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Falls are considered a significant cause of disability, pain, and premature deaths in older adults, often due to sedentary lifestyles and various risk factors. Combining immersive virtual reality (IVR) with physical exercise, or exergames, enhances motivation and personalizes training, effectively preventing falls by improving strength and balance in older people. IVR technology may increase the ecological validity of the assessments. The main goal of our study was to assess the feasibility of using a KAVE-based VR platform combining simulations of Levadas and a cable car to perform a balanced assessment and profiling of the older adult population for high risk of falls and the related user experience. A VR-based platform using a Wii balance board and a CAVE was developed to assess balance and physical fitness. Validated by the Biodex Balance System (BBS), 25 older adults participated in this study. The usability and presence were measured through the System Usability Scale and ITC-SOPI questionnaires, respectively. The IVR system showed a high presence and a good usability score of 75. Significant effects were found in the maximum excursion of the centre of pressure (COP) on the anterior–posterior axis during the cable car simulation (CCS), correlating with BBS metrics. Multiple discriminative analysis models and the support vector machine classified fall risk with moderate to high accuracy, precision, and recall. The system accurately identified all high-risk participants using the leave-one-out method. This study suggests that an IVR-based platform based on simulations with high ecological validity can be used to assess physical fitness and identify individuals at a higher risk of falls.

Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack adaptability, offering one-size-fits-all solutions that may not effectively meet each patient's unique needs. Furthermore, the resource-intensive nature of these interventions, often confined to clinical settings, poses barriers to widespread, cost-effective, and sustained implementation, resulting in suboptimal outcomes in terms of intervention adaptability, intensity, and duration. In response to these challenges, this paper introduces NeuroAIreh@b, an innovative cognitive profiling and training methodology that uses an AI-driven framework to optimize neurorehabilitation prescription. NeuroAIreh@b effectively bridges the gap between neuropsychological assessment and computational modeling, thereby affording highly personalized and adaptive neurorehabilitation sessions. This approach also leverages virtual reality-based simulations of daily living activities to enhance ecological validity and efficacy. The feasibility of NeuroAIreh@b has already been demonstrated through a clinical study with stroke patients employing a tablet-based intervention. The NeuroAIreh@b methodology holds the potential for efficacy studies in large randomized controlled trials in the future.

PurposeA systematic review was conducted to examine the state of the literature regarding using ecologically valid virtual environments and related technologies to assess and rehabilitate people with Acquired Brain Injury (ABI).Materials and methodsA literature search was performed following the PRISMA guidelines using PubMed, Web of Science, ACM and IEEE databases. The focus was on assessment and intervention studies using ecologically valid virtual environments (VE). All studies were included if they involved individuals with ABI and simulated environments of the real world or Activities of Daily Living (ADL).ResultsSeventy out of 363 studies were included in this review and grouped and analyzed according to the nature of its simulation, prefacing a total of 12 kitchens, 11 supermarkets, 10 shopping malls, 16 streets, 11 cities, and 10 other everyday life scenarios. These VE were mostly presented on computer screens, HMD’s and laptops and patients interacted with them primarily via mouse, keyboard, and joystick. Twenty-five out of 70 studies had a non-experimental design.ConclusionEvidence about the clinical impact of ecologically valid VE is still modest, and further research with more extensive samples is needed. It is important to standardize neuropsychological and motor outcome measures to strengthen conclusions between studies.Systematic review registrationidentifier CRD42022301560, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=301560.

