Human Interface Research Articles

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.

Pedestrians’ intent to cross in a fully autonomous vehicle environment; looking at crossing opportunity, eHMI message and wait time

Objectives Currently, pedestrians’ road-crossing decisions depend on the traffic at the crossing point, crossing opportunities, and circumstantial elements. Longer wait times on the curb and time pressure raise the number of traffic violations among pedestrians. The era of fully autonomous vehicles (FAVs) promises new interactions. External Human-Machine Interfaces (eHMIs) aim to increase the understanding of FAVs’ intentions and will influence pedestrians’ intent to cross. Yet, how pedestrian behaviors will change in the FAV era remains unclear. Further, there are no good metrics for evaluating the intricate interaction of crossing-related factors. Methods In a laboratory study, sixty participants observed crossing scenarios of a typical one-lane road with 10–12 FAVs driving along, two possible eHMI messages (“Cross!”/“Stop!”), and varying crossing opportunities (safe, risky, and unsafe). Participants had to indicate when they intended to cross the road using a designated button. Half of the participants were induced to feel time pressure. We developed index scores to convey the complexity of such crossing situations from the perspective of a pedestrian and used them to analyze the experimental data. Results In total, 48% of participants’ indications were to cross in safe crossing opportunities, 34% risky, and 18% unsafe; 69% were made when the eHMI proposition was “Cross!” and 31% for “Stop!”. High, significant, and negative correlations were found for compatible responses, e.g., with a safe crossing gap and an eHMI sign to “Cross!”. The opposite was true for incompatibility, e.g., a safe crossing gap and an eHMI sign to “Stop!” or an unsafe crossing gap and an eHMI sign to “Cross!”. The time pressure group’s median times to cross were shorter, making riskier decisions. Conclusion Our findings reflect a tendency to comply with the eHMI. In more complex scenarios where the crossing opportunity was uncertain or waiting times were longer, we observed that pedestrians were more inclined to follow the eHMI’s guidance to cross. These findings suggest that pedestrians may be more likely to follow the eHMI’s suggestion to cross than their self-skill, especially under time pressure. Our indices and metrics reflect the intricate interaction aspects of pedestrian behavior with eHMIs.

Robust gesture recognition based on attention-deep fast convolutional neural network and surface electromyographic signals.

The surface electromyographic (sEMG) signals reflect human motor intention and can be utilized for human-machine interfaces (HMI). Comparing to the sparse multi-channel (SMC) electrodes, the high-density (HD) electrodes have a large number of electrodes and compact space between electrodes, which can achieve more sEMG information and have the potential to achieve higher performance in myocontrol. However, when the HD electrodes grid shift or damage, it will affect gesture recognition and reduce recognition accuracy. To minimize the impact resulting from the electrodes shift and damage, we proposed an attention deep fast convolutional neural network (attention-DFCNN) model by utilizing the temporary and spatial characteristics of high-density surface electromyography (HD-sEMG) signals. Contrary to the previous methods, which are mostly base on sEMG temporal features, the attention-DFCNN model can improve the robustness and stability by combining the spatial and temporary features. The performance of the proposed model was compared with other classical method and deep learning methods. We used the dataset provided by The University Medical Center Göttingen. Seven able-bodied subjects and one amputee involved in this work. Each subject executed nine gestures under the electrodes shift (10 mm) and damage (6 channels). As for the electrodes shift 10 mm in four directions (inwards; onwards; upwards; downwards) on seven able-bodied subjects, without any pre-training, the average accuracy of attention-DFCNN (0.942 0.04) is significantly higher than LSDA (0.910 0.04, p < 0.01), CNN (0.920 0.05, p < 0.01), TCN (0.840 0.07, p < 0.01), LSTM (0.864 0.08, p < 0.01), attention-BiLSTM (0.852 0.07, p < 0.01), Transformer (0.903 0.07, p < 0.01) and Swin-Transformer (0.908 0.09, p < 0.01). The proposed attention-DFCNN algorithm and the way of combining the spatial and temporary features of sEMG signals can significantly improve the recognition rate when the HD electrodes grid shift or damage during wear.

