Junpei 'Joni' Zhong

University of Wollongong Hong Kong

Movements play a critical role in robotic systems, with considerations varying across different robotic systems regarding factors, such as accuracy, speed, energy consumption, and naturalness of movements in various parts of the robotic mechanics. Over the past decades, the robotics community has developed computationally efficient mathematical too...

Objective: How abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD sympt...

With the rapid development and wide proliferation of sensor devices and the Internet of Things (IoT), machine learning algorithms processing and analysing one or more modalities of sensory signals have become an active research field given its numerous applications, particularly in the domestic intelligent environment (DIE). In the past decades, th...

This study aimed to investigate the relations of emotion, relaxation and embodied experiences by providing the Virtual Reality (VR)-based nature. 31 participants without a clinical diagnosis of mental illness were invited to attend VR sessions to view natural sceneries under different levels of controlling the VR equipment. Participants’ mood state...

Robotic imitation faces challenges due to the lack of nuanced movements when employing keyframe methods, which can potentially lead to the uncanny valley effect due to constraints in fitting data within motor speed capacities. This research proposes a keyframeless motion-transferring method for robotic imitation using motion capture data. Initially...

Deaf-mutes face many difficulties in daily interactions with hearing people through spoken language. Sign language is an important way of expression and communication for deaf-mutes. Therefore, breaking the communication barrier between the deaf-mute and hearing communities is significant for facilitating their integration into society. To help the...

In ambient-assisted living facilitated by smart home systems, the recognition of daily human activities is of great importance. It aims to infer the household’s daily activities from the triggered sensor observation sequences with varying time intervals among successive readouts. This paper introduces a novel deep learning framework based on embedd...

The pyramidal predictive network (PPNV1) proposes an interesting temporal pyramid architecture and yields promising results on the task of future video-frame prediction. We expose and analyze its signal dissemination and characteristic artifacts, and propose corresponding improvements in model architecture and training strategies to address them. A...

We are introducing a multi-scale predictive model for video prediction here, whose design is inspired by the "Predictive Coding" theories and "Coarse to Fine" approach. As a predictive coding model, it is updated by a combination of bottom-up and top-down information flows, which is different from traditional bottom-up training style. Its advantage...

Bio-controllers inspired by the characteristics of the human lower limb play an important role in the study of lower limb rehabilitation robots (LLRRs). However, the inverse dynamics modeling of robots for human lower limb rehabilitation remains a challenging issue due to the non-linear and strong coupling characteristics of the bio-controller. To...

Recently, deep learning methods have achieved considerable performance in gesture recognition using surface electromyography signals. However, improving the recognition accuracy in multi-subject gesture recognition remains a challenging problem. In this study, we aimed to improve recognition performance by adding subject-specific prior knowledge to...

Background: Abnormal global brain signaling, which is associated with impaired neural connectivity, is evident in people with autism spectrum disorder (ASD), yet its association with impeded cognitive processes underlying social information processing and ASD symptomatology remained elusive. Methods: This study aimed to investigate the local and gl...

Visual-frame prediction is a pixel-dense prediction task that infers future frames from past frames. A lack of appearance details, low prediction accuracy and a high computational overhead are still major problems associated with current models or methods. In this paper, we propose a novel neural network model inspired by the well-known predictive...

Gesture can be used as an important way for human–robot interaction, since it is able to give accurate and intuitive instructions to the robots. Various sensors can be used to capture gestures. We apply three different sensors that can provide different modalities in recognizing human gestures. Such data also owns its own statistical properties for...

Inspired by the well-known predictive coding theory in cognitive science, we propose a novel neural network model for the task of visual-frame prediction. In this paper, our main work is to combine the theoretical framework of predictive coding and deep learning architectures, to design an efficient predictive network model for visual-frame predict...

In today’s highly competitive market, a large number of high-tech products have been used by the athletesin various academic competitions. At present, a large number of athletes have used their own rigorous theories to predict the effects of modern games. In this study, the fuzzy theory calculation method based on information security is used to in...

Thermal imaging has recently come to light to measure high human body temperature (fever) in responses to the global public health issues. This is normally achieved by very expensive high-resolution thermal cameras. Lately, there has been a new commercial low-resolution Thermal Sensor Array (TSA) that have gained growing interest in indoor human mo...

Human-centric applications of a single Thermal Sensor Array (TSA) have performed extremely well in many areas. However, most of these works have not yet reached the real applicability stage of the Internet of Things (IoT) applications. The main limitation of deploying such systems on a large scale is the challenge of fusing multiple TSAs to cover a...

The smart home is one application of intelligent environments, where sensors are equipped to detect the status inside the domestic home. With the development of sensing technologies, more signals can be obtained with heterogenous statistical properties with faster processing speed. To make good use of the technical advantages, data-driven methods a...

Localizing a smart capsule within the gastrointestinal (GI) tract is essential for high performance, accurate sensing as well as efficacious drug delivery at designated locations. In this work, we describe a data-driven framework that employs a near infrared (NIR) tracking scheme to achieve the localization of smart capsule in GI tract. A prototype...

To support the independent living of older adults in their own homes, it is essential to identify their abnormal behaviors before triggering an automated alert system. Existing normal vision sensing approaches to detect human falls in the activities of daily living (ADL) experienced acceptability issues due to outstanding privacy concerns when they...

