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"Man Machine Systems"
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Human-robot interaction strategies for walker-assisted locomotion
This book presents the development of a new multimodal human-robot interface for testing and validating control strategies applied to robotic walkers for assisting human mobility and gait rehabilitation. The aim is to achieve a closer interaction between the robotic device and the individual, empowering the rehabilitation potential of such devices in clinical applications. A new multimodal human-robot interface for testing and validating control strategies applied to robotic walkers for assisting human mobility and gait rehabilitation is presented. Trends and opportunities for future advances in the field of assistive locomotion via the development of hybrid solutions based on the combination of smart walkers and biomechatronic exoskeletons are also discussed.
Book
Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
Background
Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.
Methods
The signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.
Results
A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O
2
Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of
β
1
and
β
2
bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O
2
Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks.
Conclusions
The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.
Journal Article
The measure of all minds : evaluating natural and artificial intelligence
Are psychometric tests valid for a new reality of artificial intelligence systems, technology-enhanced humans, and hybrids yet to come? Are the Turing Test, the ubiquitous CAPTCHAs, and the various animal cognition tests the best alternatives? Josâe Hernâandez-Orallo formulates major scientific questions, integrates the most significant research developments, and offers a vision of the universal evaluation of cognition. By replacing the dominant anthropocentric stance with a universal perspective where living organisms are considered as a special case, long-standing questions in the evaluation of behavior can be addressed in a wider landscape. Can we derive task difficulty intrinsically? Is a universal g factor - a common general component for all abilities - theoretically possible? Using algorithmic information theory as a foundation, the book elaborates on the evaluation of perceptual, developmental, social, verbal and collective features and critically analyzes what the future of intelligence might look like.
Book
A call for open data to develop mental health digital biomarkers
Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions (‘model equity’) across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.
Journal Article
Avatars and virtual agents – relationship interfaces for the elderly
In the Digital Era, the authors witness a change in the relationship between the patient and the care-giver or Health Maintenance Organization's providing the health services. Another fact is the use of various technologies to increase the effectiveness and quality of health services across all primary and secondary users. These technologies range from telemedicine systems, decision making tools, online and self-services applications and virtual agents; all providing information and assistance. The common thread between all these digital implementations, is they all require human machine interfaces. These interfaces must be interactive, user friendly and inviting, to create user involvement and cooperation incentives. The challenge is to design interfaces which will best fit the target users and enable smooth interaction especially, for the elderly users. Avatars and Virtual Agents are one of the interfaces used for both home care monitoring and companionship. They are also inherently multimodal in nature and allow an intimate relation between the elderly users and the Avatar. This study discusses the need and nature of these relationship models, the challenges of designing for the elderly. The study proposes key features for the design and evaluation in the area of assistive applications using Avatar and Virtual agents for the elderly users.
Journal Article
iSurgARy: A mobile augmented reality solution for ventriculostomy in resource‐limited settings
Global disparities in neurosurgical care necessitate innovations addressing affordability and accuracy, particularly for critical procedures like ventriculostomy. This intervention, vital for managing life‐threatening intracranial pressure increases, is associated with catheter misplacement rates exceeding 30% when using a freehand technique. Such misplacements hold severe consequences including haemorrhage, infection, prolonged hospital stays, and even morbidity and mortality. To address this issue, a novel, stand‐alone mobile‐based augmented reality system (iSurgARy) aimed at significantly improving ventriculostomy accuracy, particularly in resource‐limited settings such as those in low‐ and middle‐income countries is presented. iSurgARy uses landmark based registration by taking advantage of light detection and ranging to allow for accurate surgical guidance. To evaluate iSurgARy, a two‐phase user study is conducted. Initially, the usability and learnability is assessed with novice participants using the system usability scale (SUS), incorporating their feedback to refine the application. In the second phase, human‐computer interaction and clinical domain experts are engaged to evaluate this application, measuring root mean square error, SUS and NASA task load index metrics to assess accuracy usability, and cognitive workload, respectively. Global disparities in neurosurgical care demand innovations that improve affordability and procedural accuracy, particularly for high‐risk procedures like ventriculostomy, where catheter misplacement rates are over 30% with freehand techniques. To address this, iSurgARy, a mobile‐based augmented reality system leveraging light detection and ranging for precise guidance, was developed to enhance accuracy in resource‐limited settings. The system's usability, learnability, and effectiveness were evaluated through a two‐phase user study, incorporating feedback from novice users and expert assessments using metrics like root mean square error, system usability scale, and NASA task load index.
