Impact of Post-Covid-19 on driver behaviour: A perspective towards pandemic-sustained transportation.

Introduction: With the announcement of novel Coronavirus disease 2019 (Covid-19) as a pandemic by World Health Organization (WHO) in March 2020, the whole world went into a lockdown that heavily affected human economic and social life. Since December 2020, with the discovery of effective vaccines, the world is now returning to some normality, particularly for those who are vaccinated. The multimodal transportation has resumed with majority of vaccinated drivers being back on road, driving to their work, and providing transport services. However, there are still several long-term Post-Covid-19 factors, affecting driver health and psychology. Methods: The study deployed a systematic search strategy and selected 62 research publications after rigorous evaluation of the literature. The review was based on (1) forming the inclusion and exclusion criteria, (2) selecting the appropriate keywords, and (3) searching of relevant publications and assessing the eligible articles. Results: A broad perspective study is carried out to gauge the impact of Post-Covid-19 scenarios on the driver physical health and mindset in the context of road safety and pandemic-sustained transportation. It was found that the Post-Covid-19 factors such as wearing face-mask during driving, taking oral anti-viral drugs, and fear of contracting disease, significantly impact the driver's performance and situation awareness skills. The analysis suggested that driver's health vitals and psychological driving awareness can be precisely detected through hybrid driver state monitoring methods. Conclusions: The paper conducts a comprehensive review of the published work and provides unique research opportunities to counteract the challenges involved in precise monitoring of driver behaviour under the effects of different Post-Covid-19 factors. The perspective suggested the possible solutions to live with the pandemic in the context of pandemic-sustained transportation.

Patient-Specific Robot-Assisted Stroke Rehabilitation Guided by EEG - A Feasibility Study.

Multi-session robot-assisted stroke rehabilitation program requires patients to perform repetitive tasks. It is challenging for the patient to maintain concentration during training sessions. A novel intervention strategy using Electroencephalography (EEG) signals is proposed to maintain concentration during training by enhancing the engagement of stroke patients using robot-assisted multi-session rehabilitation. The approach is illustrated by applying it to one stroke patient undergoing 12 training sessions of hand motor training on the AMADEO rehabilitation device. AMADEO offers four modes of training programs of increased intensity comprising passive training, passive training with biofeedback, assistive training as well as active 2D training games. The EEG signals are measured over eight electrode sites: FC4, C4, CP4, FC3, C3, CP3, Cz, and CPz during each training day to extract movement-related cortical potential (MRCP) signals. Moreover, functional hand recovery parameters are determined using the AMADEO assessment tool. The patient's level of engagement is determined by the negative amplitude of the MRCP signal. The rehabilitation program is switched to a more intense training mode when a consistent decrease is observed in the negative amplitude of MRCP signals from the monitored electrodes. Using this approach, the rehabilitation program becomes patient-specific and adaptive. In addition, it is shown that each training mode exhibits a different recovery level of the affected hand and maximum recovery is achieved when MRCP signals indicate that the patient is actively participating in the training.

Quantitative Frailty Assessment Based on Kinematic Parameters of Daily Living Activities.

Frailty is a prevailing phenomena in older people. It is an age related syndrome that can increase the risk of fall in elderly. The people with age above 65 suffers from various functional decline and cognitive impairments. Such deficiencies are conventionally measured subjectively by geriatrics using questionnaire-based methods and clinical tests. Activities of daily living are also assessed in clinical settings by analysing simple tasks performed by the subject such as sit to stand and walking some distances. The clinical methods used to assess frailty and analyse the activity of daily living are subjective in nature and prone to human error. An objective method is proposed to quantitatively measure frailty using inertial sensor mounted on healthy, frail and nonfrail subjects while performing the sit to stand test (SiSt). An artificial neural networks based algorithm is developed to classify the frailty by extracting a unique set of features from 2D -Centre of Mass (CoM) trajectories derived from SiSt clinical test. The results indicate that the proposed algorithms provides an objective assessment of frailty that can be used by geriatrics in turn to make a more objective judgement of frailty status of older people.

The sit to stand to sit postural transition variability in the five time sit to stand test in older people with different fall histories.

