Role In Human-machine Interaction Research Articles

Bioinspired ion channel receptor based on hygroelectricity for precontact sensing of living organism

Tactile sensors play an important role in human-machine interaction (HMI). Compared to contact tactile sensing, which leaves physical hardware vulnerable to wear and tear, proximity sensing is better at reacting to remote events before physical contact. The apteronotus albifrons possess ion channel receptors for remote surroundings perception. Inspired by the relevant ion channel structure and self-powered operation mode, we designed a new proximity sensor with ion rectification characteristics and self-powered capability. This bio-inspired ion channel receptor exploits the hygroelectric effect to convert the humidity information into a series of current signals when the living organism approaches, and it is insensitive to non-aquatic non-organisms. The sensor offers high sensitivity (2.3 mm−1), a suitable range (0–10 mm) for close object detection, fast response (0.3 s), and fast recovery (2.5 s). The unique combination of bio-sensitivity, non-contact detection characteristics, and humidity-based power generation capabilities enriches the functionality of future HMI electronics. As a proof of concept, the sensor has been successfully applied in different scenarios such as human health management, early warning systems, non-contact switches to prevent virus transmission, object recognition, and finger trajectory detection.

Advancements in Indian Sign Language Recognition Systems: Enhancing Communication and Accessibility for the Deaf and Hearing Impaired

Sign language is an ocular-mobile language utilized by deaf or hearing-impaired individuals. It conveys a combination of handshapes, motions, facial expressions, and body postures. Sign language is an essential form of communication with its unique grammar and syntax. The communication barrier between those who use sign language and those who do not is substantial. India has approximately 63 million people in the deaf or hearing-impaired community (DHH). Therefore, research within the realm of sign language recognition (SLR) shows great potential in enhancing the quality of life for individuals facing hearing disabilities and promoting better communication and integration into society. In recent years, sign language recognition has attracted considerable attention due to its significant role in human-machine interaction, accessibility, real-time interpretation, educational tools, and communication aids. This study reviews the most current developments in Indian Sign Language Recognition Systems (ISLRS). It discusses the commonly used algorithms, standard datasets, and performance characteristics of these systems in detail. Lastly, it highlights the challenges and future perspectives of these emerging technologies.

Machine learning and human‐machine trust in healthcare: A systematic survey

AbstractAs human‐machine interaction (HMI) in healthcare continues to evolve, the issue of trust in HMI in healthcare has been raised and explored. It is critical for the development and safety of healthcare that humans have proper trust in medical machines. Intelligent machines that have applied machine learning (ML) technologies continue to penetrate deeper into the medical environment, which also places higher demands on intelligent healthcare. In order to make machines play a role in HMI in healthcare more effectively and make human‐machine cooperation more harmonious, the authors need to build good human‐machine trust (HMT) in healthcare. This article provides a systematic overview of the prominent research on ML and HMT in healthcare. In addition, this study explores and analyses ML and three important factors that influence HMT in healthcare, and then proposes a HMT model in healthcare. Finally, general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Speech Emotion Recognition (SER) plays a predominant role in human-machine interaction. SER is a challenging task because of number of complexities involved in it. For an accurate emotion classification system, feature extraction is the first and important step carried out on speech signals. And after the features are extracted, it is very important to select the best features out of all and reject the redundant and least important features. Feature selection methods play an important role in SER performance. The classifier gets the selected features, so as to reduce the unnecessary overload and perform better to classify the emotions. In this study, a good combination of features is selected from Punjabi Emotional Speech Database. Then a number of feature selection algorithms are explored and experimented upon, to select the best features. 1D-CNN is used for classification purpose. The results are shown and compared on the basis of number of performance metrics. LASSO has shown the best performance results as compared to other feature selection methods.

Using microneedle array electrodes for non-invasive electrophysiological signal acquisition and sensory feedback evoking.