Inspired by the neurons’ differences in membrane time-scales, the multiple timescale recurrent neural network model (MTRNN) adopts the hierarchical architecture with increasing time-scales from bottom to top layers. Based on this idea, the recent adaptive and continuous time recurrent neural networks (ACTRNN) and the gated adaptive continuous time...

In daily life, people use their hands in various ways for most daily activities. There are many applications based on the position, direction, and joints of the hand, including gesture recognition, gesture prediction, robotics and so on. This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap M...

Physical distancing measurements are accepted to be critical strategies to slow the spread of COVID-19 disease. Most of the recent works to measure the physical distancing between people aims for easy-to-deploy systems such as contact tracing applications to speed up the deployment process. However, these systems, which rely heavily on the Bluetoot...

Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance betwee...

The pursuit of higher levels of autonomy and versatility in robotics is arguably led by two main factors. Firstly, as we push robots out of the labs and productions lines, it becomes increasingly challenging to design for all possible scenarios that a particular robot might encounter. Secondly, the cost of designing, manufacturing, and maintaining...

Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques...

Automatic robot activity understanding plays an important role in human–computer interaction (HCI), especially in smart home service robots. Existing manipulator control methods, such as position control, vision-based control method, fail to meet the requirements of autonomous learning. Reinforcement learning can cope with the interaction of robot...

Thermal sensor array (TSA) offers privacy-preserving, low-cost, and non-invasive features, which makes it suitable for various indoor applications such as anomaly detection, health monitoring, home security, and monitoring energy efficiency applications. Previous approaches to human-centred applications using the TSA usually relied on the use of a...

Modern technology has been improving, as is medical technology. Over the years, rehabilitation medicine is developing and growing. The use of rehabilitation robots to achieve the upper limb motor function of patients with hemiplegia has also become a popular research in academia. Under this background, this paper proposes an upper limb robot rehabi...

Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental learning framework based on a teleoperation strategy that can enable the robot to reproduce the demon...

There is a growing demand for efficient and privacy-preserving intelligent solutions in a multi-occupancy environment. This paper proposes a non-contact scheme for occupancy estimation using an infrared thermal sensor array, which has the advantages of low-cost, low-power, and high-performance capabilities. The proposed scheme offers an accurate hu...

The recognition of human activities of daily living has gained increasing attention in recent years due to its potential to promote autonomy for elderly people in their own homes and its usage for security surveillance in scenarios such as supermarkets, banks, etc. Infrared thermal array has been proposed as a non-invasive device for human activity...

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a...

The disentanglement of different objective properties from the external world is the foundation of language development for agents. The basic target of this process is to summarise the common natural properties and then to name it to describe those properties in the future. To realise this purpose, a new learning model is introduced for the disenta...

Learning a task such as pushing something, where the constraints of both position and force have to be satisfied, is usually difficult for a collaborative robot. In this work, we propose a multimodal teaching-by-demonstration system which can enable the robot to perform this kind of tasks. The basic idea is to transfer the adaptation of multi-modal...

A self-tuning PID control strategy using a reinforcement learning method, called CACLA (Continuous Actor-critic Learning Automata) is proposed in this paper with the example application of human-in-the-loop physical assistive control. An advantage of using reinforcement learning is that it can be done in an online manner. Moreover, since human is a...

Impedance control has been widely used in robotic applications where a robot has physical interaction with its environment. However, how the impedance parameters are adapted according to the context of a task is still an open problem. In this paper, we focus on a physical training scenario, where the robot needs to adjust its impedance parameters a...

The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with differ...

Studies suggest that, within the hierarchical architecture, the topological higher level possibly represents the scenarios of the current sensory events with slower changing activities. They attempt to predict the neural activities on the lower level by relaying the predicted information after the scenario of the sensorimotor event has been determi...

Learning from demonstration, as an important component of imitation learning, is a paradigm for robot to learn new tasks. Considering the application of learning from demonstration in the navigation issue, the robot can also acquire the navigation task via the human teacher’s demonstration. Based on research of the human brain neocortex, in this ar...

Studies suggest that within the hierarchical architecture , the topological higher level possibly represents a conscious category of the current sensory events with a slower changing activities. They attempt to predict the activities on the lower level by relaying the predicted information. On the other hand, the incoming sensory information correc...

Studies suggest that within the hierarchical architecture, the topological higher level possibly represents a conscious category of the current sensory events with slower changing activities. They attempt to predict the activities on the lower level by relaying the predicted information. On the other hand, the incoming sensory information corrects...

The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action. We propose a neural architecture in which such regularities of active inference are encoded hierarchically. We further suggest that this encoding emerges during the embodied le...

Skill learning autonomously through interactions with the environment is a crucial ability for intelligent robot. A perception-action integration or sensorimotor cycle, as an important issue in imitation learning, is a natural mechanism without the complex program process. Recently, neurocomputing model and developmental intelligence method are con...

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-a...

Sentiment classification for product reviews is of great significance for business feedback for manufactures, sellers and users. However, since a large amount of training data for a specific product domain is not always available, transfer learning is often utilized to do sentiment analysis applications. Specifically, after a pre-training of the la...

In this paper, a dual arm control method in a tele-operation system is introduced. For a bimanual robot, moving a common object precisely requires real-time cooperation between the two arms. In such conditions the force interaction including the internal forces applied on the object must be taken into account. Therefore the dynamics models for mast...

ion tasks are challenging for multi- modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time traini...

Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours. These regulation can be depicted in the Bayesian framework, that is why cognitive agents are able to gen...