Aging is often associated with a decline in physical function that eventually leads to loss of autonomy in activities of daily living (ADL). Walking is a very common ADL, important for main determinants of quality of life in older age, and it requires the integration of many physiological systems. Gait speed has been described as the ‘sixth vital sign’ because it is a core indicator of health and function in aging and disease. We reviewed original studies up to June 2020 that assessed frailty in both longitudinal and cross‐sectional observational studies, paying particular attention to how gait is measured in older population and how the gait parameter adopted may influence the estimated frailty models and the health‐related outcomes of the various studies (i.e. clinical, cognitive, physical, and nutritional outcomes). Eighty‐five studies met the search strategy and were included in the present systematic review. According to the frailty tools, more than 60% of the studies used the physical phenotype model proposed by Fried and colleagues, while one‐third referred to multi‐domain indexes or models and only 5% referred to other single‐domain frailty models (social or cognitive). The great heterogeneity observed in gait measurements and protocols limited the possibility to directly compare the results of the studies and it could represent an important issue causing variability in the different outcome measures in both clinical‐and population‐based settings. Gait appeared to be an indicator of health and function also in frail older adults, and different gait parameters appeared to predict adverse health‐related outcomes in clinical, cognitive, and physical domains and, to a lesser extent, in nutritional domain. Gait has the potential to elucidate the common basic mechanisms of cognitive and motor decline. Advances in technology may extend the validity of gait in different clinical settings also in frail older adults, and technology‐based assessment should be encouraged. Combining various gait parameters may enhance frailty prediction and classification of different frailty phenotypes.

A novel wearable haptic device for modulating contact forces at the fingertip is presented. Rendering of forces by skin deformation in three degrees of freedom (DoF), with contact-no contact capabilities, was implemented through rigid parallel kinematics. The novel asymmetrical three revolute-spherical-revolute (3-RSR) configuration allowed compact dimensions with minimum encumbrance of the hand workspace. The device was designed to render constant to low frequency deformation of the fingerpad in three DoF, combining light weight with relatively high output forces. A differential method for solving the non-trivial inverse kinematics is proposed and implemented in real time for controlling the device. The first experimental activity evaluated discrimination of different fingerpad stretch directions in a group of five subjects. The second experiment, enrolling 19 subjects, evaluated cutaneous feedback provided in a virtual pick-and-place manipulation task. Stiffness of the fingerpad plus device was measured and used to calibrate the physics of the virtual environment. The third experiment with 10 subjects evaluated interaction forces in a virtual lift-and-hold task. Although with different performance in the two manipulation experiments, overall results show that participants better controlled interaction forces when the cutaneous feedback was active, with significant differences between the visual and visuo-haptic experimental conditions.

The past decade has seen the emergence of rehabilitation treatments using Virtual Reality (VR) environments, although translation into clinical practice has been limited so far. In this paper an immersive virtual reality rehabilitation training system endowed with wearable haptics is proposed for children with neuromotor impairments: it aims to enhance involvement and engagement of patients, to provide congruent multi-sensory afferent feedback during motor exercises and to benefit from the flexibility of VR in adapting exercises to the patient’s need. An experimental rehabilitation session conducted with children with Cerebral Palsy (CP) and Developmental Dyspraxia (DD) has been performed to evaluate the usability of the system and proof of concept trial of the proposed approach. We compared CP/DD performance with both Typically Developing (TD) children and ADult (AD) control group. Results show the system was compliant with different levels of motor skills and allowed patients to complete the experimental rehabilitation session, with performance varying according to the expected motor abilities of the different groups. Moreover, a kinematic assessment based on the presented system has been designed. Obtained results reflected the different motor abilities of patients and participants, suggesting suitability of the proposed kinematic assessment as a motor function outcome.

Background: Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. Methods: In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. Results: Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). Conclusions: The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Background: The past decade has seen the emergence of rehabilitation treatments using virtual reality. One of the advantages in using this technology is the potential to create positive motivation, by means of engaging environments and tasks shaped in the form of serious games. The aim of this study is to determine the efficacy of immersive Virtual Environments and weaRable hAptic devices (VERA) for rehabilitation of upper limb in children with Cerebral Palsy (CP) and Developmental Dyspraxia (DD). Methods: A two period cross-over design was adopted for determining the differences between the proposed therapy and a conventional treatment. Eight children were randomized into two groups: one group received the VERA treatment in the first period and the manual therapy in the second period, and viceversa for the other group. Children were assessed at the beginning and the end of each period through both the Nine Hole Peg Test (9-HPT, primary outcome) and Kinesiological Measurements obtained during the performing of similar tasks in a real setting scenario (secondary outcomes). Results: All subjects, not depending from which group they come from, significantly improved in both the performance of the 9-HPT and in the parameters of the kinesiological measurements (movement error and smoothness). No statistically significant differences have been found between the two groups. Conclusions: These findings suggest that immersive VE and wearable haptic devices is a viable alternative to conventional therapy for improving upper extremity function in children with neuromotor impairments. Trial registration ClinicalTrials, NCT03353623. Registered 27 November 2017-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03353623