As robots become more prevalent in society and applied in various workplace sectors, individuals must have an appropriate amount of trust that aligns with robots' or automated systems' actual capabilities, facilitating optimal and safe human-robot interaction. Appropriately calibrated trust levels can enhance robots' safe and successful adoption into our society and their unique applied environments. The current research aims to assess individuals' self-reported trust levels in a triadic human-robot-human interaction concerning a collaborative haptically enabled “sonography” style robot (having tele-operative capabilities) to assess moderators of trust unique to this specific domain. The objectives of the current research are to identify participants' trust levels in a triadic interaction focusing on the operator's proxemic location while operating the robot (1) and to compare self-reported trust levels across conditions suggested by the literature to have an influence (2). A repeated measures ANDVA revealed a significant association between the replicated traditional sonography assessment and participants possessing higher trust levels than all robot-related conditions. Further, participants had greater trust for the smooth and slow-functioning robot than the non-smooth functioning robot. Lastly, the current study's findings suggest that, compared to the other robot-related conditions, the experimenter's location operating the tele-operative robot does not significantly influence participants' trust levels. Future research should consider exploring humans' qualitative perceptions of their interactions with sonography robots and whether trust can be more accurately calibrated over time. Doing so may assist in developing an in-depth understanding of the discrepancies between human-human interaction and human-robot interactions unique to this setting.

Human Activity Recognition (HAR) is one of the essential building blocks of so many applications like security, monitoring, the internet of things and human-robot interaction. The research community has developed various methodologies to detect human activity based on various input types. However, most of the research in the field has been focused on applications other than human-in-the-centre applications. This paper focused on optimising the input signals to maximise the HAR performance from wearable sensors. A model based on Convolutional Neural Networks (CNN) has been proposed and trained on different signal combinations of three Inertial Measurement Units (IMU) that exhibit the movements of the dominant hand, leg and chest of the subject. The results demonstrate k-fold cross-validation accuracy between 99.77 and 99.98% for signals with the modality of 12 or higher. The performance of lower dimension signals, except signals containing information from both chest and ankle, was far inferior, showing between 73 and 85% accuracy.

Driving simulators are utilized for many applications including basic driver training, human factor studies, human-machine interaction, and vehicle prototyping in automobile industries. The main purpose of using driving simulator is to provide realistic driving experience. Since simulator platforms have physical limitations, Motion Cueing Algorithms (MCAs) are used to generate driving sensation for the simulator user while considering the simulator's physical and dynamical constraints. When using a model predictive control (MPC)-based MCA, the principle of MPC is leveraged to predict an optimized future behavior of the simulator where a series of control actions is developed across a defined future horizon using the explicitly specified process model. Corresponding to the pre-positioning or time-varying reference MPC, it is crucial to predict the future vehicle motion signals for the simulator accurately. The existing methods for predicting vehicle motion signals do not excel in predicting time-series of a long sequence due to the missing feedback loop or limited memory size. To address this issue, the Long Short-Term Memory (LSTM) model is developed to predict motion signals using Python. The performance of LSTM is compared with those from different traditional methods using several measurements criteria, which include the root mean squared error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (r). The results indicate that LSTM outperforms RNN by producing more accurate motion allowing the MCA to deliver realistic motion sensations, the LSTM model can be employed in a wide range of applications including autonomous vehicles trajectory prediction, and other prediction problems.

High-G Centrifuge Systems (HCSs) are valuable tools for training aircrews and research on aviation medicine. Providing a safe and controlled environment, they are an enabler for protecting aircrews and air assets. Despite their vast applications, development of a human-rated HCS is a costly and challenging engineering project. One of the most critical steps in the development of HCSs is the experimental validation. This step has not received enough attention within the published research. This study reports evaluation and validation of an operational HCS located in the Institute for Intelligent Systems Research and Innovation (IISRI) at Deakin University, Australia. The system, owning a low-cost structure with an effective arm length of over 5m, is capable of generating a maximum sustained acceleration of 9G with an onset rate of 5G/sec. The experimental validation of system is implemented using an Advanced Wireless Human Dummy (AWHD) which is fully instrumented. The results of experimental validation show that the system can reliably generate the reference centripetal acceleration with lowest error at the human spine location.

