A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills

Mobility has been one of the most impacted aspects of human life due to the spread of the COVID-19 pandemic. Home confinement, the lack of access to physical rehabilitation, and prolonged immobilization of COVID-19-positive patients within hospitals are three major factors that affected the mobility of the general population world-wide. Balance is one key indicator to monitor the possible movement disorders that may arise both during the COVID-19 pandemic and in the coming future post-COVID-19. A systematic quantification of the balance performance in the general population is essential for preventing the appearance and progression of certain diseases (e.g., cardiovascular, neurodegenerative, and musculoskeletal), as well as for assessing the therapeutic outcomes of prescribed physical exercises for elderly and pathological patients. Current research on clinical exercises and associated outcome measures of balance is still far from reaching a consensus on a “golden standard” practice. Moreover, patients are often reluctant or unable to follow prescribed exercises, because of overcrowded facilities, lack of reliable and safe transportation, or stay-at-home orders due to the current pandemic. A novel balance assessment methodology, in combination with a home-care technology, can overcome these limitations. This paper presents a computational framework for the in-home quantitative assessment of balance control skills. Novel outcome measures of balance performance are implemented in the design of rehabilitation exercises with customized and quantifiable training goals. Using this framework in conjunction with a portable technology, physicians can treat and diagnose patients remotely, with reduced time and costs and a highly customized approach. The methodology proposed in this research can support the development of innovative technologies for smart and connected home-care solutions for physical therapy rehabilitation

Stability of Mina v2 for Robot-Assisted Balance and Locomotion

The assessment of the risk of falling during robot-assisted locomotion is critical for gait control and operator safety, but has not yet been addressed through a systematic and quantitative approach. In this study, the balance stability of Mina v2, a recently developed powered lower-limb robotic exoskeleton, is evaluated using an algorithmic framework based on center of mass (COM)- and joint-space dynamics. The equivalent mechanical model of the combined human-exoskeleton system in the sagittal plane is established and used for balance stability analysis. The properties of the Linear Linkage Actuator, which is custom-designed for Mina v2, are analyzed to obtain mathematical models of torque-velocity limits, and are implemented as constraint functions in the optimization formulation. For given feet configurations of the robotic exoskeleton during flat ground walking, the algorithm evaluates the maximum allowable COM velocity perturbations along the fore-aft directions at each COM position of the system. The resulting velocity extrema form the contact-specific balance stability boundaries (BSBs) of the combined system in the COM state space, which represent the thresholds between balanced and unbalanced states for given contact configurations. The BSBs are obtained for the operation of Mina v2 without crutches, thus quantifying Mina v2's capability of maintaining balance through the support of the leg(s). Stability boundaries in single and double leg supports are used to analyze the robot's stability performance during flat ground walking experiments, and provide design and control implications for future development of crutch-less robotic exoskeletons

Agent's Motor Performance: an Index of Difficulty-based Model

Motor performance of operators is extensively studied in physical tasks where movement's accuracy and control are required. In the present work, authors propose a new formulation of the Index of Difficulty (ID) to capture the performance of an agent (e.g., human, robot, co-bot) in executing a given (reference) motor task characterized by a nominal trajectory, spatially constrained along the entire path. The novelty of the model relies on considering the behaviour of an observed agent (e.g., movement variability, average trajectory), and evaluating its performance compared to a reference agent, whose behaviour corresponds to the best execution of the reference motor task. The novel ID can capture differences in performance due to age, and therefore be applied as an indicator to choose the proper agent for the specific physical task (i.e., resource allocation), as well as to evaluate the effectiveness of the human-robot collaboration in work environments. Further research will be focused on extending the model to three-dimensional motor tasks and validating it through real case studies

A ‘Speed—Difficulty—Accuracy’ Model Following a General Trajectory Motor Task with Spatial Constraints: An Information-Based Model

Accuracy in executing a motor task, i.e., in following a given trajectory under geometrical constraints, is of great interest in work operations as well as in biomechanics applications. In the framework of the Fitts’ law research on motor tasks, experimental studies usually refer to simple trajectories which are of low interest in practical applications. Furthermore, available models lack predicting accuracy in executing motor tasks since do not systematically investigate effects of both speed and task difficulty (index of difficulty (ID)). In this paper, the authors propose a ‘Speed-ID-Accuracy’ model aiming at overcoming abovementioned limits. The model is of general validity as is based on an information-based formulation of a trajectory ID; the model proposed put into relation accuracy in task execution with a general trajectory and with the speed of task execution. Modeling accuracy, defined as standard deviation of the endpoint position, is carried out by regressing data available in the literature. The model proposed proves to be more accurate than the classical ‘Speed-Accuracy’ model in fitting available data. Such a result has been found in both numerical cases relating to ‘tunnel’ and ‘circular’ traveling tasks. Limits of data from field experiments are stressed out and future research field of investigations in work environment and biomechanics are figured out

Heart rate variability based assessment of cognitive workload in smart operators

The study on cognitive workload is a field of research of high interest in the digital society. The implementation of ‘Industry 4.0’ paradigm asks the smart operators in the digital factory to accomplish more ‘cognitive-oriented’ than ‘physical-oriented’ tasks. The Authors propose an analytical model in the information theory framework to estimate the cognitive workload of operators. In the model, subjective and physiological measures are adopted to measure the work load. The former refers to NASA-TLX test expressing subjective perceived work load. The latter adopts Heart Rate Variability (HRV) of individuals as an objective indirect measure of the work load. Subjective and physiological measures have been obtained by experiments on a sample subjects. Subjects were asked to accomplish standardized tasks with different cognitive loads according to the ‘n-back’ test procedure defined in literature. Results obtained showed potentialities and limits of the analytical model proposed as well as of the experimental subjective and physiological measures adopted. Research findings pave the way for future developments