signal monitoring and sound detection

Wearable piezoresistive sensors based on flexible electronics have gained considerable attention in motion monitoring, human-machine interface, disease diagnosis and health monitoring. Research on flexible wearable sensors can be applied not only to basic sensing and human motion monitoring, but also to sound visualization techniques. In this paper, a piezoresistive sensor was prepared from bacterial cellulose, zinc oxide and polypyrrole and its sensing performance as a sound detector was investigated. The sensor has good sensing performance and can monitor human motion signals. As a sound detector, it can not only recognize different sound signals by monitoring the movement of the laryngeal muscles, but also sense air vibration to detect and recognize various natural sounds. Due to its ability to capture and present sound signals, it is able to build a visual-centred information dissemination system through visualization technology.