Introduction: Bidirectional transmission of information is needed to realize a closed-loop human-machine interaction (HMI), where electrophysiological signals are recorded for man-machine control and electrical stimulations are used for machine-man feedback. As a neural interface (NI) connecting man and machine, electrodes play an important role in HMI and their characteristics are critical for information transmission. Methods: In this work, we fabricated a kind of microneedle array electrodes (MAEs) by using a magnetization-induced self-assembly method, where microneedles with a length of 500-600μm and a tip diameter of ∼20μm were constructed on flexible substrates. Part of the needle length could penetrate through the subjects' stratum corneum and reach the epidermis, but not touch the dermis, establishing a safe and direct communication pathway between external electrical circuit and internal peripheral nervous system. Results: The MAEs showed significantly lower and more stable electrode-skin interface impedance than the metal-based flat array electrodes (FAEs) in various testing scenarios, demonstrating their promising impedance characteristics. With the stable microneedle structure, MAEs exhibited an average SNR of EMG that is more than 30% higher than FAEs, and a motion-intention classification accuracy that is 10% higher than FAEs. The successful sensation evoking demonstrated the feasibility of the MAE-based electrical stimulation for sensory feedback, where a variety of natural and intuitive feelings were generated in the subjects and thereafter objectively verified through EEG analysis. Discussion: This work confirms the application potential of MAEs working as an effective NI, in both electrophysiological recording and electrical stimulation, which may provide a technique support for the development of HMI.

Common ground, cooperation, and recipient design in human-computer interactions

In recent years, the number of human-machine interactions has increased considerably. Additionally, we have evidence of linguistic differences between human-machine interactions and human–human conversations (e.g., Timpe-Laughlin et al., 2022). Therefore, it is reasonable to revisit theoretical frameworks that conceptualize interactional language use and investigate to what extent they still apply to technology-mediated interactions. As a first attempt at exploring whether pragmatics theories apply to human-machine interaction, we examined how well Kecskés's (2013) socio-cognitive approach (SCA) focusing on asymmetric interactions (e.g., between interlocutors of different language backgrounds) applies to the asymmetry of human-machine interactions.Using examples from experimental data, we present the nature of common ground between human and machine (spoken dialogue system) interlocutors, focusing on the construction of and reliance on the emergent side of common ground that is informed by the actual situational experience. Like Kecskés, we argue that both egocentrism and cooperation play a role in human-machine interaction. While the former is manifested in approaching the machine interlocutor as if it was human, the latter appears to play a role in common ground seeking and building as well as in recipient design. We demonstrate that Kecskés's SCA is a fitting framework for analyzing human-machine communication contexts.

Restaurant Chatbot Using IBM Watson

Restaurant chatbot is an automated system for carrying out day to day works in a restaurant such as ordering, reservations, FAQs at reception, and many more tasks which are not that much important but take much time of receptionist while working. Chatbot is a system backed by an Artificial Intelligence technology to learn, adapt and work according to surrounding environment. In this particular system, development of a chatbot is done with the help of API provided by IBM named as WATSON to create, train, and deploy AI and ML models for various purposes to cut down the too much efforts done while developing an AI/ML model. System helps out staff at restaurant to carry out above stated tasks with less to no efforts. Then, staff can give attention to other important tasks related to their routine. Many restaurants and similar organizations offer services like webpages for reservations, helplines with the help of call centers. But these systems are not much efficient and doesn’t answer much of the questions of customer. Chat bots form a solid surface for this situation, answers much of the questions of customers, carry out some operations available online like collecting online payments, reserving rooms, answering different questions like address of an organization, facilities provided by them, vacant rooms available or not and many more. For the development of a restaurant chatbot as a last year university project on experimental basis, adding large facilities is avoided. Data sets for training chatbot model is collected from a leading website KAGGLE, which provides various data sets for models. A chatbot is a conversation agent where a computer program is made to simulate an intelligent conversation. It can use user inputs in several formats such as text, voice. For such different open-source platforms can be found. A Chatbot plays an important role in human-machine interaction. A Chatbot has three modules: the user interface, an interpreter, and a knowledge base. It is a program that tries to simulate the typed text in such a manner that human feels like it's talking to another human, not a machine. There are many chatbots that are on the internet for different purposelike education, customer service, entertainment.