The areas of entertainment computing and persuasive technologies are interdisciplinary fields that have gained increasing attention in the last decades. Health is one of the domains that has leveraged the benefits of fun to improve the results of its technology-enabled interventions. Previous work has reviewed the area of health entertainment from many different perspectives; however, an integrative analysis across disciplines (health sciences and computer science and engineering) throughout the development and validation cycle of technologies in this domain is missing. Having such an in-depth retrospective analysis would shed light on how research on entertainment computing and persuasive technologies for health has evolved, acknowledging its contributions, recognizing its strengths and limitations, and, as a result, allowing for the definition of ways forward. This paper engages in an unprecedented systematic review of the work produced between 2004 and 2017 in this area. From an initial total of 10,350 retrieved results, a total of 1,307 full-texts were included in this review and were thoroughly examined to gain a retrospective understanding of the type of studies that have been produced. Among others, this systematic review reports on the trends, venues of publication, and the characteristics of the studies including methodologies, sample characteristics, study design, the type of solutions produced, the conditions and domains of application, and the purpose of the studies. Results show that there is a growing body of research in the area, with most studies being published in roughly the same venues, and where the lion's share of solutions fall into the area of health rehabilitation and motor conditions. With regards to the most addressed health domains, our review shows that most solutions produced are aimed at stroke and fitness, followed by balance training. Most studies (82.3%) are conducted with their target population, mostly adults (18–49 y), and are conducted either in the lab or in clinical settings. However, the median sample size of the studies has remained stable (N = 20) in the last decades. Regarding technology, 2D and 3D solutions are equally used, and most systems employ movement sensors and are single-user. Finally, only 21.4% of the studies are performed using validated instruments.

Abstract Background Serious games are promising for stroke rehabilitation, with studies showing a positive impact on reducing motor and cognitive deficits. However, most of the evidence is in the context of single-user rehabilitation, and little is known concerning the impact in multi-user settings. This study evaluates the impact that different game modes can have on engagement and social involvement during a two-user game. Specifically, we want to understand the benefits of game modalities based on competition, co-activation, and collaboration and analyze the influence of different motor and cognitive deficits and personality traits. Methods We developed a two-player setup—using tangible objects and a large screen interactive table—for upper limb rehabilitation purposes. We implemented a game that, while keeping the same basic mechanics, can be played in the three different modes (Competitive, Co-active, and Collaborative). We ran a within-person randomized study with 21 stroke survivors that were paired and played the game in its three versions. We used the Game Experience Questionnaire—Core Module to assess engagement and the Social Presence Module to assess Social Involvement. For personality, motor, and cognitive function, users answered the International Personality Item Pool (short version), Fugl-Meyer Assessment—Upper Extremity, Modified Ashworth Scale, and Montreal Cognitive Assessment, respectively. Results The Collaborative mode promoted significantly more Behavioral Involvement. The Competitive mode promoted more Flow and Challenge than the Co-active mode with participants with better cognitive performance, with low extraversion, or with higher motor skills. Participants with higher cognitive deficits reported more Competence with the Co-active mode. Conclusions Our results indicate that, for multi-user motor rehabilitation settings, the collaborative mode is the more appropriate gaming approach to promote social involvement, showing a high potential for increasing adherence and effectiveness of therapy. Additionally, we show that a player's motor and cognitive ability and personality should be considered when designing personalized tasks for multiplayer settings.

Standardized Emotion Elicitation Databases (SEEDs) allow studying emotions in laboratory settings by replicating real-life emotions in a controlled environment. The International Affective Pictures System (IAPS), containing 1182 coloured images as stimuli, is arguably the most popular SEED. Since its introduction, multiple countries and cultures have validated this SEED, making its adoption on the study of emotion a worldwide success. For this review, 69 studies were included. Results focus on the discussion of validation processes by combining self-report and physiological data (Skin Conductance Level, Heart Rate Variability and Electroencephalography) and self-report only. Cross-age, cross-cultural and sex differences are discussed. Overall, IAPS is a robust instrument for emotion elicitation around the world.