EHMI on the Vehicle or on the Infrastructure? A Driving Simulator Study

Automated vehicles (AVs) may require the implementation of an external human-machine interface (eHMI) to communicate their intentions to human-driven vehicles. The optimal placement of the eHMI, either on the AV itself or as part of the road infrastructure, remains undetermined. The current driving simulator study investigated the effect of eHMI positioning on human driving behavior, during the approach and execution of right turns at T-intersections. Forty-three participants drove under three conditions: absence of eHMI, eHMI on the AV (eHMIv), and eHMI integrated into the infrastructure (eHMIi). Participants encountered AVs that either yielded or did not yield to their vehicles. The results regarding the placement of the eHMI showed that both concepts are advantageous, but for different reasons. eHMIv was appreciated because implicit and explicit communication are congruent, although the AV must first be visually identified to respond to it. eHMIi was appreciated because a familiar cue is always at a known location in the environment; as a result, participants braked earlier for the intersection and came less close to the AV (which can be interpreted as a safety advantage or efficiency disadvantage). Although there are limitations to a driving simulator study like this, this research provides important insights into the fundamental question of how information placement affects drivers’ visual attention demands and driving behavior, topics that are important in view of the development of future cities.

AI-empowered neural processing for intelligent human-machine interface and biomedical devices

AI-empowered neural processing for intelligent human-machine interface and biomedical devices Jie Gu, Associate Professor from Northwestern University, examines AI-empowered neural processing for intelligent human-machine interface and biomedical devices. Most conventional wearable devices rely on motion detection or image classifications to capture users’ activities. However, they lack the ability to decode neural signals generated by the human body. Neural signals, such as EEG, ECG, and EMG, offer a rich amount of information on a person’s physiological and psychological activities. Recognition and use of such signals present many new opportunities for applications in medical and daily commercial usage. Recently, artificial intelligence (AI) has been applied to neural signal processing, leading to a new generation of intelligent human-machine interfaces and biomedical devices.

Design analysis and development of a gantry robot for multi-layer 3-D concrete printing with simulation and experimental validation

The present study investigates the design analysis and development of a framed gantry robot (FGR) for 3-D concrete printing. Finite element simulations are performed for design validation and parameter estimation to conduct detailed analysis of the concrete printer. The kinematic and dynamic models of FGR are used for workspace analysis, motion control study, and motor sizing for printer development. The motion trajectory of the printer nozzle tip is evaluated using MATLAB simulations. An integrated programmable logic controller (PLC) with an in-built human-machine interface (HMI) through control algorithms has been implemented for interactive, user-friendly, and precise motion control. A separate control system is developed to monitor the pumping power and feed rate of mortar mix during printing. The simulated control scheme is validated experimentally using trajectory tracking for various input printing paths through HMI, with a maximum tracking error of 1 mm. Successful concrete printing trials have been conducted using various mix-design, following extensive evaluation of different printer and material parameters at the laboratory scale. The proposed design concept, mechanisms, interactive control interface, and tracking accuracy have been validated through simulation and experimental results, for ensuring quality concrete prints by the developed printer. Material testing parameters have been evaluated experimentally for modeling various mix-designs. To further evaluate printing performance, simulations of layer deformation in printed structures are conducted using finite element analysis, incorporating non-linear material models and various contact layer interactions. The simulated results of printed layers of different shapes of structures show a close resemblance with the experimental concrete printing trials.

Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System

Remote condition monitoring (RCM) aims to ensure the availability of railway assets. Previous work has indicated the importance of a user-centred RCM design approach based on cognitive principles, but there has been no known demonstration of the application of these principles. The following paper takes this theory-based approach and applies it to the design of an RCM system for the rail pantograph/Overhead Line (OHL) system. The paper first presents a high-level conceptual architecture, based on four stages of cognitive decision-making (notification, acceptance, analysis and clearance), linked to the wider monitoring architecture. Second, the paper uses cognitive principles to propose demonstration Human–Machine Interface designs for the OHL system. These HMIs were presented in an evaluation with subject matter experts. The outcomes of the process generated user-centred design recommendations for RCM. Furthermore, the evaluation suggested the importance of multiple paths through the HMI dependent on the type and urgency of fault. Finally, the outcomes of the evaluation also highlighted the importance of considering context when deploying user-centred RCM.

Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom(DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.

The gender dynamics along the human–machine interface

By deconstructing the human–machine interface across China's economic history, this commentary on Luger and Schwarze's paper highlights the critical need to explore gender dynamics in post-industrial cities through the human–machine interface. We suggest two new concepts, subjectivity and deep interface, which may offer a way forward for us to move deeper into post-industrial cities for a more intricate understanding of the enduring inequalities shaping these urban landscapes.

