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BACKGROUND: Involving users has become a prominent principle in the development of Health Information Technologies (HIT) and has led to an uprise in agile and cocreation methods. Previous literature shows how the two can be combined in one method, but also suggest that using such a method may come with challenges, for which the solutions are unclear. PURPOSE: To identify the challenges of using a method that combines agile and cocreation, provide solutions for these challenges, and evaluate its usage. METHODS AND MATERIALS: The setting for this research was the Agile Cocreation of Robots for Aging (ACCRA) project. The research consisted of three phases: 1) evaluating the Agile Cocreation method to identify challenges in its usage, by analysing documents from the project (e-mails, meeting notes), 2) solving the challenges to improve the method, by designing solutions in a cocreation session; and 3) evaluating the usage of the improved version via a survey among engineers and user researchers involved in the project. RESULTS: We identified three main challenges and developed three solutions, which were used in the next phase of the project. First, to engage all stakeholders in cocreation, we implemented more fun and playful materials. Second, to bridge the differences between engineers and user researchers we invested in face-to-face meetings. Third, to manage knowledge in the project we intensified our meeting schedule to weekly meetings. In the quantitative evaluation of the improved cocreation method, the engineers and user researchers were positive about the agile cocreation method and about our improvements. CONCLUSION: When developing HIT, a method that combines agile and cocreation is useful because it helps to identify user needs and to translate these needs into technology. To identify the needs of these users and other stakeholders it is important to involve them as active partners in cocreation using fun and playful materials. Engineers and user researchers should bridge their differences and meet face-to-face as much as possible.

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BACKGROUND: Social robots are becoming increasingly important as companions in our daily lives. Consequently, humans expect to interact with them using the same mental models applied to human-human interactions, including the use of cospeech gestures. Research efforts have been devoted to understanding users' needs and developing robot's behavioral models that can perceive the user state and properly plan a reaction. Despite the efforts made, some challenges regarding the effect of robot embodiment and behavior in the perception of emotions remain open. OBJECTIVE: The aim of this study is dual. First, it aims to assess the role of the robot's cospeech gestures and embodiment in the user's perceived emotions in terms of valence (stimulus pleasantness), arousal (intensity of evoked emotion), and dominance (degree of control exerted by the stimulus). Second, it aims to evaluate the robot's accuracy in identifying positive, negative, and neutral emotions displayed by interacting humans using 3 supervised machine learning algorithms: support vector machine, random forest, and K-nearest neighbor. METHODS: Pepper robot was used to elicit the 3 emotions in humans using a set of 60 images retrieved from a standardized database. In particular, 2 experimental conditions for emotion elicitation were performed with Pepper robot: with a static behavior or with a robot that expresses coherent (COH) cospeech behavior. Furthermore, to evaluate the role of the robot embodiment, the third elicitation was performed by asking the participant to interact with a PC, where a graphical interface showed the same images. Each participant was requested to undergo only 1 of the 3 experimental conditions. RESULTS: A total of 60 participants were recruited for this study, 20 for each experimental condition for a total of 3600 interactions. The results showed significant differences (P<.05) in valence, arousal, and dominance when stimulated with the Pepper robot behaving COH with respect to the PC condition, thus underlying the importance of the robot's nonverbal communication and embodiment. A higher valence score was obtained for the elicitation of the robot (COH and robot with static behavior) with respect to the PC. For emotion recognition, the K-nearest neighbor classifiers achieved the best accuracy results. In particular, the COH modality achieved the highest level of accuracy (0.97) when compared with the static behavior and PC elicitations (0.88 and 0.94, respectively). CONCLUSIONS: The results suggest that the use of multimodal communication channels, such as cospeech and visual channels, as in the COH modality, may improve the recognition accuracy of the user's emotional state and can reinforce the perceived emotion. Future studies should investigate the effect of age, culture, and cognitive profile on the emotion perception and recognition going beyond the limitation of this work.