Abstract Background Paper-and-pencil tasks are still widely used for cognitive rehabilitation despite the proliferation of new computer-based methods, like VR-based simulations of ADL’s. Studies have established construct validity of VR assessment tools with their paper-and-pencil version by demonstrating significant associations with their traditional construct-driven measures. However, VR rehabilitation intervention tools are mostly developed to include mechanisms such as personalization and adaptation, elements that are disregarded in their paper-and-pencil counterparts, which is a strong limitation of comparison studies. Here we compare the clinical impact of a personalized and adapted paper-and-pencil training and a content equivalent and more ecologically valid VR-based ADL’s simulation. Methods We have performed a trial with 36 stroke patients comparing Reh@City v2.0 (adaptive cognitive training through everyday tasks VR simulations) with Task Generator (TG: content equivalent and adaptive paper-and-pencil training). The intervention comprised 12 sessions, with a neuropsychological assessment pre, post-intervention and follow-up, having as primary outcomes: general cognitive functioning (assessed by the Montreal Cognitive Assessment - MoCA), attention, memory, executive functions and language specific domains. Results A within-group analysis revealed that the Reh@City v2.0 improved general cognitive functioning, attention, visuospatial ability and executive functions. These improvements generalized to verbal memory, processing speed and self-perceived cognitive deficits specific assessments. TG only improved in orientation domain on the MoCA, and specific processing speed and verbal memory outcomes. However, at follow-up, processing speed and verbal memory improvements were maintained, and a new one was revealed in language. A between-groups analysis revealed Reh@City v2.0 superiority in general cognitive functioning, visuospatial ability, and executive functions on the MoCA. Conclusions The Reh@City v2.0 intervention with higher ecological validity revealed higher effectiveness with improvements in different cognitive domains and self-perceived cognitive deficits in everyday life, and the TG intervention retained fewer cognitive gains for longer. Trial registration The trial is registered at ClinicalTrials.gov, number NCT02857803 . Registered 5 August 2016, .

(1) Background: Different intervention strategies, such as reminiscence and music-based therapies, effectively mitigate the cognitive decline of individuals with dementia (IwD). An important challenge when implementing these approaches lies in the need for content customization. We designed Musiquence platform, a framework that allows the customization of music and reminiscence content in cognitive stimulation activities. (2) Methods: Here, we present a participatory design methodology for designing a seven-week customized cognitive stimulation program (CCSP) for IwD using Musiquence. We ran focus groups with 19 formal caregivers, who provided information regarding themes and music to be used in activities for IwD, and gathered preferences from 20 IwD. (3) Results: The CCSP consists of seven cycles of tasks combining the activities available in Musiquence, the seven preferential themes of IwD, and the seven songs most frequently referenced by formal caregivers. (4) Conclusions: Finally, we provide a set of guidelines to implement a participatory design approach for content development together with IwD.

Abstract Background Serious games have been increasingly used for motor rehabilitation. However, it is not well known how different game features can be used to impact specific skills properly. Here, we study how the mode (competitive, co-active, collaborative) in which a multi-user game is presented impacts engagement and social involvement. Methods We collected data from 20 pairs of community-dwelling older adults (71.5 ± 8.7 years) in a study following a within-persons design. The participants performed a two-player upper limb rehabilitation game with three conditions (Competitive, Co-active, and Collaborative modes). Engagement and social involvement were assessed through the Core Module and Social Presence Module, respectively, from the Game Experience Questionnaire. To infer the impact of personality and cognitive function, users answered the International Personality Item Pool (short version) and the Montreal Cognitive Assessment, respectively. Results Results show that the Collaborative game mode promotes more social involvement when compared to Competitive and Co-active modes. This result is mostly explained by those participants with higher cognitive skills, and those that are more extrovert. Extrovert participants feel more empathy and are behaviorally more involved when playing the Collaborative mode. Also, the Collaborative mode is shown to be appropriate to promote interaction with participants that previously had a distant relationship, while the Competitive mode seems to be more beneficial to promote empathy between players with a closer relationship. Conclusions The Collaborative game mode elicited significantly higher social involvement in terms of Empathy, Positive Affect, and Behavioral Involvement. Hence, this game mode seems to be the most adequate choice to be used in multiplayer rehabilitation settings, where social interaction is intended.

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.

Music is frequently regarded as a unique way to connect with dementia patients. Yet little is known about how persons with dementia respond emotionally to music. Are their responses different from those of healthy listeners? If so, why? The present study makes a first attempt to tackle these issues in a Portuguese context, with a focus on psychological mechanisms. In Experiment 1, featuring 20 young and healthy adults, we found that musical excerpts which have previously been shown to activate specific emotion induction mechanisms (brain stem reflex, contagion, episodic memory, musical expectancy) in Sweden were valid and yielded predicted emotions also in Portugal, as indexed by self-reported feelings, psychophysiology, and post hoc mechanism indices. In Experiment 2, we used the same stimuli to compare the responses of 20 elderly listeners diagnosed with Alzheimer’s disease (AD) with those of 20 healthy listeners. We controlled for cognitive functioning (Mini-Mental State Examination) and depression (Geriatric Depression Scale). Our predictions about how mechanisms would be differentially affected by decline in brain regions associated with AD received support in that AD patients reported significantly lower levels of (a) sadness in the contagion condition, (b) happiness and nostalgia in the episodic memory condition, and (c) anxiety in the musical expectancy condition. By contrast, no significant difference in reported surprise was found in the brain stem reflex condition. Implications for musical interventions aimed at dementia are discussed, highlighting the key role that basic research may play in developing applications.

