The neuroscience of body memory: Recent findings and conceptual advances

The body is a very special object, as it corresponds to the physical component of the self and it is the medium through which we interact with the world. Our body awareness includes the mental representation of the body that happens to be our own, and traditionally has been defined in terms of body schema and body image. Starting from the distinction between these two types of representations, the present paper tries to reconcile the literature around body representations under the common framework of body memory. The body memory develops ontogenetically from birth and across all the life span and is directly linked to the development of the self. Therefore, our sense of self and identity is fundamentally based on multisensory knowledge accumulated in body memory, so that the sensations collected by our body, stored as implicit memory, can unfold in the future, under suitable circumstances. Indeed, these sets of bodily information had been proposed as possible key factors underpinning several mental health illnesses. Following this perspective, the Embodied Medicine approach put forward the use of advanced technologies to alter the dysfunctional body memory to enhance people’s well-being. In the last sections, recent experimental pieces of evidence will be illustrated that targeted specifically bodily information for increasing health and wellbeing, by means of two strategies: interoceptive feedback and bodily illusions

MITIGATING PUBLIC SPEAKING ANXIETY USING VIRTUAL REALITY AND POPULATION-SPECIFIC MODELS

In the education and workplace landscape of the 21st century, it is often said that a person is only as valuable as the ideas s/he has and can share. Public speaking skills are essential to help people effectively exchange ideas, persuade, inform their audiences as well as make a tangible impact. They also plays a vital role in one’s academic and professional success. However, research shows that public speaking anxiety (PSA) ranks as a top social phobia among many people and tends to be aggravated in minorities, first generation students, and non-native speakers. This research aims at mitigating this anxiety by utilizing physiological (cardiovascular activity, electrodermal activity etc.) and acoustic (pitch, intonation, etc.) indices captured from wearable devices and virtual reality (VR) interfaces to quantify and predict PSA. This work also examines the significance of individual-specific factors, such as general trait anxiety and personality metrics, as well as contextual factors, such as age, gender, highest education, and native language, receny of public speaking in moderating the association between bio-behavioural (physiological and acoustic) indices and PSA. The individual-specific information is used to develop population-specific machine learning models of PSA. Results of this research highlight the importance of including such factors for detecting PSA with the proposed population-based PSA models yielding Spearman’s correlation of 0.55 n(p < 0.05) between the actual and predicted state-based scores. This work further analyzes whether systematic exposure to public speaking tasks in a VR environment can help alleviate PSA. Results indicate that systematic exposure to public speaking in VR can alleviate PSA in terms of both self-reported (p < 0.05) and physiological (p < 0.05) indices. Findings of this study will enable researchers to better understand antedecedents and causes of PSA as well as lay the foundation toward developing adaptive behavioural interventions for social communication disorders using systematic exposure (e.g., through VR stimuli), relaxation feedback, and cognitive restructuring

Exploration of digital biomarkers in chronic low back pain and Parkinson’s disease

