Stroke Patients’ Psychophysiological responses to Robot Training

Robotic interfaces are becoming increasingly common in motor rehabilitation, for they enable more intensive therapy. As the patient’s cognitive intent further enhances motor relearning, the robots have been usually combined with virtual reality (VR). In clinical environment the difficulty level of the training has to be ensured in a way to meet a particular patient’s performance capabilities, inducing appropriate motivation and arousal. While rehabilitation robots can provide objective information about the patient’s motor performance and VR-based game systems include real-time feedback, such systems do not offer insight into the patient’s psychological state (mood, motivation, engagement). Emotions experienced while playing computer games are reflected in physiological responses, which could be used to determine a patient’s level of enjoyment or frustration while training. The most commonly used psychophysiological responses are those of the autonomic nervous system: heart rate, skin conductance, respiration and skin temperature. Though autonomic nervous system responses are also influenced by any physical activity, their usefulness up to a certain level of physical load was confirmed. Stroke survivors seem to have weaker psychophysiological responses than healthy subjects. The disease itself can change the activity of the autonomic nervous system and other factors such as comorbidity and medication should be taken in consideration to influence psychophysiological measurements. Only skin conductance and skin temperature have been proven to be useful for psychological state estimation in stroke patients during robot-aided training in VR. Changes in heart rate primarily reflect physical activity while changes in respiration rate are small and unreliable. The psychophysiological measurements seem to be unreliable for assessing stroke patients’ psychological state during robot training in VR. Further studies are needed in this aspect of rehabilitation robotics

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

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Multimodal Interfaces to Improve Therapeutic Outcomes in Robot-Assisted Rehabilitation

The paper presents the developing of a new robotic system for the administration of a highly sophisticated therapy to stroke patients. This therapy is able to maximize patient motivation and involvement in the therapy and continuously assess the progress of the recovery from the functional viewpoint. Current robotic rehabilitation systems do not include patient information on the control loop. The main novelty of the presented approach is to close patient in the loop and use multisensory data (such as pulse, skin conductance, skin temperature, position, velocity, etc.) to adaptively and dynamically change complexity of the therapy and real-time displays of a virtual reality system in accordance with specific patient requirements. First, an analysis of subject’s physiological responses to different tasks is presented with the objective to select the best candidate of physiological signals to estimate the patient physiological state during the execution of a virtual rehabilitation task. Then, the design of a prototype of multimodal robotic platform is defined and developed to validate the scientific value of the proposed approach

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.Comment: 20 page

Brain computer interfaces: psychology and pragmatic perspectives for the future.

Whilst technologies, such as psychophysiological measurements in general and electroencephalograms (EEG) in particular, have been around and continually improving for many years, future technologies promise to revolutionise the emerging Information Society through the development of brain-computer interfaces and augmented cognition solutions. This paper explores critical psychological and pragmatic issues that must be understood before these technologies can deliver their potential well. Within the context of HCI, we examined a sample (n =105) BCI papers and found that the majority of research aimed to provide communication and control resources to people with disabilities or with extreme task demands. However, the concepts of usability and accessibility, and respective findings from their substantial research literatures were rarely applied explicitly but referenced implicitly. While this suggests an increased awareness of these concepts and the related large research literatures, the task remains to sharpen these concepts and to articulate their obvious relevance to BCI work

Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton

When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user’s physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase ; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Preventing and monitoring work-related diseases in firefighters: a literature review on sensor-based systems and future perspectives in robotic devices.

: In recent years, the necessity to prevent work-related diseases has led to the use of sensor based systems to measure important features during working activities. This topic achieved great popularity especially in hazardous and demanding activities such as those required of firefighters. Among feasible sensor systems, wearable sensors revealed their advantages in terms of possibility to conduct measures in real conditions and without influencing the movements of workers. In addition, the advent of robotics can be also exploited in order to reduce work-related disorders. The present literature review aims at providing an overview of sensor-based systems used to monitor physiological and physical parameters in firefighters during real activities, as well as to offer ideas for understanding the potentialities of exoskeletons and assistive devices

Perceived distress in assisted gait with a four-wheeled rollator under stress induction conditions

In assisted ambulation, the user’s psychological comfort has a significant impact not only on acceptability of mobility aids but also on overall gait performance. Specifically, in the case of rollators, negative states such as distress may result in balance loss, inefficient manoeuvres, and an increased risk of falling. This paper presents a pilot study to investigate the effect of distress on rollator assisted navigation. To achieve this goal, a novel test protocol is proposed to assess distress while walking with a rollator, using the Self-Assessment Manikin (SAM) questionnaire. First, the participant completes a standardised visual stress induction test and fills in a SAM questionnaire on the dimensions of arousal and valence, to establish personal benchmarks. Then, they complete a course consisting of four navigation tasks with different levels of difficulty that affect the rollator manoeuvrability, filling in a SAM questionnaire after each task. An experiment including 25 healthy volunteers has been completed. Our preliminary results show that stressors like uneven or sloping surfaces increase perceived stress, whereas the shape of the trajectory does not significantly affect stress. The ultimate purpose of this work is to validate a performance-oriented protocol to investigate the dynamics of stress response in assisted walk and to train automatic stress detection systems.The work was supported by the Ministerio de Ciencia e Innovación [RTI2018-096701-B-C22]; Ministerio de Ciencia, Innovación y Universidades [RTI2018-096701-B-C21 (SAVIA:]; Ministerio de Ciencia, Innovación y Universidades [RTI2018-096701-B-C21]; Universidad de Málaga [E3-PROYECTOS DE PRUEBA DE CONCEPTO (E3/02/18)].Peer ReviewedPostprint (published version

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level