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50% of older adults over 60 years old are experiencing social isolation. Assistive technology can provide solutions that promote the connection with their families and other stakeholders. In this context, this paper presents a pilot study of a socialization service with 3 functions tested by 10 older adults and 10 informal caregivers. After a short training, participants were requested to use the system in their daily life for six months. At the beginning (T0) and at the end (T6) of the trial, qualitative structured questionnaires were addressed to investigate training, usability, acceptance (i.e. trust, anxiety, facilitating condition, enjoyment, and attitude), and user experience. Collected results underline good training, good usability of the system (SUS>68), and user experience for both groups. Furthermore, the values associated with acceptance domains are higher than 3.5 for intention to use and trust, at the end of the tests. We can observe a decreasing trend in stress associated with technology use. Informal caregivers have a higher evaluation of the system novelty with respect to older adults. Overall, qualitative feedback collected remarked the good impression of this service among the study's participants. Finally, this study represents a promising starting point for better investigating technology-based services that can improve the quality of life of older people living alone providing them with tools that can decrease their social isolation.Clinical Relevance- The results suggest the potential use of this type of service for promoting socialization among older adults thus reducing their loneliness.

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Physical therapy is strongly recommended for patients with neurological disorders. Tai Chi-based treatments seem to improve physical functions like gait speed and balance. However, assessments after treatment rely on semi-quantitative clinical scales affected by subjectivity with controversial results. This study aims at investigating whether Tai Chi could be a valid alternative to traditional physiotherapy rehabilitation. We propose a wearable system composed of two inertial devices able to objectively measure the effect of the rehabilitation treatment on the range of movement of the trunk. Seventeen patients with Parkinson's Disease (PD) were recruited and assessed. They have been randomly divided into two groups: group 1 followed a Tai Chi-based treatment, while group 2 underwent a traditional physiotherapy rehabilitation. The two groups have been assessed before (t0) and after the treatment (t1). No statistical differences have been found in the relative range of motion between the upper and lower sensors between the two groups at the baseline. Both treatments resulted in a significant improvement in the trunk range of movement (on the right side). Notably, the improvement in the effect size of the treatment was greater in group 1 than in group 2. In fact, even if both the groups benefited from their treatment group 1 gained larger mobility of the trunk if compared to group 2. Interestingly, no differences have been accounted adopting the traditional UPDRS III for motor symptoms of PD, strengthening the idea that objective measurement coming from wearable biomedical sensors could detect information otherwise neglected by traditional clinical tools.Clinical Relevance- This study preliminary confirms that beneficial motor effects after a Tai Chi rehabilitation program are comparable and quite better than after traditional physiotherapy, promoting Tai Chi as a valid alternative treatment for PD patients.

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OBJECTIVE: This study investigates the possibility of adopting motor and cognitive dual-task (MCDT) approaches to identify subjects with mild cognitive impairment (MCI) and subjective cognitive impairment (SCI). METHODS: The upper and lower motor performances of 44 older adults were assessed using the SensHand and SensFoot wearable system during three MCDTs: forefinger tapping (FTAP), toe-tapping heel pin (TTHP), and walking 10 m (GAIT). We developed five pooled indices (PIs) based on these MCDTs, and we included them, along with demographic data (age) and clinical scores (Frontal Assessment Battery (FAB) scores), in five logistic regression models. RESULTS: Models which consider cognitively normal adult (CNA) vs MCI subjects have accuracies that range from 67% to 78%. The addition of clinical scores stabilised the accuracies, which ranged from 85% to 89%. For models which consider CNA vs SCI vs MCI subjects, there are great benefits to considering all three regressors (age, FAB score, and PIs); the overall accuracies of the three-class models range between 50% and 59% when just PIs and age are considered, whereas the overall accuracy increases by 18% when all three regressors are utilised. CONCLUSION: Logistic regression models that consider MCDT PIs and age have been effective in distinguishing between CNA and MCI subjects. The inclusion of clinical scores increased the models' accuracy. Particularly high performances in distinguishing among CNA, SCI, and MCI subjects were obtained by the TTHP PI. This study suggests that a broader framework for MCDTs, which should encompass a greater selection of motor tasks, could provide clinicians with new appropriate tools.

