Predicting Perceived Exhaustion in Rehabilitation Exercises Using Facial Action Units.

Physical exercise has become an essential tool for treating various non-communicable diseases (also known as chronic diseases). Due to this, physical exercise allows to counter different symptoms and reduce some risk of death factors without medication. A solution to support people in doing exercises is to use artificial systems that monitor their exercise progress. While one crucial aspect is to monitor the correct physical motions for rehabilitative exercise, another essential element is to give encouraging feedback during workouts. A coaching system can track a user's exhaustion and give motivating feedback accordingly to boost exercise adherence. For this purpose, this research investigates whether it is possible to predict the subjective exhaustion level based on non-invasive and non-wearable technology. A novel data set was recorded with the facial record as the primary predictor and individual exhaustion levels as the predicted variable. 60 participants (30 male, 30 female) took part in the data recording. 17 facial action units (AU) were extracted as predictor variables for the perceived subjective exhaustion measured using the BORG scale. Using the predictor and the target variables, several regression and classification methods were evaluated aiming to predict exhaustion. The results showed that the decision tree and support vector methods provide reasonable prediction results. The limitation of the results, depending on participants being in the training data set and subjective variables (e.g., participants smiling during the exercises) were further discussed.

Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists.

The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive assistance. Smart walkers are a particular type of these devices since they integrate navigation systems, path-following algorithms, and user interaction modules to ensure natural and intuitive interaction. Although these functionalities are often implemented in rehabilitation scenarios, there is a need to actively involve the healthcare professionals in the interaction loop while guaranteeing safety for them and patients. This work presents the validation of two visual feedback strategies for the teleoperation of a simulated robotic walker during an assisted navigation task. For this purpose, a group of 14 clinicians from the rehabilitation area formed the validation group. A simple path-following task was proposed, and the feedback strategies were assessed through the kinematic estimation error (KTE) and a usability survey. A KTE of 0.28 m was obtained for the feedback strategy on the joystick. Additionally, significant differences were found through a Mann-Whitney-Wilcoxon test for the perception of behavior and confidence towards the joystick according to the modes of interaction (p-values of 0.04 and 0.01, respectively). The use of visual feedback with this tool contributes to research areas such as remote management of therapies and monitoring rehabilitation of people's mobility.

Assistive locomotion device with haptic feedback for guiding visually impaired people.

Robotic assistive devices are able to enhance physical stability and balance. Smart walkers, in particular, are also capable of offering cognitive support for individuals whom conventional walkers are unsuitable. However, visually impaired individuals often need additional sensorial assistance from those devices. This work proposes a smart walker with an admittance controller for guiding visually impaired individuals along a desired path. The controller uses as inputs the physical interaction between the user and the walker to provide haptic feedback hinting the path to be followed. Such controller is validated in a set of experiments with healthy individuals. At first, users were blindfolded during navigation to assess the capacity of the smart walker in providing guidance without visual input. Then, the blindfold is removed and the focus is on evaluating the human-robot interaction when the user had visual information during navigation. The results indicate that the admittance controller design and the design of the guidance path were factors impacting on the level of comfort reported by users. In addition, when the user was blindfolded, the linear velocity assumed lower values than when did not wear it, from a mean value of 0.19 m/s to 0.21 m/s.

A Novel Multimodal Cognitive Interaction for Walker-Assisted Rehabilitation Therapies.

This work presents a multimodal cognitive interaction strategy aiming at walker-assisted rehabilitation therapies, with special focus on post-stroke patients. Such interaction strategy is based on monitoring user's gait and face orientation to command the displacement of the smart walker. Users are able to actively command the steering of the walker by changing their face orientation, while their lower limbs movement affect the walker's linear velocity. The proposed system is validated using a smart walker and the results obtained point to the feasibility of employing such cognitive interaction in rehabilitation therapies.