Chronic pain and Parkinson’s disease are illnesses with personal disease progression, symptoms, and the experience of these. The ability to measure and monitor the symptoms by digitally and remotely is still limited. The aim was to study the usability and feasibility of real-world data from wearables, mobile devices, and patients in exploring digital biomarkers in these diseases. The key hypothesis was that this allows us to measure, analyse and detect clinically valid digital signals in movement, heart rate and skin conductance data. The laboratory grade data in chronic pain were collected in an open feasibility study by using a program and built-in sensors in virtual reality devices. The real-world data were collected with a randomized clinical study by clinical assessments, built-in sensors, and two wearables. The laboratory grade dataset in Parkinson’s disease was obtained from Michael J. Fox Foundation. It contained sensor data from three wearables with clinical assessments. The real-world data were collected with a clinical study by clinical assessments, a wearable, and a mobile application. With both diseases the laboratory grade data were first explored, before the real-world data were analyzed. The classification of chronic pain patients with the laboratory grade movement data was possible with a high accuracy. A novel real-world digital signal that correlates with clinical outcomes was found in chronic low back pain patients. A model that was able to detect different movement states was developed with laboratory grade Parkinson’s disease data. A detection of these states followed by the quantification of symptoms was found to be a potential method for the future. The usability of data collection methods in both diseases were found promising. In the future the analyses of movement data in these diseases could be further researched and validated as a movement based digital biomarkers to be used as a surrogate or additional endpoint. Combining the data science with the optimal usability enables the exploitation of digital biomarkers in clinical trials and treatment.Digitaalisten biomarkkereiden tunnistaminen kroonisessä alaselkäkivussa ja Parkinsonin taudissa Krooninen kipu ja Parkinsonin tauti ovat oireiden, oirekokemuksen sekä taudin kehittymisen osalta yksilöllisiä sairauksia. Kyky mitata ja seurata oireita etänä on vielä alkeellista. Väitöskirjassa tutkittiin kaupallisten mobiili- ja älylaitteiden hyödyntämistä digitaalisten biomarkkereiden löytämisessä näissä taudeissa. Pääolettamus oli, että kaupallisten älylaitteiden avulla kyetään tunnistamaan kliinisesti hyödyllisiä digitaalisia signaaleja. Kroonisen kivun laboratorio-tasoinen data kerättiin tätä varten kehitettyä ohjelmistoa sekä kaupallisia antureita käyttäen. Reaaliaikainen kipudata kerättiin erillisen hoito-ohjelmiston tehoa ja turvallisuutta mitanneessa kliinisessä tutkimuksessa sekä kliinisiä arviointeja että anturidataa hyödyntäen. Laboratorio-tasoinena datana Parkinsonin taudissa käytettiin Michael J. Fox Foundationin kolmella eri älylaitteella ja kliinisin arvioinnein kerättyä dataa. Reaaliaikainen data kerättiin käyttäen kliinisia arviointeja, älyranneketta ja mobiilisovellusta. Molempien indikaatioiden kohdalla laboratoriodatalle tehtyä eksploratiivista analyysia hyödynnettiin itse reaaliaikaisen datan analysoinnissa. Kipupotilaiden tunnistaminen laboratorio-tasoisesta liikedatasta oli mahdollista korkealla tarkkuudella. Reaaliaikaisesta liikedatasta löytyi uusi kliinisten arviointien kanssa korreloiva digitaalinen signaali. Parkinsonin taudin datasta kehitettiin uusi liiketyyppien tunnistamiseen tarkoitettu koneoppimis-malli. Sen hyödyntäminen liikedatan liiketyyppien tunnistamisessa ennen varsinaista oireiden mittausta on lupaava menetelmä. Käytettävyys molempien tautien reaaliaikaisissa mittausmenetelmissä havaittiin toimivaksi. Reaaliaikaiseen, kaupallisin laittein kerättävään liikedataan pohjautuvat digitaaliset biomarkkerit ovat lupaava kohde jatkotutkimukselle. Uusien analyysimenetelmien yhdistäminen optimaaliseen käytettävyyteen mahdollistaa tulevaisuudessa digitaalisten biomarkkereiden hyödyntämisen sekä kroonisten tautien kliinisessä tutkimuksessa että itse hoidossa

Machine learning methods for the study of cybersickness: a systematic review

This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness

Within- and between-session prefrontal cortex response to virtual reality exposure therapy for acrophobia

