Background: Smart Aging is a Serious games (SGs) platform in a 3D virtual environment in which users perform a set of screening tests that address various cognitive skills. The tests are structured as 5 tasks of activities of daily life in a familiar environment. The main goal of the present study is to compare a cognitive evaluation made with Smart Aging with those of a classic standardized screening test, the Montreal Cognitive Assessment (MoCA).Methods: One thousand one-hundred thirty-one healthy adults aged between 50 and 80 (M = 64.3 ± 8.3) were enrolled in the study. They received a cognitive evaluation with the MoCA and the Smart Aging platform. Participants were grouped according to their MoCA global and specific cognitive domain (i.e., memory, executive functions, working memory, visual spatial elaboration, language, and orientation) scores and we explored differences among these groups in the Smart Aging indices.Results: One thousand eighty-six older adults (M = 64.0 ± 8.0) successfully completed the study and were stratified according to their MoCA score: Group 1 with MoCA < 27 (n = 360); Group 2 with 27 ≥ MoCA < 29 (n = 453); and Group 3 with MoCA ≥ 29 (n = 273). MoCA groups significantly differed in most of the Smart Aging indices considered, in particular as concerns accuracy (ps < 0.001) and time (ps < 0.001) for completing most of the platform tasks. Group 1 was outperformed by the other two Groups and was slower than them in these tasks, which were those supposed to assess memory and executive functions. In addition, significant differences across groups also emerged when considering the single cognitive domains of the MoCA and the corresponding performances in each Smart Aging task. In particular, this platform seems to be a good proxy for assessing memory, executive functions, working memory, and visual spatial processes.Conclusion: These findings demonstrate the validity of Smart Aging for assessing cognitive functions in normal aging. Future studies will validate this platform also in the clinical aging populations.
Background: Smart Aging is a serious game (SG) platform that generates a 3D virtual reality environment in which users perform a set of screening tasks designed to allow evaluation of global cognition. Each task replicates activities of daily living performed in a familiar environment. The main goal of the present study was to ascertain whether Smart Aging could differentiate between different types and levels of cognitive impairment in patients with neurodegenerative disease.Methods: Ninety-one subjects (mean age = 70.29 ± 7.70 years)—healthy older adults (HCs, n = 23), patients with single-domain amnesic mild cognitive impairment (aMCI, n = 23), patients with single-domain executive Parkinson's disease MCI (PD-MCI, n = 20), and patients with mild Alzheimer's disease (mild AD, n = 25)—were enrolled in the study. All participants underwent cognitive evaluations performed using both traditional neuropsychological assessment tools, including the Mini-Mental State Examination (MMSE), Montreal Overall Cognitive Assessment (MoCA), and the Smart Aging platform. We analyzed global scores on Smart Aging indices (i.e., accuracy, time, distance) as well as the Smart Aging total score, looking for differences between the four groups.Results: The findings revealed significant between-group differences in all the Smart Aging indices: accuracy (p < 0.001), time (p < 0.001), distance (p < 0.001), and total Smart Aging score (p < 0.001). The HCs outperformed the mild AD, aMCI, and PD-MCI patients in terms of accuracy, time, distance, and Smart Aging total score. In addition, the mild AD group was outperformed both by the HCs and by the aMCI and PD-MCI patients on accuracy and distance. No significant differences were found between aMCI and PD-MCI patients. Finally, the Smart Aging scores significantly correlated with the results of the neuropsychological assessments used.Conclusion: These findings, although preliminary due to the small sample size, suggest the validity of Smart Aging as a screening tool for the detection of cognitive impairment in patients with neurodegenerative diseases.
Objective: To evaluate whether introducing gamification in BCI rehabilitation of the upper limbs of post-stroke patients has a positive impact on their experience without altering their efficacy in creating motor mental images (MI). Design: A game was designed purposely adapted to the pace and goals of an established BCI-rehabilitation protocol. Rehabilitation was based on a double feedback: functional electrostimulation and animation of a virtual avatar of the patient's limbs. The game introduced a narrative on top of this visual feedback with an external goal to achieve (protecting bits of cheese from a rat character). A pilot study was performed with 10 patients and a control group of six volunteers. Two rehabilitation sessions were done, each made up of one stage of calibration and two training stages, some stages with the game and others without. The accuracy of the classification computed was taken as a measure to compare the efficacy of MI. Users' opinions were gathered through a questionnaire. No potentially identifiable human images or data are presented in this study. Results: The gamified rehabilitation presented in the pilot study does not impact on the efficacy of MI, but it improves users experience making it more fun. Conclusion: These preliminary results are encouraging to continue investigating how game narratives can be introduced in BCI rehabilitation to make it more gratifying and engaging.
The paper presents a 3D Virtual Environment (VE)\ud for neurorehabilitation of the upper limb. Patients move one\ud of their arms trying to simulate concrete daily actions, such\ud as grasping a bottle, opening a door or putting a book on a\ud shelve. They wear a special garment that integrates four inertial\ud sensors providing in real time information on the orientation of\ud the patient’s shoulder, elbow and wrist. The VE is a complete\ud scenario integrating all the objects needed to perform virtually\ud the actions simulated by the patients. In the VE, patients are\ud represented by 3D avatars that, using the data provided by the\ud inertial sensors, reproduce in real time their arm movements.\ud Since only the arm movement is monitorized, but neither the\ud hand nor the trunk and the neck, the system must combine real\ud movements with baked animations in order to show a realistic\ud behavior of the 3D avatar. Moreover, it must take into account\ud collisions between the 3D avatar and the virtual objects. Finally,\ud it must detect when the patient is about to simulate an interaction\ud with an object in order to realize it virtually. We describe the\ud strategies that we have designed to provide these functionalities.Peer ReviewedPostprint (published version
Many different direct volume rendering methods have been developed to visualize 3D scalar fields on uniform rectilinear grids. However, little work has been done on rendering simultaneously various properties of the same 3D region measured with different registration devices or at different instants of time. The demand for this type of visualization is rapidly increasing in scientific applications such as medicine in which the visual integration of multiple modalities allows a better comprehension of the anatomy and a perception of its relationships with activity. This paper presents different strategies of direct multimodal volume rendering (DMVR). It is restricted to voxel models with a known 3D rigid alignment transformation. The paper evaluates at which steps of the rendering pipeline the data fusion must be realized in order to accomplish the desired visual integration and to provide fast re-renders when some fusion parameters are modified. In addition, it analyses how existing monomodal visualization algorithms can be extended to multiple datasets and it compares their efficiency and their computational cost.
Finite elements methods for radiosity are aimed at computing global illumination solutions efficiently. However these methods are not suitable for obtaining high quality images due to the lack of error control. Two-pass methods allow to achieve that level of quality computing illumination at each pixel and thus introducing a high computing overhead. We present a two-pass method for radiosity that allows to produce high quality images avoiding most of the per-pixel computations. The method computes a coarse hierarchical radiosity solution and then performs a second pass using current graphics hardware accelerators to generate illumination as high definition textures.
On the basis of this experiment we believe that 3D gamified simulations can be efficient tools to train social and professional skills of persons with intellectual disabilities contributing thus to foster their social inclusion through work.
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