Journal Article
Clinical trainee performance on task‐based AR/VR‐guided surgical simulation is correlated with their 3D image spatial reasoning scores
This paper describes a methodology for the assessment of training simulator‐based computer‐assisted intervention skills on an AR/VR‐guided procedure making use of CT axial slice views for a neurosurgical procedure: external ventricular drain (EVD) placement. The task requires that trainees scroll through a stack of axial slices and form a mental representation of the anatomical structures in order to subsequently target the ventricles to insert an EVD. The process of observing the 2D CT image slices in order to build a mental representation of the 3D anatomical structures is the skill being taught, along with the cognitive control of the subsequent targeting, by planned motor actions, of the EVD tip to the ventricular system to drain cerebrospinal fluid (CSF). Convergence is established towards the validity of this assessment methodology by examining two objective measures of spatial reasoning, along with one subjective expert ranking methodology, and comparing these to AR/VR guidance. These measures have two components: the speed and accuracy of the targeting, which are used to derive the performance metric. Results of these correlations are presented for a population of PGY1 residents attending the Canadian Neurosurgical “Rookie Bootcamp” in 2019.
Journal Article
Binary Controller Based on the Electrical Activity Related to Head Yaw Rotation
A human machine interface (HMI) is presented to switch on/off lights according to the head left/right yaw rotation. The HMI consists of a cap, which can acquire the brain’s electrical activity (i.e., an electroencephalogram, EEG) sampled at 500 Hz on 8 channels with electrodes that are positioned according to the standard 10–20 system. In addition, the HMI includes a controller based on an input–output function that can compute the head position (defined as left, right, and forward position with respect to yaw angle) considering short intervals (10 samples) of the signals coming from three electrodes positioned in O1, O2, and Cz. An artificial neural network (ANN) training based on a Levenberg–Marquardt backpropagation algorithm was used to identify the input–output function. The HMI controller was tested on 22 participants. The proposed classifier achieved an average accuracy of 88% with the best value of 96.85%. After calibration for each specific subject, the HMI was used as a binary controller to verify its ability to switch on/off lamps according to head turning movement. The correct prediction of the head movements was greater than 75% in 90% of the participants when performing the test with open eyes. If the subjects carried out the experiments with closed eyes, the prediction accuracy reached 75% of correctness in 11 participants out of 22. One participant controlled the light system in both experiments, open and closed eyes, with 100% success. The control results achieved in this work can be considered as an important milestone towards humanoid neck systems.
Journal Article
Cognitive Work Analysis: Coping with Complexity
'Complex sociotechnical systems' are systems made up of numerous interacting parts, both human and non-human, operating in dynamic, ambiguous and safety critical domains. Cognitive Work Analysis (CWA) is a structured framework specifically developed for considering the development and analysis of these complex socio-technical systems. Unlike many human factors approaches, CWA does not focus on how human-system interaction should proceed (normative modelling) or how human-system interaction currently works (descriptive modelling). Instead, through a focus on constraints, it develops a model of how work can be conducted within a given work domain, without explicitly identifying specific sequences of actions (formative modelling).
eBook
Automated System for Monitoring and Diagnostics Pilot's Emotional State in Flight
In this article, the system for monitoring of the emotional state changes of the air navigation system's human operator in the extreme situations, based on the using of the prior models of the operator activity which built on the posterior researches of actual material of the aviation accidents investigations, has been proposed. The stability of aviation man-machine system “human-operator – aircraft” during the deformations of the operator's emotional experience has been defined according to the Nyquist criterion. A computer program for diagnostics of the emotional state of the human operator has been developed. The system based on monitoring of the current emotional state of the air navigation system's human operator and diagnostics of the deformations of emotional experience with the determination of the operator's functional stability will allow preventing the development of potentially hazardous flight situations towards worsening in a proactive manner.
Journal Article