Prediction of state anxiety by machine learning applied to photoplethysmography data.

BackgroundAs the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated.MethodsThe machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test.ResultsA leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

Research on compatibility in man-machine interface design of products

As a platform for information exchange between people and products, human-machine interface plays a more and more important role in human machine interaction. The compatibility of display and manipulation in human machine interface determines the ease of use to a large extent. Through the case study of HMI design for different products, this paper analyzes the spatial compatibility, the movement compatibility and the common measures to improve compatibility. Spatial compatibility refers to the correspondence of stimulus and response elements in physical layout on a display panel. Motion compatibility refers to the degree of correspondence between the motion effects generated by a controller and people’s psychological expectations. It is pointed out that the common measures to improve the compatibility of human-machine interface are: shape coding, position coding, color coding, symbol coding, and taking into account human physiological characteristics and usage habits.

Interactive Hand Pose Estimation: Boosting accuracy in localizing extended finger joints

Accurate 3D hand pose estimation plays an important role in Human Machine Interaction (HMI). In the reality of HMI, joints in fingers stretching out, especially corresponding fingertips, are much more important than other joints. We propose a novel method to refine stretching-out finger joint locations after obtaining rough hand pose estimation. It first detects which fingers are stretching out, then neighbor pixels of certain joint vote for its new location based on random forests. The algorithm is tested on two public datasets: MSRA15 and ICVL. After the refinement stage of stretching-out fingers, errors of predicted HMI finger joint locations are significantly reduced. Mean error of all fingertips reduces around 5mm (relatively more than 20%). Stretching-out fingertip locations are even more precise, which in MSRA15 reduces 10.51mm (relatively 41.4%).

Modality-specific and hierarchical feature learning for RGB-D hand-held object recognition

Hand-held object recognition is an important research topic in image understanding and plays an essential role in human-machine interaction. With the easily available RGB-D devices, the depth information greatly promotes the performance of object segmentation and provides additional channel information. While how to extract a representative and discriminating feature from object region and efficiently take advantage of the depth information plays an important role in improving hand-held object recognition accuracy and eventual human-machine interaction experience. In this paper, we focus on a special but important area called RGB-D hand-held object recognition and propose a hierarchical feature learning framework for this task. First, our framework learns modality-specific features from RGB and depth images using CNN architectures with different network depth and learning strategies. Secondly a high-level feature learning network is implemented for a comprehensive feature representation. Different with previous works on feature learning and representation, the hierarchical learning method can sufficiently dig out the characteristics of different modal information and efficiently fuse them in a unified framework. The experimental results on HOD dataset illustrate the effectiveness of our proposed method.

Locally regularized sliced inverse regression based 3D hand gesture recognition on a dance robot

Gesture recognition plays an important role in human machine interactions (HMIs) for multimedia entertainment. In this paper, we present a dimension reduction based approach for dynamic real-time hand gesture recognition. The hand gestures are recorded as acceleration signals by using a handheld with a 3-axis accelerometer sensor installed, and represented by discrete cosine transform (DCT) coefficients. To recognize different hand gestures, we develop a new dimension reduction method, locally regularized sliced inverse regression (LR-SIR), to find an effective low dimensional subspace, in which different hand gestures are well separable, following which recognition can be performed by using simple and efficient classifiers, e.g., nearest mean, k-nearest-neighbor rule and support vector machine. LR-SIR is built upon the well-known sliced inverse regression (SIR), but overcomes its limitation that it ignores the local geometry of the data distribution. Besides, LR-SIR can be effectively and efficiently solved by eigen-decomposition. Finally, we apply the LR-SIR based gesture recognition to control our recently developed dance robot for multimedia entertainment. Thorough empirical studies on ‘digits’-gesture recognition suggest the effectiveness of the new gesture recognition scheme for HMI.