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Assistive devices could promote independent living and support the active and healthy aging of an older population; however, several factors can badly influence the long-term use of new technologies. In this context, this paper presents a two-step methodology called "pre-validation" that aims to identify the factors that can bias the use of new services, thus minimizing the risk of an unsuccessful longer trial. The proposed pre-validation methodology is composed of two main phases that aim to assess the usability and the reliability of the technology assessed in a laboratory environment and the usability, acceptability, user experience, and reliability of the technology in real environments. The tested services include the socialization scenario, in which older adults are better connected to the community via technological solutions (i.e., socialization applications), and the monitoring scenario, which allows for the introduction of timely interventions (technologies involved include environmental monitoring sensors, a telepresence robot, wearable sensors, and a personalized dashboard). The obtained results underline an acceptable usability level (average System Usability Scale score > 65) for the tested technologies (i.e., socialization applications and a telepresence robot). Phase Two also underlines the good acceptability, user experience, and usability of the tested services. The statistical analysis underlines a correlation between the stress related to the use of technology, digital skills, and intention of use, among other factors. Qualitative feedback also remarks on a correlation between older adults with low digital skills and an anxiety about using technology. Positive correlation indexes were highlighted between the trust and usability scores. Eventually, future long-term trials with assistive technology should rely on motivated caregivers, be founded on a strong recruitment process, and should reassure older adults­especially the ones with low digital literacy­about the use of technology by proposing personalized training and mentoring, if necessary, to increase the trust.

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Social companion robots are getting more attention to assist elderly people to stay independent at home and to decrease their social isolation. When developing solutions, one remaining challenge is to design the right applications that are usable by elderly people. For this purpose, co-creation methodologies involving multiple stakeholders and a multidisciplinary researcher team (e.g., elderly people, medical professionals, and computer scientists such as roboticists or IoT engineers) are designed within the ACCRA (Agile Co-Creation of Robots for Ageing) project. This paper will address this research question: How can Internet of Robotic Things (IoRT) technology and co-creation methodologies help to design emotional-based robotic applications? This is supported by the ACCRA project that develops advanced social robots to support active and healthy ageing, co-created by various stakeholders such as ageing people and physicians. We demonstra this with three robots, Buddy, ASTRO, and RoboHon, used for daily life, mobility, and conversation. The three robots understand and convey emotions in real-time using the Internet of Things and Artificial Intelligence technologies (e.g., knowledge-based reasoning).

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Socially Assistive Robots (SARs) are designed to support us in our daily life as a companion, and assistance but also to support the caregivers' work. SARs should show personalized and human-like behavior to improve their acceptance and, consequently, their use. Additionally, they should be trustworthy by caregivers and professionals to be used as support for their work (e.g. objective assessment, decision support tools). In this context the aim of the paper is dual. Firstly, this paper aims to present and discuss the robot behavioral model based on sensing, perception, decision support, and interaction modules. The novel idea behind the proposed model is to extract and use the same multimodal features set for two purposes: (i) to profile the user, so to be used by the caregiver as a decision support tool for the assessment and monitoring of the patient; (ii) to fine-tune the human-robot interaction if they can be correlated to the social cues. Secondly, this paper aims to test in a real environment the proposed model using a SAR robot, namely ASTRO. Particularly, it measures the body posture, the gait cycle, and the handgrip strength during the walking support task. Those collected data were analyzed to assess the clinical profile and to fine-tune the physical interaction. Ten older people (65.2 ± 15.6 years) were enrolled for this study and were asked to walk with ASTRO at their normal speed for 10 m. The obtained results underline a good estimation (p < 0.05) of gait parameters, handgrip strength, and angular excursion of the torso with respect to most used instruments. Additionally, the sensory outputs were combined in the perceptual model to profile the user using non-classical and unsupervised techniques for dimensionality reduction namely T-distributed Stochastic Neighbor Embedding (t-SNE) and non-classic multidimensional scaling (nMDS). Indeed, these methods can group the participants according to their residual walking abilities.

