Human Interface Research Articles

Self-template manufacturing of on-skin electrodes with 3D multi-channel structure for standard 3-limb-lead ECG suit

Wearable electrocardiogram (ECG) devices are the mainstream technology in the diagnosis of various cardiovascular diseases, in which soft, flexible, permeable electrodes are the key link in human-machine interface to capture bioelectrical signals. Herein, we propose a self-template strategy to fabricate silver-coated fiber/silicone (AgCF-S) electrodes. With a simple dissolving-curing-redissolving process, the polyvinyl acetate shell around the AgCF core is in-situ removed to form a three-dimensional (3D) multi-channel structure. The conductive fibers overlap each other and pass through the silicon substrate in a network state, so that the electrode can be bent to 180° or stretched to 30%. The 3D multi-channels in AgCF-S adhesive is further coupled with a Kirigami-design structure of flexible substrate, to maintain high flexibility without sacrificing air-permeability, enabling an excellent water evaporation rate of 1.8 μg/mm2/min, and non-allergenic adhere on pigskin after 24 h. Combined with the self-developed standard 3-limb-lead ECG suit, multi-lead signals with high signal-to-noise ratio (SNR) and low variance (σ2), can be transmitted in real-time via Bluetooth and displayed in the client. Typical heart diseases such as coronary, arrhythmia, myocardial infarction, etc., are detected by our ECG equipment, revealing a huge promise in future medical electronics.

Comparing the Pressure on the Carpal Tunnel When Using an Ergonomic Pointer Driver and an Optical Alternative

Objectives: The objective of this paper is to introduce a method to measure the force or pressure over the carpal tunnel indirectly, using a new device to drive the pointer of a computer system. The measurements were compared with those obtained using an ergonomic mouse. Simultaneously, measurements of muscular stress on the digitorum extensor muscle were performed to correlate the applied force against muscle activity. Methods: An experimental setup was constructed using an infrared static receiver plus two wearable moving light emitters, which can be displaced inside a rectangular projected region. The pointer functions are performed through two finger gestures, while the hand is naturally extended. A microcontroller was used to communicate with the computer, which works as a human interface device and possesses firmware to associate the position of each light source with the pointer functions. Meanwhile, force and electromyography sensing circuits were developed to transmit and measure carpal tunnel strength and muscular stress. The system was tested on five healthy volunteers, who were encouraged to solve the same computational tasks using this new device and a trademark ergonomic mouse. Results: Our results show great differences (greater than one magnitude) between the efforts of the same volunteers performing the same predefined tasks using both pointer controllers. Only when the new device was used did the Pearson’s correlation coefficients show a higher correlation between the effort measured on the carpal tunnel and the muscular activity. Conclusions: The optic pointer driver diminishes the strength on the carpal tunnel, causing slightly increased stress on the digitorum extensor muscle.

A wearable ionic hydrogel strain sensor with double cross-linked network for human–machine interface

A wearable ionic hydrogel strain sensor with double cross-linked network for human–machine interface

One-shot learning HMI for people with disabilities

Abstract For people with physical disabilities, it is often desirable to regain control over their personal environment and communication tools. This paper introduces a novel Human- Machine Interface (HMI) using one-shot learning for individualized control signals without extensive training or specialized hardware. Our work suggests a modular system that utilizes common, easily accessible devices like webcams to interpret user-defined gestures and commands through a single demonstration. As a feasibility study on healthy volunteers, we investigate the control of a computer mouse by head movements only. We demonstrate the technical details of the HMI and discuss its potential applications in enhancing the autonomy and interaction capabilities of users with disabilities. By combining usercentric design principles with the advancements in one-shot learning, we aim to forge a more inclusive, accessible path forward in the development of assistive technologies.

Deep-SFER: deep convolutional neural network and MFCC an effective speech and face emotion recognition

There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for human-machine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC).