Human walking is a complex activity with a high level of cooperation and interaction between different systems in the body. Accurate detection of the phases of the gait in real-time is crucial to control lower-limb assistive devices like exoskeletons and prostheses. There are several ways to detect the walking gait phase, ranging from cameras and depth sensors to the sensors attached to the device itself or the human body. Electromyography (EMG) is one of the input methods that has captured lots of attention due to its precision and time delay between neuromuscular activity and muscle movement. This study proposes a few Machine Learning (ML) based models on lower-limb EMG data for human walking. The proposed models are based on Gaussian Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis (LDA) and Deep Convolutional Neural Networks (DCNN). The traditional ML models are trained on hand-crafted features or their reduced components using Principal Component Analysis (PCA). On the contrary, the DCNN model utilises convolutional layers to extract features from raw data. The results show up to 75% average accuracy for traditional ML models and 79% for Deep Learning (DL) model. The highest achieved accuracy in 50 trials of the training DL model is 89.5%.

Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control wearable assistive devices, yet there has not been enough research facilitating its implementation. In this study, we propose several models to predict four activities from inertial sensors located in the ankle area of a lower-leg assistive device user. This choice is because they do not need to be attached to the user’s skin and can be directly implemented inside the control unit of the device. The proposed models are based on Artificial Neural Networks and could achieve up to 92.8% average classification accuracy.

The autonomous vehicle (AV) has been studied by many researchers recently because of its valuable points in transportation, aviation, military, smart city, and aerospace. The model predictive control (MPC) is employed to track the artificial intelligent regenerated motion signals with higher accuracy than other error-and model-based controllers as it can consider the constraints of the system in extracting the optimal solution. However, the accuracy and applicability of the MPC rely on the MPC horizons, including prediction and control horizons. The higher prediction horizons mean a higher computational load of the system, which reduces the real-time applicability of the system. On the other hand, a higher prediction horizon increases the system’s stability in facing abrupt motion signals. In addition, higher control horizons mean more dexterity in the system facing an unknown situation. On the other hand, a longer control horizon increases the computational load of the system exponentially. This study employs particle swarm optimisation (PSO) to extract the optimal MPC horizons considering the accuracy and computational load. The cost function is defined to increase the accuracy of the longitudinal time-varying velocity tracking, decrease the lateral deviation, decrease the relative yaw angle and decrease the computational load of the system. It should be noted that the lateral deviation and relative yaw angle are extracted using the vehicle four wheels dynamic model in order to evaluate the AVs’ passenger motion comfort. The proposed method is designed and developed under MATLAB/Simulink. The extracted optimal MPC horizon is compared with some other arrangements of the MPC horizons to prove the efficiency of the proposed method compared with the trial-and-error method.

The conflicts between the perceived sensation of the different sensory systems can cause adverse effects which is known as motion sickness (MS) and the side effects of MS include nausea, dizziness, stomach awareness etc. Virtual reality sickness (also called Cybersickness or visually induced motion sickness (VIMS)) happens during exposure to a virtual environment when senses transfer conflicting sensation signals to the brain. The symptoms of Cybersickness are similar to motion sickness symptoms. The adverse effects of this common phenomenon can negatively affect the training outcome and benefits using VR, undermine users’ health and usefulness of simulators as it involves health risk and contributes to the increase of dropout rates. Therefore, to mitigate these issues, MS should be detected and measured. The primary objective of this study is to subjectively and objectively detect and quantify cybersickness level using a helicopter simulator. This study has also investigated the change in cybersickness self-reported scores in different weather conditions such as clear and stormy. Simulator sickness questionnaire (SSQ) has been employed for subjective scoring. This research also aimed to correlate SSQ scores with physiological data such as Galvanic Skin Response (GSR). The findings demonstrated that the SSQ total score (TS) has increased significantly from clear weather to stormy for the participants. There is also a positive correlation found between the change in TS and the amount of GSR but not significant.