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Objective: Work-related musculoskeletal disorders (WRMSDs) are considered nowadays the most serious issue in the Occupational Health and Safety field and industrial exoskeletons appear to be a new approach to addressing this medical burden. A systematic review has been carried out to analyze the real-life data of the application of exoskeletons in work settings considering the subjective responses of workers. Methods: The review was registered on PROSPERO. The literature search and its report have been performed following the PRISMA guidelines. A comprehensive literature search was performed in PubMed, EMBASE, Web of Science, and Scopus. Results: Twenty-four original studies were included in the literature review; 42% of the papers retrieved included automobilist industry workers, 17% of the studies evaluated the use of exoskeletons in logistic facilities, and 17% of articles involved healthcare. The remaining six papers recruited farmers, plasterers, wasting collectors, construction workers, and other workmen. All the papers selected tested the use of passive exoskeletons, supporting upper arms or back. Usability, perceived comfort, perceived exertion and fatigue, acceptability and intention to use, occupational safety and health, and job performance and productivity were the main topic analyzed. Conclusion: Exoskeletons are not a fix-all technology, neither for workers nor for job tasks; they tend to show more of their potential in static activities, while in dynamic tasks, they can obstacle regular job performance. Comfort and easiness of use are the key factors influencing the user's experience. More research is needed to determine the most effective and safe ways to implement exoskeleton use in occupational settings. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=275728, identifier CRD42021275728.

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Cognitive disability strongly reduces people's autonomy in performing desired as well as daily activities. The use of Social Assistive Robots (SARs) for cognitive rehabilitation therapy for disabled people could be a valuable gateway for the residential facility of the future. In this work, we design and develop a SAR that can be used for cognitive therapy proposing music and game activities. The results confirm that participants were positively engaged during the proposed activities and satisfied by the robot, despite the low perception of its usability. Professional caregivers noticed and confirmed the high level of engagement and the positive acceptance of the robot within the session, suggesting future tasks for SAR. Clinical Relevance- The results suggest the potential use of SAR also with disabled people proposing cognitive games as a part of the cognitive rehabilitation program.

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As a consequence of the COVID-19 emergency, frail citizens felt isolated because of social isolation, suspended and/or strongly reduced home assistance, and limited access to hospitals. In this sense, assistive technology could play a pivotal role in empowering frail older adults reducing their isolation, as well as in reinforcing the work of formal caregivers and professionals. In this context, the goal of this paper is to present four pilot studies-conducted from March 2020 to April 2021-to promptly react to COVID-19 by providing assistive technology solutions, aiming to (1) guarantee high-quality service to older adults in-home or in residential facility contexts, (2) promote social inclusion, and (3) reduce the virus transmission. In particular, four services, namely, telepresence service, remote monitoring service, virtual visit, and environmental disinfection, were designed, implemented, and tested in real environments involving 85 end-users to assess the user experience and/or preliminary assess the technical feasibility. The results underlined that all the proposed services were generally accepted by older adults and professionals. Additionally, the results remarked that the use of telepresence robots in private homes and residential facilities increased enjoyment reducing anxiety, whereas the monitoring service supported the clinicians in monitoring the discharged COVID-19 patients. It is also worth mentioning that two new services/products were developed to disinfect the environment and to allow virtual visits within the framework of a hospital information system. The virtual visits service offered the opportunity to expand the portfolio of hospital services. The main barriers were found in education, technology interoperability, and ethical/legal/privacy compliance. It is also worth mentioning the key role played by an appropriate design and customer needs analysis since not all assistive devices were designed for older persons.