Real-time anomaly detection in electric motor operation noise

Anomaly detection plays a very important role in many fields to identify abnormalities occurring in the system earlier. This study proposes a new abnormality detection solution for 3-phase electric motors based on their working noise. Normal and abnormal operating noise data sets for an electric motor were acquired in the laboratory. These datasets are converted into the corresponding two-dimensional gray spectrogram image sets. The normal set is used to train the autoencoder (AE) model to find the abnormality evaluation threshold. This threshold is validated again with anomalous data sets. The trained AE is then quantized to be installed on a system consisting of two duo-core microcontroller units (MCUs) for real-time testing. Free real-time operating system (FreeRTOS), a real-time operating system, is used to schedule tasks on the system. Experimental results show that the designed anomaly detector can accurately detect over 99% of abnormal events. The system can communicate with a supervisory control and data acquisition (SCADA) application running on the S7-1200 programmable logic controller (PLC) platform using the Modbus transmission control protocol (TCP) protocol. The SCADA application can continuously record evaluated results from the system and adjust abnormal thresholds for the system directly on the human-machine interface (HMI) screen.

Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency

Incorporating artificial intelligence (AI) into in vitro fertilization (IVF) laboratories signifies a significant advancement in reproductive medicine. AI technologies, such as neural networks, deep learning, and machine learning, promise to enhance quality control (QC) and quality assurance (QA) through increased accuracy, consistency, and operational efficiency. This comprehensive review examines the effects of AI on IVF laboratories, focusing on its role in automating processes such as embryo and sperm selection, optimizing clinical outcomes, and reducing human error. AI’s data analysis and pattern recognition capabilities offer valuable predictive insights, enhancing personalized treatment plans and increasing success rates in fertility treatments. However, integrating AI also brings ethical, regulatory, and societal challenges, including concerns about data security, algorithmic bias, and the human–machine interface in clinical decision-making. Through an in-depth examination of current case studies, advancements, and future directions, this manuscript highlights how AI can revolutionize IVF by standardizing processes, improving patient outcomes, and advancing the precision of reproductive medicine. It underscores the necessity of ongoing research and ethical oversight to ensure fair and transparent applications in this sensitive field, assuring the responsible use of AI in reproductive medicine.

Recent Advances in Nanomaterial-Based Biosignal Sensors.

Recent research for medical fields, robotics, and wearable electronics aims to utilize biosignal sensors to gather bio-originated information and generate new values such as evaluating user well-being, predicting behavioral patterns, and supporting disease diagnosis and prevention. Notably, most biosignal sensors are designed for body placement to directly acquire signals, and the incorporation of nanomaterials such as metal-based nanoparticles or nanowires, carbon-based or polymer-based nanomaterials-offering stretchability, high surface-to-volume ratio, and tunability for various properties-enhances their adaptability for such applications. This review categorizes nanomaterial-based biosignal sensors into three types and analyzes them: 1) biophysical sensors that detect deformation such as folding, stretching, and even pulse, 2) bioelectric sensors that capture electric signal originating from human body such as heart and nerves, and 3) biochemical sensors that catch signals from bio-originated fluids such as sweat, saliva and blood. Then, limitations and improvements to nanomaterial-based biosignal sensors is depicted. Lastly, it is highlighted on deep learning-based signal processing and human-machine interface applications, which can enhance the potential of biosignal sensors. Through thispaper, it is aim to provide an understanding of nanomaterial-based biosignal sensors, outline the current state of the technology, discuss the challenges that be addressed, and suggest directions for development.

Transforming industrial automation: voice recognition control via containerized PLC device

The article discusses the impact of voice recognition and containerization technologies in the industrial sector, particularly on Programmable Logic Controller (PLC) devices. It highlights how voice assistants like Alexa, Siri, Cortana, and Google Assistant are pioneering and pushing the future of human–machine interfaces, with applications moving from smart homes to industrial automation. Containerization, illustrated by Docker, is transforming software deployment practices, offering benefits such as enhanced portability, modular architecture, and improved security when applied to industrial PLCs. The article introduces a novel approach to enhancing human–machine interfaces (HMIs) within industrial applications, leveraging voice recognition and containerization technologies on Programmable Logic Controllers (PLCs). Unlike traditional systems, this article integrates voice assistant with industrial PLCs through a containerized IoT architecture. This innovative framework enables efficient deployment on edge devices, supporting modular, portable, and secure operations aligned with Industry 4.0 and 5.0 paradigms. The study further includes a detailed implementation on microcontrollers and industrial PLCs, validating its application in a controlled laboratory environment and virtual model.