Sistem Otomasi Untuk Menyortir Barang Pada Ruang Produksi Menggunakan Scada dan PLC

The development of the industrial revolution is progressing. Especially on production performance or machine parts. Industrial revolution 4.0 uses the internet and digitalization. So there are lots of new innovations. In a large-scale company, of course there are many tools and machines that need to be regulated whether they are on or not with regular supervision. With regular supervision, it is impossible for human workers to check one by one, thereby wasting time and reducing production levels. Therefore, it is necessary to develop a control system using PLC and HMI (Human Machine Interface) as the brain or control center for the company. In the industrial sector, PLC (Programmable Logic Controller) is an important factor in the operation of automatic machines in factories replacing relay control systems. So that PLC as a control system in the industrial sector can move machines according to needs. One part of the industry is the production room. The production room is the part where there are manufacturing materials or goods up to packaging. This research aims to design and implement a PLC (Programmable Logic Controller) and SCADA by adding sensors and actuators as output. The method used is a rule base system, the system runs according to a predetermined sequence and flow. The results of this research are the PLC, the SHT 20 temperature and humidity sensor can produce temperature and humidity values, the capacitive photosensor can detect items with 35 trials accurately but the distance is limited to only 27cm, the proximity sensor can only detect metal objects by attaching to the sensor and The actuator in the form of a stepper motor can run according to the commands given and is well integrated into the HMI (Human Machine Interface).

Simple Siamese Model with Long Short-Term Memory for User Authentication with Field-Programmable Gate Arrays

Recent studies have focused on user authentication methods that use biometric signals such as electrocardiogram (ECG) and photo-plethysmography (PPG). These authentication technologies have advantages such as ease of acquisition, strong security, and the capability for non-aware authentication. This study addresses user authentication using electromyogram (EMG) signals, which are particularly easy to acquire, can be fabricated in a wearable form such as a wristwatch, and are readily expandable with technologies such as human–machine interface. However, despite their potential, they often exhibit lower accuracy (approximately 90%) than traditional methods such as fingerprint recognition. Accuracy can be improved using complex algorithms and multiple biometric authentication technologies; however, complex algorithms use substantial hardware resources, making their application to wearable devices difficult. In this study, a simple Siamese model with long short-term memory (LSTM) (SSiamese-LSTM) is proposed to achieve a high accuracy of over 99% with limited resources suitable for wearable devices. The hardware implementation was accomplished using field-programmable gate arrays (FPGAs). In terms of accuracy, EMG measurement results from Chosun University were used, and data from 100 individuals were employed for verification. The proposed digital logic will be integrated with an EMG sensor in the form of a watch that can be used for user authentication.

Scoring the Ethics of AI Robo-Advice: Why We Need Gateways and Ratings

AbstractUnlike the many services already transformed by artificial intelligence (AI), the financial advice sector remains committed to a human interface. That is surprising as an AI-powered financial advisor (a robo-advisor) can offer personalised financial advice at much lower cost than traditional human advice. This is particularly important for those who need but cannot afford or access traditional financial advice. Robo-advice is easily accessible, available on-demand, and pools all relevant information in finding and implementing an optimal financial plan. In a perfectly competitive market for financial advice, robo-advice should prevail. Unfortunately, this market is imperfect with asymmetric information causing generalised advice aversion with a disproportionate lack of trust in robo-advice. Initial distrust makes advice clients reluctant to use, or switch to, robo-advice. This paper investigates the ethical concerns specific to robo-advice underpinning this lack of trust. We propose a regulatory framework addressing these concerns to ensure robo-advice can be an ethical resource for good, resolving the increasing complexity of financial decision-making. Fit for purpose regulation augments initial trust in robo-advice and supports advice clients in discriminating between high-trust and low-trust robo-advisors. Aspiring robo-advisors need to clear four licensing gateways to qualify for an AI Robo-Advice License (AIRAL). Licensed robo-advisors should then be monitored for ethical compliance. Using a balanced score card for ethical performance generates an ethics rating. This gateways-and-ratings methodology builds trust in the robo-advisory market through improved transparency, reduced information asymmetry, and lower risk of adverse selection.