People perform most of their activities while being in an upright position. Nonetheless, there are some circumstances where they are required to adapt to different positions other than the upright position as in air manoeuvres and sport gymnastics. In these unexpected scenarios, the physiological signals are likely to change which can affect their performance. While some studies investigated the correlation between physiological signals and different body positions, to our best knowledge, these studies were limited to a rotating chair (1 or 2 degree of freedoms). Here, we investigated and evaluated human physiological responses (such as pupil diameter, skin temperature, heart rate, and breathing rate) to different seated positions including seated supine, seated side, seated inverted, and seated upright using Universal Motion Simulator (UMS), a 6 degree of freedom simulator with the most realistic acceleration and motion sensation. Open loop acrobatic flight motion sensation for the 11 participants were created and accompanied with a series of pre- and post-questionnaires to subjectively assess the physical wellbeing of each participant. The results of the study based on the objective assessment of collected physiological data showed that the mean heart rate decreases during an inverted position (82 beats per minutes bpm) and increased by an average of 7 beats per minute in an upright position 89.5 bpm. Moreover, the mean breathing rate in an upright position was 18.3 respirations per minutes (rpm) which is higher than mean breathing rate in the side position 19.8 rpm. Furthermore, it was found that the mean pupil diameter (PD) in an upright position was 4.33 mm which is higher compared to other positions. Independent from the motion scenarios and body positions, the Skin Temperature kept increasing which might be because of excitement and other emotional factors.

Human Activity Recognition (HAR) is a crucial technology for many applications such as smart homes, surveillance, human assistance and health care. This technology utilises pattern recognition and can contribute to the development of human-in-the-loop control of different systems such as orthoses and exoskeletons. The majority of reported studies use a small dataset collected from an experiment for a specific purpose. The downsides of this approach include: 1) it is hard to generalise the outcome to different people with different biomechanical characteristics and health conditions, and 2) it cannot be implemented in applications other than the original experiment. To address these deficiencies, the current study investigates using a publicly available dataset collected for pathology diagnosis purposes to train Machine Learning (ML) algorithms. A dataset containing knee motion of participants performing different exercises has been used to classify human activity. The algorithms used in this study are Gaussian Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors Vote, Support Vector Machine and Gradient Boosting. Furthermore, two training approaches are compared to raw data (de-noised) and manually extracted features. The results show up to 0.94 performance of the Area Under the ROC Curve (AUC) metric for 11-fold cross-validation for Gradient Boosting algorithm using raw data. This outcome reflects the validity and potential use of the proposed approach for this type of dataset.

The usage of autonomous vehicles in the transportation sector can achieve the objective of a safe environment. To increase riding comfort in an autonomous vehicle, one main challenge is to implement motion scenarios according to the passenger’s driving behaviours. This leads to customization of the driving style of an autonomous vehicle according to the preference of its passenger. The main disadvantage of the current autonomous vehicles is the regeneration of driving motion signals without taking into consideration the comfort/discomfort of the passengers according to their driving behaviours and preferred driving styles such as acceleration/deceleration rate and steering styles. In this paper, a nonlinear autoregressive network model is developed and trained based on the generated motion scenarios of the passenger and the position of the autonomous vehicle, in order to predict and replicate the motion signals based on the passenger’s driving behaviours. The MATLAB toolbox is used to train the network and forecast the motion signals. The results show the usefulness of the proposed method in terms of a higher shape similarity level and a lower mean square error rate between the actual and forecasted motion signals. These regenerated motion signals can increase the riding comfort of autonomous vehicle’s passengers as it is able to imitate the behaviour of the passengers.