Polyphenol‐Mediated Multifunctional Human–Machine Interface Hydrogel Electrodes in Bioelectronics

Polyphenol‐Mediated Multifunctional Human–Machine Interface Hydrogel Electrodes in Bioelectronics

Advancing Human–Machine Interface (HMI) through Development of a Conductive‐Textile Based Capacitive Sensor

AbstractSmart wearable sensors in human–machine interfaces (HMI) facilitate communication and interface between humans and robots, as well as among humans. Conventional wearables face significant limitations, including performance degradation under various deformations (e.g., strain and pressure), limited stretchability and flexibility, poor comfort, and breathability, complicating their integration into HMI applications. In response to these limitations, a smart, sewable, high‐precision HMI device based on a soft, textile‐based sensor with machine learning (ML)‐assisted data processing is proposed. The (Ag)‐based fabric electrode integrated into polymeric composite dielectric layer, exhibiting hysteresis error of (≤4%), a tensile strain sensitivity of gauge factor (GF) ≃ 1.03 at 100% elongation, a pressure sensitivity of ≈1.583 × 10−2 kPa−1 at pressures (<50 kPa) and excellent stability (>1000 cycles). To demonstrate its versatility in HMI, the textile‐based sensor is integrated into a glove for real‐time control of a commercially available prosthetic hand and a drone, as well as for sign language recognition, achieving 95.4% classification accuracy using the random forest (RF) classifier across 10 gestures. Beyond gesture recognition, the sensor measures a range of subtle physiological activities (>0.35 kPa), functioning as a force myograph, esophageal manometry, and in gait analysis, among other strain and pressure‐related applications, highlighting exceptional capabilities for advanced wearable systems.

Human-Machine Function Allocation Method for Submersible Fault Detection Tasks

The operation and support (OS) officer is responsible for buoyancy regulation and fault detection of onboard equipment in the civil submersible. The OS officer carries out the above tasks through the human-machine interface (HMI) of a submersible buoyancy regulation and support (SBRS) system. However, the OS officer often faces uneven task frequency produced by fault tasks, which leads to an unbalanced mental workload and individual failures. To address this issue, we proposed a human-machine function allocation method based on level of automation (LOA) taxonomy and submersible task complexity (STC), aimed at improving human-machine cooperation in submersible fault detection tasks. Based on this method, we identified the LOA2 as the optimal human-computer function allocation scheme. In this study, three measurement techniques (subjective scale, work performance, and physiological status) were used to test 15 subjects to validate the effectiveness of the proposed optimal human-machine function allocation scheme. The GAMM test results also indicate that the proposed optimal human-machine function allocation scheme (LOA2) can improve the work performance of the operating system officials under low or high workloads and reduce the subjective workload.

The HADRIAN novel human–machine interface prototype for automated driving: safety and impact assessment

The current paper was performed within the HADRIAN project and focuses on exploring the effects of innovative Human–Machine Interface (HMI) prototypes on safety, driving performance, and driver perceptions. Employing driving simulator experiments and questionnaires, this study investigates whether HADRIAN innovative HMI enhances safety and receives positive evaluations from drivers. Specifically, the research centers on a driving simulator experiment that evaluates novel HMI prototypes designed to improve automated driving at SAE Levels 2 or 3. To facilitate HMI assessment, a tailored safety and impact assessment methodology was developed using unique Key Performance Indicators (KPIs). To benchmark and generate a total score for the HADRIAN HMI, data envelopment analysis was deployed based on the aforementioned KPIs. The findings shed light on the influence of HADRIAN HMI innovations on safety and perceived impact when compared to a baseline “state-of-the-art” HMI. Subsequently, a comprehensive discussion unfolds, highlighting the key KPIs that contributed significantly to the safety and perceived impact scores. This method and its outcomes can serve as a valuable resource for other HMI stakeholders, enabling them to employ similar human-centered assessment methodologies to assess the safety and perceived impact of potential HMI configurations.

