In this paper, an experimental evaluation of an active upper-limb exoskeleton, where 12 subjects perform an overhead industrial task with and without the exoskeleton, is presented. The experimental laboratory test has been carried out to analyze the advantages of wearing the exoskeleton to carry out repetitive industrial tasks, assessing whether the proposed device reduces metabolic parameters and the muscular strain. The set of metrics to assess the effects of the exoskeleton included biomechanical parameters, such as electromyographic signals, and metabolic parameters, such as heart rate, heart rate variability, respiratory frequency, tidal volume, ventilation and oxygen consumption. The results show that the developed active upper-limb exoskeleton can reduce cardiorespiratory responses and muscular activity. In addition, statistical data analysis shows significant differences in oxygen consumption, heart rate and effort supported by muscles between conditions (when handling a load of 1.7 kg with and without the exoskeleton). It is observed that, wearing the exoskeleton reduces oxygen consumption by more than 24%, the heart rate decreases by 14%, and muscle activity is reduced by almost 37% in triceps and up to 64% in biceps compare to no wearing. Based on these results, the presented active upper-limb exoskeleton could be potentially useful to reduce muscular strain and fatigue in repetitive overhead tasks.

One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine learning techniques. Four wearable IMUs were used to obtain flexion/extension angles of the hip, knee, and ankle joints, and an IMU placed over the C7 vertebra to measure vertical acceleration. We trained and compared the performance of two machine learning algorithms: feedforward neural networks (FNN) and random forest (RF). We investigated the importance of the inputs introduced into the models and analyzed in detail the contribution of lower limb kinematics and vertical acceleration to model performance. The results suggest that the inclusion of vertical acceleration increases the root mean square error in the FNN, while the RF appears to decrease it. We also analyzed the ability of the models to construct the force signal, with particular emphasis on the magnitude and timing of the vGRF peaks. Using the proposed method, we concluded that FNN and RF models can estimate the vGRF with high accuracy.

Background: The aging of the population and the progressive increase in life expectancy in developed countries is leading to a high incidence of cerebrovascular diseases. Several studies have demonstrated that robot-assisted rehabilitation therapies combined with serious games can improve rehabilitation outcomes. Social interaction in the form of multiplayer games has been highlighted as a potential element to increase patient’s motivation and exercise intensity, which professionals have described as one of the determining factors in maximizing rehabilitation outcomes. Despite this, it has not been widely studied. Physiological measures have been proven as an objective tool to evaluate patients’ experience in robot-assisted rehabilitation environments. However, they have not been used to evaluate patients’ experience in multiplayer robot-assisted rehabilitation therapies. The main objective of this study is to analyze whether the interpersonal interaction inherent in a competitive game mode affects the patients’ physiological responses in robot-assisted rehabilitation environments. Methods: A total of 14 patients participated in this study. The results of a competitive game mode were compared with a single-player game mode with different difficulty levels. Exercise intensity and performance were measured through parameters extracted from the game and the information provided by the robotic rehabilitation platforms. The physiological response of patients in each game mode was measured by the heart rate (HR) and the galvanic skin response (GSR). Patients were asked to fill out the IMI and the overall experience questionnaire. Results: The exercise intensity results show that high-difficulty single-player game mode is similar in terms of intensity level to a competitive game mode, based on velocity values, reaction time and questionnaire results. However, the results of the physiological responses of the patients measured by GSR and HR are lower in the case of the competitive mode compared to the high-difficulty single-player game mode, obtaining results similar to those obtained in the low-difficulty single-player game mode. Conclusions: Patients find the competitive game mode the most fun, which is also the mode they report experiencing the most effort and stress level. However, this subjective evaluation is not in line with the results of physiological responses. This study concludes that interpersonal interaction inherent to a competitive game mode influences patients’ physiological responses. This could mean that social interaction is an important factor to consider when interpreting the results obtained from physiological measurements.

Human gait activity recognition can be crucial to adapt the assistance provided by lower limb exoskeletons, as well as for biomechanical analysis. With this purpose, Deep Learning techniques can be applied to develop a classifier based on the acquisition of the lower limb kinematics. In this paper, we present a one-dimensional Convolutional Neural Network (CNN) to classify different activities from the hip, knee, and ankle flexion/extension angles, measured with wearable inertial sensors. The proposed CNN classifier achieves 99.56% accuracy with users not involved in the learning process. In addition, the Gradient-weighted Class Activation-Map (Grad-CAM) and the t-Distributed Stochastic Neighbor Embedding (t-SNE) were used to understand the CNN model decision-making. Finally, how the accuracy of the CNN model is impacted by input reduction was analyzed to adapt the CNN model to multiple situations, and it can be concluded that the CNN maintains high accuracy with a single joint angle as input.

Health personnel are often unavailable for supervised robot-aided neurorehabilitation in hospitals, and patients are usually challenged by transportation issues to get to hospital. Thus, a discontinuity between therapy in hospital and at home appears slowing down the upper extremity mobility recovery. The aim of this work was to develop a system, based on wearable devices and EMG armband, able to assess the quality of the upper extremity joint movements and intelligently guide the patients during a home-based rehabilitation. This system fuses a classification model together with a dynamic time warping analysis. The classification model was trained with upper extremity joint movements gathered from clinicians, obtaining more than 80% of accuracy using only five joint angles. Then, the system was tested in two post-stroke patients and a healthy subject. The results suggest that the proposed system can be: (i) a useful tool for clinicians to evaluate the rehabilitation therapy; and (ii) an intelligent system able to make decision based on the quality of the activity executed at home.

Interpersonal rehabilitation games, compared to single-player games, enhance motivation and intensity level. Usually, it is complicated to restrict the use of the system to pairs of impaired patients who have a similar skill level. Thus, such games must be dynamically adapted. Difficulty-adaptation algorithms are usually based only on performance parameters. In this way, the patient’s condition cannot be considered when adapting the game. Introducing physiological reactions could help to improve decision-making. However, it is difficult to control how social interaction influences physiological reactions, making it difficult to interpret physiological responses. This article aimed to explore the changes in physiological responses due to the social interaction of a competitive game modality. This pilot study involved ten unimpaired participants (five pairs). We defined different therapy sessions: (i) a session without a competitor; (ii) two sessions with a virtual competitor with different difficulty levels; (iii) a competitive game. Results showed a difference in the physiological response in the competitive mode concerning single-player mode only due to the interpersonal game modality. In addition, feedback from participants suggested that it was necessary to keep a certain difficulty level to make the activity more challenging, and therefore be more engaging and rewarding.