Exposure Therapy (ET) has demonstrated its efficacy in the treatment of phobias, anxiety and Post-traumatic Stress Disorder (PTSD), however, it suffers a high drop-out rate because of too low or too high patient engagement in treatment. Virtual Reality Exposure Therapy (VRET) is comparably effective regarding symptom reduction and offers an alternative tool to facilitate engagement for avoidant participants. Neuroimaging studies have demonstrated that both ET and VRET normalize brain activity within a fear circuit. However, previous studies have employed brain imaging technology which restricts people’s movement and hides their body, surroundings and therapist from view. This is at odds with the way engagement is typically controlled. We used a novel combination of neural imaging and VR technology—Functional Near-Infrared Spectroscopy (fNIRS) and Immersive Projection Technology (IPT), to avoid these limitations. Although there are a few studies that have investigated the effect of VRET on a brain function after the treatment, the present study utilized technologies which promote ecological validity to measure brain changes after VRET treatment. Furthermore, there are no studies that have measured brain activity within VRET session. In this study brain activity within the prefrontal cortex (PFC) was measured during three consecutive exposure sessions. N = 13 acrophobic volunteers were asked to walk on a virtual plank with a 6 m drop below. Changes in oxygenated (HbO) hemoglobin concentrations in the PFC were measured in three blocks using fNIRS. Consistent with previous functional magnetic resonance imaging (fMRI) studies, the analysis showed decreased activity in the DLPFC and MPFC during first exposure. The activity increased toward normal across three sessions. The study demonstrates potential efficacy of a method for measuring within-session neural response to virtual stimuli that could be replicated within clinics and research institutes, with equipment better suited to an ET session and at fraction of the cost, when compared to fMRI. This has application in widening access to, and increasing ecological validity of, immersive neuroimaging across understanding, diagnosis, assessment and treatment of, a range of mental disorders such as phobia, anxiety and PTSD or addictions

Experimental studies of the interaction between people and virtual humans with a focus on social anxiety

Psychotherapy has been one of the major applications of Virtual Reality technology; examples include fear of flying, heights, spiders, and post‐traumatic stress disorder. Virtual reality has been shown to be useful, in the context of exposure therapy for the treatment of social anxiety, such as fear of public speaking, where the clients learn how to conquer their anxiety through interactions with Virtual Characters (avatars). This thesis is concerned with the interaction between human participants and avatars in a Virtual Environment (VE), with the main focus being on Social Anxiety. It is our hypothesis that interactions between people and avatars can evoke in people behaviours that correspond to their degree of social anxiety or confidence. Moreover the responses of people to avatars will also depend on their degree of exhibited social anxiety – they will react differently to a shy avatar compared to a confident avatar. The research started with an experimental study on the reaction of shy and confident male volunteers to an approach by an attractive and friendly virtual woman in a VE. The results show that the participants responded according to expectations towards the avatar at an emotional, physiological, and behavioural level. The research then studied a particular cue which represents shyness – “blushing”. Experiments were carried out on how participant responds towards a blushing avatar. The results suggested that, even without consciously noticing the avatar’s blushing, the participants had an improved relationship with her when she was blushing. Finally, the research further investigated how people respond towards a shy avatar as opposed to a confident one. The results show that participants gave more positive comments to the personality of the avatar displaying signs of shyness

Virtual reality for anxiety and stress-related disorders: A SWOT analysis

Virtual Reality (VR) Therapy has emerged in the 90s as an appealing way of delivering exposure treatment. Throughout these years, ample evidence has been published. Although there is an agreed consensus regarding its efficacy, currently a quick shift in the field is being experienced, especially due to the advent of off-the-shelf technology that is greatly facilitating its dissemination. In this context, theoretical discussions of the field appear as an important action in order to take stock of the mounting evidence that has been produced and the main challenges for the coming future. To stimulate the discussion in a burgeoning field, a SWOT analysis is proposed, which may help to map the field of VR therapy for anxiety and stress-related disorders. Overall, it is undoubted that VR appears as a well-established technology for the treatment of ASRD and the main challenges are in line with the possibility of hurdling the same obstacles that the whole field of clinical psychology and psychotherapy has to deal with: How to bridge the gap between research and clinical practice

Tecnologia assistiva para crianças com transtorno do espectro autista que vivenciam estresse e ansiedade