Sistem Kontrol Dan Monitoring Kualitas Ph Air Pada Ground Tank Berbasis Mikrokontroller Di Bandar Udara Tjilikriwut Palangkaraya

Abstract – The centralized water pH quality monitoring control system uses a microcontroller as the control center and program. By using theoretical basics regarding relays, microcontrollers and other electronic components, the author designed a centralized monitoring control system using a computer HMI (Human Machine Interface) as a display with a pH sensor for each water quality situation. The pH sensor is an analog data reader, where the reading results will be processed by the microcontroller which will then activate the relays so that the acid-base solution pump is active when the water pH is unstable. Keywords — Monitoring controls, Microcontrollers, pH Sensors

Design and building CNC engraving router machine using Arduino Uno with GRBL control system

Computer Numerical Control Machines are one of the advances in automatically operated machining technology that can meet the need for complex and high-precision products. CNC machine parts generally have a mechanical and electrical system, also called a machine controller, that uses a Human Machine Interface at a high price. One of the software used to control the CNC machine is Arduino Uno, a PC-based GRBL System with a low price and the same function. This software is not far from the advantages of the HMI panel. Operating the machine with this software is also not that difficult. In this research, we intend to make a CNC machine using the Arduino Uno control system and software with a PC-based GRBL System, making it easier for operators to operate it. The results of the research that has been carried out show that the CNC has a size of 800x600x50mm with a system that is operated well and precisely

PLC/HMI Based Portable Workbench for PLC and Digital Logic Learning and Application Development

A Programmable logic controller (PLC) uses the digital logic circuits and their operating concepts in its hardware structure and its programming instructions and algorithms. Therefore, the deep understanding of these two items is staple for the development of control applications using the PLC. This target is only possible through the practical sensing of the various components or instructions of these two items and their applications. In this work, a user-friendly and re-configurable ladder, digital logic learning and application development design and testing platform has been designed and implemented using a Programmable Logic Controller (PLC), Human Machine Interface panel (HMI), four magnetic contactors, one Single-phase power line controller and one Variable Frequency Drive (VFD) unit. The PLC role is to implement the ladder and digital logic functions. The HMI role is to establish the virtual circuit wiring and also to drive and monitor the developed application in real time mode of application. The magnetic contactors are to play the role of industrial field actuators or to link the developed application control circuit to another field actuator like three phase induction motor. The Single -phase power line controller is to support an application like that of the soft starter. The VFD is to support induction motor driven applications like that of cut-to-length process in which steel coils are uncoiled and passed through cutting blade to be cut into required lengths. The proposed platform has been tested through the development of 14 application examples. The test results proved the validity of the proposed platform.

A numerical conductive network model for investigating the strain-sensing response of graphene nanoplatelets-filled elastomeric strain sensors

Strain sensors have attracted great attentions for practical applications such as human-machine interface. It is challenging to model the electrical behavior of elastomeric strain sensors modified with nanomaterials. This study presents a finite element simulation to explore the piezoresistivity of graphene nanoplatelet (GNP)-filled elastomeric nanocomposites. The percolation network model is employed to determine the critical distance necessary for electrical percolation. The finite element percolation network model uses the Euclidian distance between GNPs distributed inside the representative volume element to calculate the resistivity. The influence of various parameters, including GNP alignment direction, aspect ratio, and volume fraction, on the resistivity changes of the nanocomposite is investigated. Results demonstrate that nearly 20% more tunneling distance is allowed for percolation of nanocomposite with 30% larger aspect ratio GNPs. Findings reveal that as the volume fraction of GNPs increases, the critical distance for the electrical percolation decreases significantly.