Assistive wearable exoskeleton can help to restore respective muscle functions of people with disabilities and provide support to workers within strenuous activities. The design of a practical exoskeleton should be user-friendly, compact, lightweight without any obtrusive effect on the user, which justifies the execution of optimisation steps for the design of the links and configuration of the system. Most exoskeletons struggle to accommodate a slight variation in functionality from their original design. This work addresses the design and development of a light-weight semi-active elbow exoskeleton. The main objectives consist of the development of a semi-active actuation mechanism with an adaptive exoskeleton frame and harness to accommodate varying user anthropometrics. The effectiveness of the proposed exoskeleton design has been evaluated through simulation. Furthermore, the system has been implemented in order to determine the physical feasibility and practicality through rapid fabrication prototyping. The outcomes of the study show that the exoskeleton can adjust to varying anthropometrics and assist elbow flexion, reducing stress and fatigue on the human elbow joint and muscles induced by gravity during flexion.

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.

Uncertainty quantification (UQ) from similar events brings transparency. However, the presence of an irrelevant event may degrade the performance of similarity-based algorithms. This paper presents a UQ technique from similarity and sensitivity. A traditional neural network (NN) for the point prediction is trained at first to obtain the sensitivity of different input parameters at different points. The relative range of each input parameter is set based on sensitivity. When the sensitivity of one parameter is very high, a small deviation in that parameter may result in a large deviation in output. While selecting similar events, we allow a small deviation in highly sensitive parameters and a large deviation in less sensitive parameters. Uncertainty bounds are computed based on similar events. Similar events contain exact matches and slightly different samples. Therefore, we train a NN for bound correction. The bound-corrected uncertainty bounds (UB) provide a fair and domain-independent uncertainty bound. Finally, we train NNs to compute UB directly. The end-user need to run the final NN to obtain UB, instead of following the entire process. The code of the proposed method is also uploaded to Github. Also, users need to run only the fifth script to train a NN of a different UB.

End to end learning for autonomous navigation and driving has become a growing research trend in both industry and academia in recent years. Its promise is in treating the whole driving pipeline as the development of a deep neural network (DNN). Its Achilles' heel is access to thousands of images required for training of the DNN. This paper comprehensively investigates the applicability of the deep transfer learning for the specific task of end to end learning of autonomous navigation. Five state of the art DNNs including ResNet, AlexNet, and Densenet are applied here for extracting features from images taken by the front-facing camera of a mobile robot. Extracted features have different information values as DNNs have different architectures and learning capabilities. These features are then processed by a multilayer fully connected neural network to estimate the robot angular velocity. Obtained results for different DNNs indicate that the transfer learning-based models show a promising performance for accurately estimating the angular velocity purely using visual information. According to obtained results, AlexNet-base model outperforms others in terms of the estimation accuracy and the performance consistency.

Communication delays are known to create stability and performance issues in multilateral teleoperation systems. Multilateral teleoperation configurations usually include more than two communication channels, which can become problematic for robot control when limitations in network bandwidth results in delays and uncertainties in data transmission routes. This study develops a sliding surface based on the synchronization errors characterized between each sides of the considered multilateral teleoperation system. Here, two slave robots receive commands from the master system to cooperatively execute the desired teleoperation task in the remote, shared workspace. Lyapunov stability analysis approach guarantees the performance of the proposed controller. Moreover, the effectiveness of the controller is experimentally evaluated through a real-world Internet-based double-slave teleoperation system.

