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Introduction Early detection of cognitive impairments is crucial for the successful implementation of preventive strategies. However, in rural isolated areas or so-called ‘medical deserts’, access to diagnosis and care is very limited. With the current pandemic crisis, now even more than ever, remote solutions such as telemedicine platforms represent great potential and can help to overcome this barrier. Moreover, current advances made in voice and image analysis can help overcome the barrier of physical distance by providing additional information on a patients’ emotional and cognitive state. Therefore, the aim of this study is to evaluate the feasibility and reliability of a videoconference system for remote cognitive testing empowered by automatic speech and video analysis.Methods and analysis 60 participants (aged 55 and older) with and without cognitive impairment will be recruited. A complete neuropsychological assessment including a short clinical interview will be administered in two conditions, once by telemedicine and once by face-to-face. The order of administration procedure will be counterbalanced so half of the sample starts with the videoconference condition and the other half with the face-to-face condition. Acceptability and user experience will be assessed among participants and clinicians in a qualitative and quantitative manner. Speech and video features will be extracted and analysed to obtain additional information on mood and engagement levels. In a subgroup, measurements of stress indicators such as heart rate and skin conductance will be compared.Ethics and dissemination The procedures are not invasive and there are no expected risks or burdens to participants. All participants will be informed that this is an observational study and their consent taken prior to the experiment. Demonstration of the effectiveness of such technology makes it possible to diffuse its use across all rural areas (‘medical deserts’) and thus, to improve the early diagnosis of neurodegenerative pathologies, while providing data crucial for basic research. Results from this study will be published in peer-reviewed journals.

Abstract Introduction The aim of this study was to evaluate the efficacy of a serious exergame in improving the neuropsychiatric symptoms of patients with neurocognitive disorders. Methods X‐Torp is a serious exergame combining motor and cognitive activities. Ninety‐one subjects (mean age = 81.7 years, mean Mini‐Mental State Examination = 18.3) were recruited in 16 centers. Centers were randomized into intervention and control centers. Subjects underwent assessment for cognitive and behavioral symptoms at baseline (BL), the end of the intervention (W12), and 12 weeks after the end of the intervention (W24). Results The comparison of neuropsychiatric symptoms between BL and W12 and W24 showed that subjects of the intervention group improved in apathy between BL and W12. Mixed analysis (time BL, W12, W24 x group) indicated a significant increase in apathy and neuropsychiatric symptoms in the control subjects. Discussion The use of X‐Torp improved neuropsychiatric symptoms, particularly apathy. Future studies should more consistently use behavioral and neuropsychiatric symptoms as outcome measures.

Today, in rural isolated areas or so-called ‘medical deserts’, access to diagnosis and care is very limited. With the current pandemic crisis, now even more than ever, telemedicine platforms are gradually more employed for remote medical assessment. Only a few are tailored to comprehensive teleneuropsychological assessment of older adults. Hence, our study focuses on evaluating the feasibility of performing a remote neuropsychological assessment of older adults suffering from a cognitive complaint. 50 participants (aged 55 and older) were recruited at the local hospital of Digne-les-Bains, France. A brief neuropsychological assessment including a short clinical interview and several validated neuropsychological tests was administered in two conditions, once by Teleneuropsychology (TNP) and once by Face-to-Face (FTF) in a crossover design. Acceptability and user experience was assessed through questionnaires. Results show high agreement in most tests between the FTF and TNP conditions. The TNP was overall well accepted by the participants. However, differences in test performances were observed, which urges the need to validate TNP tests with broader samples with normative data.

Background: At present, the assessment of autonomy in daily living activities, one of the key symptoms in Alzheimer’s disease (AD), involves clinical rating scales. Methods: In total, 109 participants were included. In particular, 11 participants during a pre-test in Nice, France, and 98 participants (27 AD, 38 mild cognitive impairment—MCI—and 33 healthy controls—HC) in Thessaloniki, Greece, carried out a standardized scenario consisting of several instrumental activities of daily living (IADLs), such as making a phone call or preparing a pillbox while being recorded. Data were processed by a platform of video signal analysis in order to extract kinematic parameters, detecting activities undertaken by the participant. Results: The video analysis data can be used to assess IADL task quality and provide clinicians with objective measurements of the patients’ performance. Furthermore, it reveals that the HC statistically significantly outperformed the MCI, which had better performance compared to the AD participants. Conclusions: Accurate activity recognition data for the analyses of the performance on IADL activities were obtained.

