Social acceptability is a determinant factor in the failure or success of the government's decisions about which electricity generation sources will satisfy the growing demand for energy. The main ...goal of this study was to validate a causal trust-acceptability model for electricity generation sources. In the model, social acceptance of an energy source is directly caused by perceived risk and benefit and also by social trust in regulatory agencies (both directly and indirectly, through perceived risk and benefit). Results from a web-based survey of Chilean university students demonstrated that data for energy sources that are controversial in Chilean society (fossil fuels, hydro, and nuclear power) fit the hypothesized model, whereas data for non conventional renewable energy sources (solar, wind, geothermal and tidal) did not. Perceived benefit had the greatest total effect on acceptability, thus emerging as a key predictive factor of social acceptability of controversial electricity generation sources. Further implications for regulatory agencies are discussed.
► We tested a causal trust-acceptability model for electricity generation sources in Chile. ► Data for controversial energy sources in the Chilean society (fossil fuels, hydro and nuclear power) fit the hypothesized model. ► Data for non conventional renewable energy sources did not fit the data. ► Perceived benefit showed the greatest total effect on acceptability.
The well-known correlation between diet and health demonstrates the great possibilities of food to maintain or even improve our health. This fact has brought about a great interest for seeking new ...products that can contribute to improve our health and well-being. This type of foods able to promote our health has generically been defined as functional foods. Nowadays, one of the main areas of research in Food Science and Technology is the extraction and characterization of new natural ingredients with biological activity (e.g., antioxidant, antiviral, antihypertensive, etc.) that can contribute to consumer's well-being as part of new functional foods. The present work shows the results of a bibliographic revision done on the chemical composition of different macroalgae together with a critical discussion about their potential as natural sources of new functional ingredients.
In this contribution, the performance of three different extraction procedures towards the extraction of antioxidants from rosemary (
Rosmarinus officinalis) is presented. Namely, pressurized liquid ...extraction (PLE), using water and ethanol as solvents, supercritical fluid extraction (SFE), using neat CO
2 and supercritical CO
2 modified with ethanol, as well as a novel extraction process called Water Extraction and Particle formation On-line (WEPO) are directly compared. Different extraction conditions including temperatures, times and pressures have been studied. The produced extracts have been characterized in terms of extraction yield, antioxidant activity (using the DPPH radical scavenging method) and total phenols (using the Folin method). Besides, all the extracts have been chemically characterized using a new quantitative UPLC-MS/MS method. This method allowed the determination of the main antioxidants present in rosemary, including, among others, rosmarinic acid, carnosic acid and carnosol, attaining detection limits as low as 2
ng/mL. The results obtained in this study show that PLE using ethanol at high temperatures (200
°C) was able to produce extracts with high antioxidant activity (EC
50 8.8
μg/mL) and high yield (ca. 40%) while efficiently extracting antioxidants of diverse polarity, among them, carnosic and rosmarinic acids, regarded as the most important antioxidants present in rosemary. Nevertheless, in this work, the ability of the three studied environmentally friendly extraction techniques to obtain bioactives from natural sources is demonstrated.
The constant growth of the population with mobility impairments has led to the development of several gait assistance devices. Among these, smart walkers have emerged to provide physical and ...cognitive interactions during rehabilitation and assistance therapies, by means of robotic and electronic technologies. In this sense, this paper presents the development and implementation of a human-robot-environment interface on a robotic platform that emulates a smart walker, the
. The interface includes modules such as a navigation system, a human detection system, a safety rules system, a user interaction system, a social interaction system and a set of autonomous and shared control strategies. The interface was validated through several tests on healthy volunteers with no gait impairments. The platform performance and usability was assessed, finding natural and intuitive interaction over the implemented control strategies.
Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these ...devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0 . 75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.
Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain ...positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.
Globally, 2.5% of upper limb amputations are transhumeral, and both mechanical and electronic prosthetics are being developed for individuals with this condition. Mechanics often require compensatory ...movements that can lead to awkward gestures. Electronic types are mainly controlled by superficial electromyography (sEMG). However, in proximal amputations, the residual limb is utilized less frequently in daily activities. Muscle shortening increases with time and results in weakened sEMG readings. Therefore, sEMG-controlled models exhibit a low success rate in executing gestures. The LIBRA NeuroLimb prosthesis is introduced to address this problem. It features three active and four passive degrees of freedom (DOF), offers up to 8 h of operation, and employs a hybrid control system that combines sEMG and electroencephalography (EEG) signal classification. The sEMG and EEG classification models achieve up to 99% and 76% accuracy, respectively, enabling precise real-time control. The prosthesis can perform a grip within as little as 0.3 s, exerting up to 21.26 N of pinch force. Training and validation sessions were conducted with two volunteers. Assessed with the "AM-ULA" test, scores of 222 and 144 demonstrated the prosthesis's potential to improve the user's ability to perform daily activities. Future work will prioritize enhancing the mechanical strength, increasing active DOF, and refining real-world usability.
Exoskeletons have been assessed by qualitative and quantitative features known as performance indicators. Within these, the ergonomic indicators have been isolated, creating a lack of methodologies ...to analyze and assess physical interfaces. In this sense, this work presents a three-dimensional relative motion assessment method. This method quantifies the difference of orientation between the user's limb and the exoskeleton link, providing a deeper understanding of the Human-Robot interaction. To this end, the AGoRA exoskeleton was configured in a resistive mode and assessed using an optoelectronic system. The interaction quantified a difference of orientation considerably at a maximum value of 41.1 degrees along the sagittal plane. It extended the understanding of the Human-Robot Interaction throughout the three principal human planes. Furthermore, the proposed method establishes a performance indicator of the physical interfaces of an exoskeleton.
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.
Smart walkers are commonly used as potential gait assistance devices, to provide physical and cognitive assistance within rehabilitation and clinical scenarios. To understand such rehabilitation ...processes, several biomechanical studies have been conducted to assess human gait with passive and active walkers. Several sessions were conducted with 11 healthy volunteers to assess three interaction strategies based on passive, low and high mechanical stiffness values on the AGoRA Smart Walker. The trials were carried out in a motion analysis laboratory. Kinematic data were also collected from the smart walker sensory interface. The interaction force between users and the device was recorded. The force required under passive and low stiffness modes was 56.66% and 67.48% smaller than the high stiffness mode, respectively. An increase of 17.03% for the hip range of motion, as well as the highest trunk's inclination, were obtained under the resistive mode, suggesting a compensating motion to exert a higher impulse force on the device. Kinematic and physical interaction data suggested that the high stiffness mode significantly affected the users' gait pattern. Results suggested that users compensated their kinematics, tilting their trunk and lower limbs to exert higher impulse forces on the device.
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