Human posture estimation is a fundamental challenge in computer vision research. This is a task that has received substantial interest due to the importance of evaluating the human performance in several disciplines. The ultimate goal for the vision-based pose estimation task is the markerless accurate prediction of necessary postural information. This paper proposes a single shot deep human posture detection and estimation network. The proposed SSDPose architecture increments standard object detection networks to feature posture estimation. SSDPose is an end-to-end trainable model that detects and estimates the body posture from a single image. Further, our network has been trained to predict joint angles which are essential information for several domains such as biomechanic and ergonomic posture analysis. The reference joint angles have been generated using motion capture sequences and a novel inverse kinematics method. Experimental results demonstrate that SSDPose effectively detects and estimates the posture by achieving person mean average precision (mAP) of 98.2%, an average joint angles MAE of 3.16 ± 1.23 deg and an RMSE of 4.22 ± 1.73 deg at up to 30 FPS.

Fingerprint recognition and indexing were addressed extensively in the literature. However, the number of the datasets that are used for research and validation is limited. Due to privacy laws and acts in several countries, it is challenging to release finger prints to the public. Consequently, this imposes a challenge on validating these search techniques on larger datasets that can be couple of hundreds of millions. To overcome this limitation, synthetic fingerprints datasets have been introduced as an alternative solution. In this paper we propose a generative model for synthesising fingerprint datasets. In this present work, the synthetic fingerprints are generated from the latent space representation using variational auto encoder. The network is trained to generate random samples that have same distribution as real finger print using latent vectors. By examining the generated synthetic fingerprints images, the ridge patterns were recognisable in most of cases. The unrecognisable synthetic images are reflecting the presence of low quality images in the training samples of the original dataset. Moreover, the extraction of minutiae relies on the quality of the input fingerprint images. In conclusion, the proposed method was able to generate synthetic image that can be further processed to accurately extract the finger minutiae and orientation field.

In this paper we propose accelerating live rendering of flame using generative adversarial neural networks. The proposed method targets entertainment and simulation-based training industries whose demands for high fidelity and high frame rate increases steadily. The proposed approach takes image frames rendered with low voxel resolution (8 × 8 × 8 voxels at 90 FPS) and produces image frames equivalent to imagery produced from high voxel resolution (64 × 64 × 64 voxels) typically rendered at 3 FPS. The error was evaluated using the structural similarity image metric (SSIM). The average error between generated image frames and the ground truth recorded 92:7%±4:6%.

VR user identification is of utmost importance especially with the increased applications of VR that will include e-payment among other applications that requires a high level of security. Biometric identification through eye movement, has been used previously due to the intrinsic characteristics of eye movement that characterises a person uniquely. In this paper, we propose using eye movement along with extraocular muscle activations in VR user verification. The muscle activations are calculated using an ocular biomechanical model. The k-NN classification results showed approximately 90% accuracy when using a feature set with eye movement parameters (3 joint angles), muscle activations for all 6 muscles along with the VR object position in 3D. The classifier is a biometric VR user verification tool that provides an easy and non-intrusive methods that can be easily integrated in different VR applications that require user verification.

Virtual and Augmented reality (VR/AR) are being extensively used in many applications that extends from entertainment, training to rehabilitation and treatment of disorders. Studies on the effects of extended use of VR immersion has been performed. However, the change of pupil diameter with the change of VR simulated depth has not been investigated. Pupil dilation is an indicative measure of cognitive overload. In this paper, we investigate the relationship between VR simulated depth and the pupil diameter change. Results showed a significant difference in pupil diameter change with simulated depth and also a strong negative correlation. This indicates that as the depth of the VR object increase (distance from the VR user increase), the VR user's pupil diameter decreases. These results show that change in pupil diameter can be an indicative of change in the depth of the observed virtual object. This can be an effective VR/AR scene scanning and understanding tool.

Imitation learning is an efficient paradigm for teaching and controlling intelligent autonomous cars. Obtaining a set of suitable demonstrations to learn an end-to-end policy from raw pixels is a challenging task in imitation learning problems. Deep neural networks have recently shown outstanding results in learning from raw high dimensional data for solving a wide range of real-world applications. The success of deep neural networks depends on finding suitable hyperparameters for constructing network architecture. Besides, designing hand-crafted deep architectures is not an efficient way for achieving the best performance. To address this issue, this paper performs a neuro-evolution method based on genetic algorithm for finding the optimal deep neural networks architecture in terms of hyperparameters. The experimental results show the effectiveness of the proposed approach for training an autonomous vehicle.