BACKGROUND: It has been proved that increased motion variability can be indicative of lack of stability. Despite the extensive literature on single sit to stand/stand to sit postural transitions analysis in older people, no studies to this date have assessed the sit to stand to sit (STSTS) transition variability in older adults. RESEARCH QUESTION: To investigate the variability in STSTS transition during the five times sit to stand (FTSS) test in older people with different fall histories. METHODS: Seventy-five older (80.5 ± 7.5) and twenty-five younger (27.7 ± 6.5) subjects participated in the study. The older participants were categorized into three groups of non-fallers, once-fallers, and multiple-fallers based on their fall histories. Subjects were fitted with an IMU at their lower backs and asked to fully stand up and then sit down again five times in a row. The angular rotation of the trunk in the sagittal plane was recorded. Using the DTW method, the first STSTS transition of each subject was compared to the last transition and the variability was measured. The correlation between STSTS variability and older participants' Berg balance scale (BBS) was investigated. RESULTS: The STSTS variability results were significantly different in older fallers (multiple-fallers and once-fallers) compared to both younger participants and older non-fallers. The results yielded a sensitivity of 85.4 % and a specificity of 83.3 % in recognizing older fallers from older non-fallers and a sensitivity and specificity of 86.7 % and 85.7 % respectively in recognizing older multiple-fallers from other older participants. The STSTS variability was found to be significantly correlated with BBS. SIGNIFICANCE: The findings demonstrated a strong indication of variability in the STSTS transition in older fallers and a significant correlation between STSTS variability and BBS. The results suggest that variability analysis of the STSTS transition has the potential to be used for fall risk analysis in older adults.

Output feedback H∞ control for active suspension of in-wheel motor driven electric vehicle with control faults and input delay.

In this paper, an output feedback H∞ controller is proposed for active suspension of an electric vehicle driven by in-wheel motors with actuator faults and time delay. The dynamic damping in-wheel motor driven system, in which the in-wheel motor is designed as a dynamic vibration absorber (DVA), is developed to improve ride quality and isolate the force transmitted to motor bearings. Furthermore, parameters of vehicle suspension and DVA are optimized based on the particle swarm optimization (PSO) to achieve better suspension performance. As some of the states such as the DVA velocity and unsprung mass velocity are difficult to measure, a robust H∞ output feedback controller is developed to deal with the problem of active suspension control with actuator faults and time delay. The proposed controller could guarantee the system's asymptotic stability and H∞ performance, simultaneously satisfying the performance constraints such as road holding, suspension stroke, and actuator limitation. Finally, the effectiveness of the proposed output feedback controllers is demonstrated based on the quarter vehicle suspension model under bump and random road excitations.

Assessment of frailty: a survey of quantitative and clinical methods.

BACKGROUND: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty. RESULTS: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty. CONCLUSION: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

Fault Tolerant Sliding Mode Predictive Control for Uncertain Steer-by-Wire System.

The Steer-by-Wire (SbW) system is an electronically controlled steering system that is able to improve steering capability without mechanical links between the steering wheel and the front wheels. However, failure of the SbW system actuator may lead to steering performance degradation and result in instability. In this paper, a fault tolerant sliding mode predictive control (SMPC) strategy for an SbW system is proposed. The sliding mode control is applied to improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances, while the MPC is applied to enhance the fault tolerant capability of the steering control processes. The chaos particle swarm optimization (CPSO) algorithm is introduced to optimize the MPC and a two-stage Kalman filter is introduced to simultaneously provide fault information and state estimation. The performance of the proposed approach is validated through computer simulation. The results demonstrate that the proposed SMPC-CPSO controller is more robust and provides better tracking performance in the presence of model uncertainties, disturbance, and actuator faults than SCMP-PSOs (heterogeneous comprehensive learning particle swarm optimization, evolutionary particle swarm optimizer, etc), SMPC-differential evolution, MPC, SMPC, and MPC-PSO.

Body postural sway analysis in older people with different fall histories.

A cross-sectional study of postural sway analysis in older non-fallers, once-fallers and multiple-fallers using five common standing tests was conducted. Eighty-six older subjects with an average age of 80.4 years (SD ± 7.9) participated in the study. The angular rotation and velocity of the trunk of the participants in the roll (lateral) and pitch (sagittal) planes were recorded using an inertial sensor mounted on their lower backs. The Gaussian Mixture Models (GMM), Expectation-Maximisation (EM) and the Minimum Message Length (MML) algorithms were applied to the acquired data to obtain an index indicative of the body sway. The standing with feet together and standing with one foot in front, sway index distinguished older fallers from non-fallers with specificity of 75.7% and 77.7%, respectively, and sensitivity of 78.6% and 82.1%, respectively. This compares favourably with the Berg Balance Scales (BBS) with specificity of 70.5% and sensitivity of 75.3%. The results suggest that the proposed method has potential as a protocol to diagnose balance disorder in older people. Graphical abstract.