The present year witnesses another milestone in Pedestrian detection's journey. It has achieved remarkable progress in the course of the past 15 years, and experts foresee an everyday use of numerous stemming applications within the next 15 years. Standing on the tipping point between yesterday and tomorrow pushes to the field's retrospect. Features, context, the combination of approaches, and feature data are responsible for most of the breakthroughs in pedestrian detection. If the first three elements cover the bulk of the literature, much fewer efforts have been dedicated to featuring data. In many aspects, the construction of a training set remains similar to what it was at the birth of the domain, and some related problems are not well studied and sometimes still tackled empirically. This paper gets down to the study of pedestrian classifier training conditions. More than a survey of existing training classifiers or features, our goal is to highlight impactful parameters, potential new research directions, and combination dilemmas. Our findings are experimentally verified on two major datasets: the INRIA and Caltech-USA datasets.

This paper tackles the problem of data selection for training set generation in the context of near real-time pedestrian detection through the introduction of a training methodology: FairTrain. After highlighting the impact of poorly chosen data on detector performance, we introduce a new data selection technique utilizing the expectation-maximization algorithm for data weighting. FairTrain also features a version of the cascade-of-rejectors enhanced with data selection principles. Experiments on the INRIA and CALTECH data sets prove that, when finely trained, a simple HoG-based detector can outperform most of its near real-time competitors.

In this study, the authors propose a complete framework based on a hierarchical activity model to understand and recognise activities of daily living in unstructured scenes. At each particular time of a long‐time video, the framework extracts a set of space‐time trajectory features describing the global position of an observed person and the motion of his/her body parts. Human motion information is gathered in a new feature that the authors call perceptual feature chunks (PFCs). The set of PFCs is used to learn, in an unsupervised way, particular regions of the scene (topology) where the important activities occur. Using topologies and PFCs, the video is broken into a set of small events (‘primitive events’) that have a semantic meaning. The sequences of ‘primitive events’ and topologies are used to construct hierarchical models for activities. The proposed approach has been tested with the medical field application to monitor patients suffering from Alzheimer's and dementia. The authors have compared their approach to their previous study and a rule‐based approach. Experimental results show that the framework achieves better performance than existing works and has the potential to be used as a monitoring tool in medical field applications.

The use of Serious Games (SG) in the health domain is expanding. In the field of neurodegenerative disorders (ND) such as Alzheimer’s disease, SG are currently employed both to support and improve the assessment of different functional and cognitive abilities, and to provide alternative solutions for patients’ treatment, stimulation, and rehabilitation. As the field is quite young, recommendations on the use of SG in people with ND are still rare. In 2014 we proposed some initial recommendations (Robert et al., 2014). The aim of the present work was to update them, thanks to opinions gathered by experts in the field during an expert Delphi panel. Results confirmed that SG are adapted to elderly people with mild cognitive impairment (MCI) and dementia, and can be employed for several purposes, including assessment, stimulation, and improving wellbeing, with some differences depending on the population (e.g., physical stimulation may be better suited for people with MCI). SG are more adapted for use with trained caregivers (both at home and in clinical settings), with a frequency ranging from 2 to 4 times a week. Importantly, the target of SG, their frequency of use and the context in which they are played depend on the SG typology (e.g., Exergame, cognitive game), and should be personalized with the help of a clinician.

Automatic detection and analysis of human activities captured by various sensors (e.g., sequences of images captured by RGB camera) play an essential role in various research fields in order to understand the semantic content of a captured scene. The main focus of the earlier studies has been widely on supervised classification problem, where a label is assigned to a given short clip. Nevertheless, in real-world scenarios, such as in Activities of Daily Living (ADL), the challenge is to automatically browse long-term (days and weeks) stream of videos to identify segments with semantics corresponding to the model activities and their temporal boundaries. This paper proposes an unsupervised solution to address this problem by generating hierarchical models that combine global trajectory information with local dynamics of the human body. Global information helps in modeling the spatiotemporal evolution of long-term activities, hence, their spatial and temporal localization. Moreover, the local dynamic information incorporates complex local motion patterns of daily activities into the models. Our proposed method is evaluated using realistic datasets captured from observation rooms in hospitals and nursing homes. The experimental data on a variety of monitoring scenarios in hospital settings reveals how this framework can be exploited to provide timely diagnose and medical interventions for cognitive disorders, such as Alzheimer’s disease. The obtained results show that our framework is a promising attempt capable of generating activity models without any supervision.