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This study investigates the adoption of innovative Motor and Cognitive Dual-Task (MCDT) based on the combination of increasing motor and cognitive tasks to discern between subjects with Mild Cognitive Impairment (MCI) and Cognitively Normal Adults (CNA). We aim to adopt new MCDT protocols and to compare their performance against the gold standard (a walking based MCDT, called GAIT). 27 older adults have been assessed through a customized wearable system during 4 MCDTs. We developed as many pooled indices (PIs), based on MCDTs perfomance, demographic data, and clinical scores. We use these parameters as regressors in 4 different logistic regression models. The regression models that encompassed features from innovative MCDT overcame the gold standard classification performance. In particular, models based on the heel tapping and the alternate heel-toe tapping reach the best outputs, namely +8% of accuracy if compared to the gold standard (a walking task). The use of logistic regression models based on MCDT PI have been effective in discerning between CNA vs MCI. Our results suggest that the gold standard MCDT may represents a too demanding exercise to highlight differences between CNA and MCI. It seems that MCDT based on an intermediate level of motor difficulty could represent the sweet spot for the identification of MCI against CNA. Clinical relevance- The combination of innovative digital devices and innovative approach on data analysis (PIs) opened a new scenarios to the early detection and prediction of dementia. Their use would standardize the assessment procedure, lightening the physician from the burden of cumbersome testing sessions. This study suggests that a broader framework for MCDT, which should encompass an ampler selection of motor tasks with different possibilities in terms of difficulties levels, could provide clinicians with a new appropriate set of tools for the early detection of dementia.

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By 2030, the World Health Organization (WHO) foresees a worldwide workforce shortfall of healthcare professionals, with dramatic consequences for patients, economies, and communities. Research in assistive robotics has experienced an increasing attention during the last decade demonstrating its utility in the realization of intelligent robotic solutions for healthcare and social assistance, also to compensate for such workforce shortages. Nevertheless, a challenge for effective assistive robots is dealing with a high variety of situations and contextualizing their interactions according to living contexts and habits (or preferences) of assisted people. This study presents a novel cognitive system for assistive robots that rely on artificial intelligence (AI) representation and reasoning features/services to support decision-making processes of healthcare assistants. We proposed an original integration of AI-based features, that is, knowledge representation and reasoning and automated planning to 1) define a human-in-the-loop continuous assistance procedure that helps clinicians in evaluating and managing patients and; 2) to dynamically adapt robot behaviors to the specific needs and interaction abilities of patients. The system is deployed in a realistic assistive scenario to demonstrate its feasibility to support a clinician taking care of several patients with different conditions and needs.

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This paper makes the VISTA database, composed of inertial and visual data, publicly available for gesture and activity recognition. The inertial data were acquired with the SensHand, which can capture the movement of wrist, thumb, index and middle fingers, while the RGB-D visual data were acquired simultaneously from two different points of view, front and side. The VISTA database was acquired in two experimental phases: in the former, the participants have been asked to perform 10 different actions; in the latter, they had to execute five scenes of daily living, which corresponded to a combination of the actions of the selected actions. In both phase, Pepper interacted with participants. The two camera point of views mimic the different point of view of pepper. Overall, the dataset includes 7682 action instances for the training phase and 3361 action instances for the testing phase. It can be seen as a framework for future studies on artificial intelligence techniques for activity recognition, including inertial-only data, visual-only data, or a sensor fusion approach.