Cognitive load is an often-discussed important topic with regards to human performance. Currently, many psychophysiological measures are used to quantify the level of perceived cognitive load under different tasks and environments. Heart rate (HR) is reported in literature as one of the physiological parameters that is influenced by varying cognitive load levels. Electrocardiography (ECG) is the gold-standard measure of HR measurement, however the use of traditional ECG measurement systems limits the applicability of the system to a lab environment. Recent advancements in wearable devices have provided a step towards bringing the physiological signal based human performance measuring system into real-world applications. In this study we are investigating the usability of the Polar OH1, a HR monitoring device initially used for the purpose of physical activity monitoring to use in an arithmetic cognitive load task. With a study carried out with a dataset of 10 subjects, we are able to conclude that the Polar OH1 can be used in place of ECG monitored HR, at varying cognitive load levels.

The measurement of the human body and categorisation of body types has become a key indicator of health risks over the past century. At first, being of interest to a mathematician, the body shape and anthropometrics was defined in order to create "the average man", an idea of what the best proportions of the typical male should be composed of during the 18th century. This would later evolve into what is commonly known as Body Mass Index (BMI) following the interests of life insurance companies trying to define a risk of fatality. The development of body type categorisation known as BMI was soon thereafter created. BMI has been a standard of measurement in a range of applications from health and well-being industries to government policy and national health surveys. However more recently, BMI has come under question of its reliability due to the number of parameters used to classify an individuals body type. This has been found in some cases to either overestimate or underestimate the category in which an individuals body mass index is defined. In this chapter, a simple non-invasive measurement tool capable of estimating subject specific body parameters is used to increase the accuracy of BMI measurement. The formulation of synthetic data sets is used to create a data base of population characteristics models. This database was then fed into machine learning models to perform regression of anthropometric measures from images acquired from depth sensors such as Kinect.

Human posture analysis is a task of utmost importance for several disciplines. For ergonomists, extracting postural information such as joint angles is necessary to evaluating ergonomie assessment metrics. This allows the early identification of potential work-related musculoskeletal disorders in manufacturing industries, and thus providing adequate interventions. In this paper, we present a holistic posture analysis system that estimates body joint angles from an input depth image. The proposed method utilizes the low cost Kinect sensor for data acquisition and a deep convolutional neural network model for joint angles regression. Further, we rely on learning from synthetic training images to allow simulating several physical tasks by different workers and obtain a highly generalizable learning model. The corresponding ground truth joint angles have been generated using a novel inverse kinematic stage. The proposed method achieves high joint angels prediction rate by recording an average MAE of 4.67 deg and RMSE of 6.64 deg.

Body surface area is an important measure in many clinical trials. It is a critical parameter that is used in estimating radiation and substance doses for human trials. Traditionally, these trials relied on skin-fold tests which are very invasive and uncomfortable to the subjects. In this paper we present a skeleton-free Kinect system to estimate body surface area of human bodies. The proposed system employs the state-of-the-art deep convolutional network to extract meaningful features and estimate the body surface area with a 12 mm2precision.

The mining industry has previously been regarded as a high risk job with safety being the primary concern. Over the years, procedures have been enforced in order to reduce these risks however muscular injuries are still occurring at a significant rate. An assistive technology known as Lift Augmentation Device(LAD) has been in use to reduce the impact on a workers body. This paper provides a musculoskeletal analysis on shoulder and core muscles. Results indicate key differences between manual procedure and LAD-assisted procedures. The LAD-assisted procedure lessened the stretching force on the right shoulder and back muscles at the price of more oscillations in the force applied, while the left shoulder and core muscles suffered more stretching forces and more oscillations in the force applied. Improvements, to be made within the system, are provided.