Ultrathin Transistors and Circuits for Conformable Electronics.

Adapting electronics to perfectly conform to nonplanar and rough surfaces, such as human skin, is a challenging task, which could open up new applications in fields of high economic and scientific interest, ranging from health to robotics, human-machine interface, and Internet of Things. The key to success lies in defining a technology that can lead to ultrathin devices, exploiting ultimately thin materials, with high mechanical flexibility and excellent electrical properties. Here, we report a hybrid approach for the development of high-performance, ultrathin and conformable electronic devices, based on the integration of semiconducting transition metal dichalcogenides, i.e., MoS2, with organic gate dielectric material, i.e., polyvinyl formal (PVF) combined with inkjet printed PEDOT:PSS electrodes. Through this novel approach, transistors and simple digital and analogue circuits are fabricated by a sequential stacking of ultrathin (nanometer) layers on a few micrometers thick polyimide substrate, which guarantees the high flexibility mandatory for the targeted applications.

Intelligent control algorithms to ensure the flight safety of aerospace vehicles

An overview of the architectures, functions and algorithms of aircraft-type aerospace vehicles equipped with fly-by-wire (FBW) digital integrated control systems (ICS) is given. The ICS has a hierarchical architecture, including the primary, backup and emergency control systems with a sophisticated control reconfiguration logic in the event of failures. Digital control computers have dissimilar redundancy of equipment and software. At the atmospheric flight stage, integrator control laws provide the specified control characteristics, flight envelope protection, automatic trim of vehicle and crew workload alleviation. Further integration of manual and automatic control, implementation of the control philosophy as a continuation of the pilot's actions, more intellectual control system and human-machine interface, use of multiple control surfaces to ensure optimal vehicle configuration and alleviate structural loading together with basic control functions are considered as promising areas for ICS improvement.

Matching vs. Mismatching Signals of External Human-Machine Interface and Vehicle Kinematics: An Examination of Pedestrian Crossing Behavior and Trust, Safety, and Affective Ratings in Interactions with Differently Sized Automated Vehicles

Highly automated vehicles (HAVs) will soon be introduced into mixed urban traffic. Pedestrians might have an idea of HAVs. Nevertheless, they probably have never interacted with them before. Moreover, pedestrians will not be able to communicate with HAVs like they are used to with manual vehicles. External human–machine interfaces (eHMIs) are possible design solutions for HAVs to ensure safe interaction with other road users. Light-based eHMIs positively affected pedestrians’ trust ratings, perceived safety, and willingness to cross. However, previous studies often neglected the effect of vehicle size, although larger-sized HAVs could be potentially perceived as the more significant threat. Additionally, the relationship between vehicle kinematics and eHMIs for differently sized HAVs remains an underexplored research topic. This study investigated the effects of vehicle size (small vs. large), eHMI state (dynamic eHMI vs. static eHMI vs. no eHMI), and vehicle kinematics (yielding vs. non-yielding) on pedestrian crossing behavior and their subjective assessment. In virtual reality, we created a shared space traffic scenario, in which the eHMI and vehicle kinematics matched or did not match. For yielding conditions, the results showed that participants felt more aroused with larger HAVs than with smaller HAVs. Moreover, pedestrians initiated their crossing significantly earlier when both vehicle sizes had a dynamic eHMI compared to a static eHMI vs. no eHMI. Additionally, pedestrians evaluated a dynamic eHMI with higher trust ratings, higher perceived safety, and more positive affective reactions. The results manifested that the use of dynamic eHMIs can effectively enhance pedestrian-vehicle communication with a large and a small HAV. For non-matching conditions, the participants tended to rely on the vehicle kinematics for both vehicle sizes. Overall, the study highlighted the potential of eHMIs for pedestrian interactions with HAVs of varying sizes when they are well-coordinated with the vehicle kinematics, aiming to enhance safety and efficiency in mixed-traffic environments.