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BACKGROUND: Emotion recognition skills are predicted to be fundamental features in social robots. Since facial detection and recognition algorithms are compute-intensive operations, it needs to identify methods that can parallelize the algorithmic operations for large-scale information exchange in real time. The study aims were to identify if traditional machine learning algorithms could be used to assess every user emotions separately, to relate emotion recognizing in two robotic modalities: static or motion robot, and to evaluate the acceptability and usability of assistive robot from an end-user point of view. METHODS: Twenty-seven hospital employees (M = 12; F = 15) were recruited to perform the experiment showing 60 positive, negative, or neutral images selected in the International Affective Picture System (IAPS) database. The experiment was performed with the Pepper robot. Concerning experimental phase with Pepper in active mode, a concordant mimicry was programmed based on types of images (positive, negative, and neutral). During the experimentation, the images were shown by a tablet on robot chest and a web interface lasting 7 s for each slide. For each image, the participants were asked to perform a subjective assessment of the perceived emotional experience using the Self-Assessment Manikin (SAM). After participants used robotic solution, Almere model questionnaire (AMQ) and system usability scale (SUS) were administered to assess acceptability, usability, and functionality of robotic solution. Analysis wasperformed on video recordings. The evaluation of three types of attitude (positive, negative, andneutral) wasperformed through two classification algorithms of machine learning: k-nearest neighbors (KNN) and random forest (RF). RESULTS: According to the analysis of emotions performed on the recorded videos, RF algorithm performance wasbetter in terms of accuracy (mean ± sd = 0.98 ± 0.01) and execution time (mean ± sd = 5.73 ± 0.86 s) than KNN algorithm. By RF algorithm, all neutral, positive and negative attitudes had an equal and high precision (mean = 0.98) and F-measure (mean = 0.98). Most of the participants confirmed a high level of usability and acceptability of the robotic solution. CONCLUSIONS: RF algorithm performance was better in terms of accuracy and execution time than KNN algorithm. The robot was not a disturbing factor in the arousal of emotions.

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Background: Information and communication technology solutions have the potential to support active and healthy aging and improve monitoring and treatment outcomes. To make such solutions acceptable, all stakeholders must be involved in the requirements elicitation process. Due to the COVID-19 situation, alternative approaches to commonly used face-to-face methods must often be used. One aim of the current article is to share a unique experience from the Pharaon project where due to the COVID-19 outbreak alternative elicitation methods were used. In addition, an overview of common functional, quality, and emotional goals identified by six pilot sites is presented to complement the knowledge about the needs of older adults. Methods: Originally planned face-to-face co-creation seminars were impossible to carry out, and all pilot sites chose alternative requirements elicitation methods that were most suitable in their situation. The elicited requirements were presented in the form of goal models. In one summary goal model, we provide an overview of common functional, quality, and emotional goals. Results: Different elicitation methods were combined based on the digital literacy of the target group and their access to digital tools. Methods applied without digital technologies were phone interviews, reviews of literature and previous projects, while by means of digital technologies online interviews, online questionnaires, and (semi-)virtual co-creation seminars were conducted. The combination of the methods allowed to involve all planned stakeholders. Virtual and semi-virtual co-creation seminars created collaborative environment comparable to face-to-face situations, while online participation helped to save the time of the participants. The most prevalent functional goals elicited were "Monitor health," "Receive advice," "Receive information." "Easy to use/comfortable," "personalized/tailored," "automatic/smart" were identified as most prevalent quality goals. Most frequently occurring emotional goals were "involved," "empowered," and "informed." Conclusion: There are alternative methods to face-to-face co-creation seminars, which effectively involve older adults and other stakeholders in the requirements elicitation process. Despite the used elicitation method, the requirements can be easily transformed into goal models to present the results in a uniform way. The common requirements across different pilots provided a strong foundation for representing detailed requirements and input for further software development processes.

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BACKGROUND: eHealth ecosystems are becoming increasingly important for national and international healthcare. In such ecosystems, different actors are connected and work together to create mutual value. However, it is important to be aware of the goals that each actor pursues within the ecosystem. METHOD: This study describes the outcomes of a workshop (30 participants) and two surveys (completed by 54 and 100 participants), which investigated how different types of industry stakeholders, namely social services, healthcare, technology developers and researchers, rated potential value propositions for an eHealth ecosystem. Both the feasibility and the importance of each proposition was taken into account. RESULTS: Interoperability between services was highly valued across industry types but there were also vast differences concerning other propositions. CONCLUSION: Jointly reflecting on the different perceived values of an ehealth ecosystem can help actors working together to form an ecosystem.