Background and Objectives: The motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico-spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere. Mental representation techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind, randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain–Computer Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI), dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention, after each intervention and 15 days after the last session. Conclusions: This trial will show the additive value of VR immersive motor imagery as an adjuvant therapy combined with a known effective neuromodulation approach opening new perspectives for clinical rehabilitation protocols.

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Stroke is one of the most common causes of acquired disability, leaving numerous adults with cognitive and motor impairments, and affecting patients’ capability to live independently. Virtual Reality (VR) based methods for stroke rehabilitation have mainly focused on motor rehabilitation but there is increasing interest toward the integration of cognitive training for providing more effective solutions. Here we investigate the feasibility for stroke recovery of a virtual cognitive-motor task, the Reh@Task, which combines adapted arm reaching, and attention and memory training. 24 participants in the chronic stage of stroke, with cognitive and motor deficits, were allocated to one of two groups (VR, Control). Both groups were enrolled in conventional occupational therapy, which mostly involves motor training. Additionally, the VR group underwent training with the Reh@Task and the control group performed time-matched conventional occupational therapy. Motor and cognitive competences were assessed at baseline, end of treatment (1 month) and at a 1-month follow-up through the Montreal Cognitive Assessment, Single Letter Cancelation, Digit Cancelation, Bells Test, Fugl-Meyer Assessment Test, Chedoke Arm and Hand Activity Inventory, Modified Ashworth Scale, and Barthel Index. Our results show that both groups improved in motor function over time, but the Reh@Task group displayed significantly higher between-group outcomes in the arm subpart of the Fugl-Meyer Assessment Test. Improvements in cognitive function were significant and similar in both groups. Overall, these results are supportive of the viability of VR tools that combine motor and cognitive training, such as the Reh@Task. Trial Registration: This trial was not registered because it is a small clinical study that addresses the feasibility of a prototype device.

After stroke, enduring rehabilitation is required for maximum recovery, and ideally throughout life to prevent functional deterioration. Hence we developed a new concept for at-home low-cost motor rehabilitation, the NTT, an Internet-based interactive system for upper-limb rehabilitation. In this paper we present the NTT design concepts, its implementation and a proof of concept study with 10 healthy participants. The NTT brings together concepts of optimal learning, engagement, and storytelling to deliver a personalized training to its users. In this study we evaluate the feasibility of NTT as a tool capable of automatically assessing and adapting to its user. This is achieved by means of a psychometric study where we show that the NTT is able to assess movement kinematics—movement smoothness, range of motion, arm displacement and arm coordination—in healthy users. Subsequently, a modeling approach is presented to understand how the measured movement kinematics relate to training parameters, and how these can be modified to adapt the training to meet the needs of patients. Finally, an adaptive algorithm for the personalization of training considering motivational and performance aspects is proposed. In the next phase we will deploy and evaluate the NTT with stroke patients at their homes.

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.

Antipersonnel mines, weapons of cheap manufacture but lethal effect, have a high impact on the population even decades after the conflicts have finished. Here we investigate the use of a chemo-sensing Unmanned Aerial Vehicle (cUAV) for demining tasks. We developed a blimp based UAV that is equipped with a broadly tuned metal-thin oxide chemo-sensor. A number of chemical mapping strategies were investigated including two biologically based localization strategies derived from the moth chemical search that can optimize the efficiency of the detection and localization of explosives and therefore be used in the demining process. Additionally, we developed a control layer that allows for both fully autonomous and manual controlled flight, as well as for the scheduling of a fleet of cUAVs. Our results confirm the feasibility of this technology for demining in real-world scenarios and give further support to a biologically based approach where the understanding of biological systems is used to solve difficult engineering problems.