The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study.

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. However, the lack of fully adaptive assist-as-needed control of the robotic devices and an inadequate immersive virtual environment that can promote active participation during training are obstacles hindering the achievement of better training results with fewer training sessions required. This study thus focuses on these research gaps by combining these 2 key components into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The effectiveness of the proposed system is tested by conducting clinical trials on a chronic stroke patient and verified through clinical evaluation methods by measuring the key kinematic features such as active range of motion (ROM), finger strength, and velocity. By comparing the pretraining and post-training results, the study demonstrates that the proposed method can further enhance the effectiveness of fine hand motion rehabilitation training by improving finger ROM, strength, and coordination.

Clinical effectiveness of combined virtual reality and robot assisted fine hand motion rehabilitation in subacute stroke patients.

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive rehabilitation training in restoring motor skills after a stroke. This study focuses on the rehabilitation of fine hand motion skills due to their vital role in performing delicate activities of daily living (ADL) tasks. The proposed rehabilitation system combines an adaptive assist-as-needed (AAN) control algorithm and a Virtual Reality (VR) based rehabilitation gaming system (RGS). The developed system is described and its effectiveness is validated through clinical trials on a group of eight subacute stroke patients for a period of six weeks. The impact of the training is verified through standard clinical evaluation methods and measuring key kinematic parameters. A comparison of the pre- and post-training results indicates that the method proposed in this study can improve fine hand motion rehabilitation training effectiveness.

Neural Network-Based Passivity Control of Teleoperation System Under Time-Varying Delays.

In this paper, a novel neural network (NN)-based four-channel wave-based time domain passivity approach (TDPA) is proposed for a teleoperation system with time-varying delays. The designed wave-based TDPA aims to robustly guarantee the channels passivity and provide higher transparency than the previous power-based TDPA. The applied NN is used to estimate and eliminate the system's dynamic uncertainties. The system stability with linearity assumption on human and environment has been analyzed using Lyapunov method. The proposed algorithm is validated through experimental work based on a 3-DOF bilateral teleoperation platform in the presence of different time delays.

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers.

This paper presents an approach for learning robust models of humanoid robot trajectories from demonstration. In this formulation, a model of the joint space trajectory is represented as a sequence of motion primitives where a nonlinear dynamical system is learned by constructing a hidden Markov model (HMM) predicting the probability of residing in each motion primitive. With a coordinated mixture of factor analyzers as the emission probability density of the HMM, we are able to synthesize motion from a dynamic system acting along a manifold shared by both demonstrator and robot. This provides significant advantages in model complexity for kinematically redundant robots and can reduce the number of corresponding observations required for further learning. A stability analysis shows that the system is robust to deviations from the expected trajectory as well as transitional motion between manifolds. This approach is demonstrated experimentally by recording human motion with inertial sensors, learning a motion primitive model and correspondence map between the human and robot, and synthesizing motion from the manifold to control a 19 degree-of-freedom humanoid robot.

Force application during cochlear implant insertion: an analysis for improvement of surgeon technique.

Highly invasive surgical procedures, such as the implantation of a prosthetic device, require correct force delivery to achieve desirable outcomes and minimize trauma induced during the operation. Improvement in surgeon technique can reduce the chances of excessive force application and lead to optimal placement of the electrode array. The fundamental factors that affect the degree of success for cochlear implant recipients are identified through empirical methods. Insertion studies are performed to assess force administration and electrode trajectories during implantations of the Nucleus 24 Contour and Nucleus 24 Contour Advance electrodes into a synthetic model of the human Scala Tympani, using associated methods. Results confirm that the Advance Off- Stylet insertion of the soft-tipped Contour Advance electrode gives an overall reduction in insertion force. Analysis of force delivery and electrode positioning during cochlear implantation can help identify and control key factors for improvement of insertion method. Based on the findings, suggestions are made to enhance surgeon technique.

Parametric model of the scala tympani for haptic-rendered cochlear implantation.

A parametric model of the human scala tympani has been designed for use in a haptic-rendered computer simulation of cochlear implant surgery. It will be the first surgical simulator of this kind. A geometric model of the Scala Tympani has been derived from measured data for this purpose. The model is compared with two existing descriptions of the cochlear spiral. A first approximation of the basilar membrane is also produced. The structures are imported into a force-rendering software application for system development.