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BACKGROUND: The progressive ageing of the population is leading to an increasing number of people affected by cognitive decline, including disorders in executive functions (EFs), such as action planning. Current procedures to evaluate cognitive decline are based on neuropsychological tests, but novel methods and approaches start to be investigated. Reach-to-grasp (RG) protocols have shown that intentions can influence the EFs of action planning. In this work, we proposed a novel ring-shaped wearable inertial device, SensRing, to measure kinematic parameters during RG and after-grasp (AG) tasks with different end-goals. The aim is to evaluate whether SensRing can characterize the motor performances of people affected by Mild Neurocognitive Disorder (MND) with impairment in EFs. METHODS: Eight Individuals with dysexecutive MND, named d-MND, were compared to ten older healthy subjects (HC). They were asked to reach and grasp a can with three different intentions: to drink (DRINK), to place it on a target (PLACE), or to pass it to a partner (PASS). Twenty-one kinematic parameters were extracted from SensRing inertial data. RESULTS: Seven parameters resulted able to differentiate between HC and d-MND in the RG phase, and 8 features resulted significant in the AG phase. d-MND, indeed, had longer reaction times (in RG PLACE), slower peak velocities (in RG PLACE and PASS, in AG DRINK and PLACE), longer deceleration phases (in all RG and AG DRINK), and higher variability (in RG PLACE, in AG DRINK and PASS). Furthermore, d-MND showed no significant differences among conditions, suggesting that impairments in EFs influence their capabilities in modulating the action planning based on the end-goal. CONCLUSIONS: Based on this explorative study, the system might have the potential for objectifying the clinical assessment of people affected by d-MND by administering an easy motor test. Although these preliminary results have to be investigated in-depth in a larger sample, the portability, wearability, accuracy, and ease-of use of the system make the SensRing potentially appliable for remote applications at home, including analysis of protocols for neuromotor rehabilitation in patients affected by MND.

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Motor and Cognitive Dual-Task (MCDT) represents an innovative chance to assess Mild Cognitive Impairment (MCI). We compare two novel MCDTs, fore-finger tapping (FTAP), toe-tapping (TTHP), to gold standards for cognitive screening (Mini-Mental State Examination-MMSE), and to a well-established MCDT (GAIT). We administered the aforementioned MCDTs to 44 subjects (MCIs and controls). Motor parameters were extracted, and correlations with MMSE investigated. Logistic regression models were built, and AUC areas computed. Spearman's correlation demonstrated that FTAP and TTHP significantly correlate with MMSE, at each cognitive load. AUC areas computed report similar (FTAP, 0.87), and even higher (TTHP, 0.97) capability to identify MCIs, if compared to GAIT (0.92). We investigated the use of novel MCDT approaches to assess MCI, aiming to enrich the clinical repertoire with objective and non-invasive tools. Our protocol shows good correlations with MMSE, and reaches high performances in identifying MCI, adopting simpler exercises.

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BACKGROUND: Social apathy, a reduction in initiative in proposing or engaging in social activities or interactions, is common in mild neurocognitive disorders (MND). Current apathy assessment relies on self-reports or clinical scales, but growing attention is devoted to defining more objective, measurable and non-invasive apathy proxies. OBJECTIVE: In the present study we investigated the interest of recording action kinematics in a social reach-to-grasp task for the assessment of social apathy. METHODS: Thirty participants took part in the study: 11 healthy controls (HC; 6 females, mean age = 68.3±10.5 years) and 19 subjects with MND (13 females, mean age = 75.7±6.3 years). Based on the Diagnostic Criteria for Apathy, MND subjects were classified as socially apathetic (A-MND, N = 9) versus non-apathetic (NA-MND, N = 10). SensRing, a ring-shaped wearable sensor, was placed on their index finger, and subjects were asked to reach and grasp a can to place it into a cup (individual condition) and pass it to a partner (social condition). RESULTS: In the reach-to-grasp phase of the action, HC and NA-MND showed different acceleration and velocity profiles in the social versus individual condition. No differences were found for A-MND. CONCLUSION: Previous studies showed the interest of recording patients' level of weekly motor activity for apathy assessment. Here we showed that a 10-min reach-to-grasp task may provide information to differentiate socially apathetic and non-apathetic subjects with MND, thus providing a tool easily usable in the clinical practice. Future studies with a bigger sample are needed to better characterize these findings.

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