With the development of current technology and influences that have been made by the Industry 4.0 utilizing ICTs, IoT, smart systems and products and many others, Assistive Technology (AT) is an important and integral part of the daily life of many people who experience disabilities. Autism Spectrum Disorder (ASD) is a special category of disorder that can greatly benefit from its use. The purpose of this research is to collect data of Assistive Technology aimed at the detection, prevention and improvement of anxiety and stress (a characteristic of which has been proven to exist and is expressed in various ways in people with ASD). In the introduction, basic definitions regarding the neurobiology of stress and ASD are analyzed. In the main part AT, stress and anxiety correlations are made with ASD and AT devices are described and documented regarding their use for anxiety and stress in children and adolescents with ASD. The Assistive equipment and devices are divided into 2 main categories, 1) Low-tech and 2) Mid-High tech. The results of the research reveal a significant research gap in the use of AT to combat stress and anxiety and the difficulty of many promising options (especially in the domain of Mid-High tech) to be an easy and economical solution in integrating them into the daily life of people with ASD.Con el desarrollo de la tecnología actual y las influencias que ha tenido la Industria 4.0 utilizando TIC, IoT, sistemas y productos inteligentes y muchos otros, la Tecnología de asistencia (TA) es una parte importante e integral de la vida diaria de muchas personas que sufren de discapacidad. . . El trastorno del espectro autista (TEA) es una categoría especial de trastorno que puede beneficiarse enormemente de su uso. El objetivo de esta investigación es recopilar datos de Tecnología Asistiva dirigidos a detectar, prevenir y mejorar la ansiedad y el estrés (una característica que está comprobada y se expresa de diferentes formas en las personas con TEA). En la introducción se analizan definiciones básicas sobre la neurobiología del estrés y el TEA. En su mayor parte se realizan correlaciones de TA, estrés y ansiedad con los TEA y se describen y documentan los dispositivos de TA en relación a su uso para la ansiedad y el estrés en niños y adolescentes con TEA. Los equipos y dispositivos de asistencia se dividen en 2 categorías principales, 1) Tecnología baja y 2) Tecnología media-alta. Los resultados de la encuesta revelan una importante brecha de investigación en el uso de TA para combatir el estrés y la ansiedad y la dificultad de que muchas opciones prometedoras (especialmente en el dominio tecnológico medio-alto) sean una solución fácil y rentable para integrarlas en la vida cotidiana. de personas con TEA.Com o desenvolvimento da tecnologia atual e as influências que foram feitas pela Indústria 4.0 utilizando TICs, IoT, sistemas e produtos inteligentes e muitos outros, a Tecnologia Assistiva (TA) é uma parte importante e integrante da vida diária de muitas pessoas que sofrem de deficiência. O Transtorno do Espectro do Autismo (TEA) é uma categoria especial de transtorno que pode se beneficiar muito com seu uso. O objetivo desta pesquisa é coletar dados de Tecnologia Assistiva voltados para a detecção, prevenção e melhora da ansiedade e do estresse (característica que comprovadamente existe e se expressa de diversas formas em pessoas com TEA). Na introdução, são analisadas definições básicas sobre a neurobiologia do estresse e do TEA. Na parte principal, são feitas correlações de TA, estresse e ansiedade com ASD e dispositivos de TA são descritos e documentados em relação ao seu uso para ansiedade e estresse em crianças e adolescentes com TEA. Os equipamentos e dispositivos assistivos são divididos em 2 categorias principais, 1) Low-tech e 2) Mid-High tech. Os resultados da pesquisa revelam uma lacuna significativa de pesquisa no uso de TA para combater o estresse e a ansiedade e a dificuldade de muitas opções promissoras (especialmente no domínio da tecnologia média-alta) serem uma solução fácil e econômica em integrá-las ao cotidiano de pessoas com TEA

Display Enhanced Testing For Concussions And Mild Traumatic Brain Injury

Cognitive assessment systems and methods that provide an integrated solution for evaluating the presence or absence of cognitive impairment. The present invention is used to test cognitive functions of an individual including information processing speed, working memory, work list learning and recall, along with variations of these tasks. Immersive and non-immersive systems and methods are disclosed. Testing and results feedback using the present invention may be completed in real time, typically in less than 15 minutes.Emory UniversityGeorgia Tech Research Corporatio

Exploring Emotion Recognition for VR-EBT Using Deep Learning on a Multimodal Physiological Framework

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality scenes. Machine learning has been used in medical areas such as rare diseases, oncology, medical data classification and psychiatry. This research used a public dataset that contained physiological recordings and emotional responses. The dataset was used to train a deep neural network, and a convolutional neural network to predict an individual’s valence, arousal and dominance. The results presented indicate that the deep neural network had the highest overall mean bounded regression accuracy and the lowest computational time