FBG-Based Temperature Sensors for Liquid Identification and Liquid Level Estimation via Random Forest.

This paper proposed a liquid level measurement and classification system based on a fiber Bragg grating (FBG) temperature sensor array. For the oil classification, the fluids were dichotomized into oil and nonoil, i.e., water and emulsion. Due to the low variability of the classes, the random forest (RF) algorithm was chosen for the classification. Three different fluids, namely water, mineral oil, and silicone oil (Kryo 51), were identified by three FBGs located at 21.5 cm, 10.5 cm, and 3 cm from the bottom. The fluids were heated by a Peltier device placed at the bottom of the beaker and maintained at a temperature of 318.15 K during the entire experiment. The fluid identification by the RF algorithm achieved an accuracy of 100%. An average root mean squared error (RMSE) of 0.2603 cm, with a maximum RMSE lower than 0.4 cm, was obtained in the fluid level measurement also using the RF algorithm. Thus, the proposed method is a feasible tool for fluid identification and level estimation under temperature variation conditions and provides important benefits in practical applications due to its easy assembly and straightforward operation.

FBG-Based Sensor for the Assessment of Heat Transfer Rate of Liquids in a Forced Convective Environment.

The assessment of heat transfer is a complex task, especially for operations in the oil and gas industry, due to the harsh and flammable workspace. In light of the limitations of conventional sensors in harsh environments, this paper presents a fiber Bragg grating (FBG)-based sensor for the assessment of the heat transfer rate (HTR) in different liquids. To better understand the phenomenon of heat distribution, a preliminary analysis is performed by constructing two similar scenarios: those with and without the thermal insulation of a styrofoam box. The results indicate the need for a minimum of thermal power to balance the generated heat with the thermal losses of the setup. In this minimum heat, the behavior of the thermal distribution changes from quadratic to linear. To assess such features, the estimation of the specific heat capacity and the thermal conductivity of water are performed from 3 W to 12 W, in 3 W steps, resulting in a specific heat of 1.144 cal/g °C and thermal conductivity of 0.5682 W/m °C. The calibration and validation of the HTR sensor is performed in a thermostatic bath. The method, based on the temperature slope relative to the time curve, allowed for the measurement of HTR in water and Kryo 51 oil, for different heat insertion configurations. For water, the HTR estimation was 308.782 W, which means an uncertainty of 2.8% with the reference value of the cooling power (300 W). In Kryo 51 oil, the estimated heat absorbed by the oil was 4.38 kW in heating and 718.14 kW in cooling.

Effect of a Brain-Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity.

Recently, studies on cycling-based brain-computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.

Assessment of an Assistive Control Approach Applied in an Active Knee Orthosis Plus Walker for Post-Stroke Gait Rehabilitation.

The goal of this study is the assessment of an assistive control approach applied to an active knee orthosis plus a walker for gait rehabilitation. The study evaluates post-stroke patients and healthy subjects (control group) in terms of kinematics, kinetics, and muscle activity. Muscle and gait information of interest were acquired from their lower limbs and trunk, and a comparison was conducted between patients and control group. Signals from plantar pressure, gait phase, and knee angle and torque were acquired during gait, which allowed us to verify that the stance control strategy proposed here was efficient at improving the patients' gaits (comparing their results to the control group), without the necessity of imposing a fixed knee trajectory. An innovative evaluation of trunk muscles related to the maintenance of dynamic postural equilibrium during gait assisted by our active knee orthosis plus walker was also conducted through inertial sensors. An increase in gait cycle (stance phase) was also observed when comparing the results of this study to our previous work. Regarding the kinematics, the maximum knee torque was lower for patients when compared to the control group, which implies that our orthosis did not demand from the patients a knee torque greater than that for healthy subjects. Through surface electromyography (sEMG) analysis, a significant reduction in trunk muscle activation and fatigability, before and during the use of our orthosis by patients, was also observed. This suggest that our orthosis, together with the assistive control approach proposed here, is promising and could be considered to complement post-stroke patient gait rehabilitation.

Simulation System of Electric-Powered Wheelchairs for Training Purposes.

For some people with severe physical disabilities, the main assistive device to improve their independence and to enhance overall well-being is an electric-powered wheelchair (EPW). However, there is a necessity to offer users EPW training. In this work, the Simcadrom is introduced, which is a virtual reality simulator for EPW driving learning purposes, testing of driving skills and performance, and testing of input interfaces. This simulator uses a joystick as the main input interface, and a virtual reality head-mounted display. However, it can also be used with an eye-tracker device as an alternative input interface and a projector to display the virtual environment (VE). Sense of presence, and user experience questionnaires were implemented to evaluate this version of the Simcadrom in addition to some statistical tests for performance parameters like: total elapsed time, path following error, and total number of commands. A test protocol was proposed and, considering the overall results, the system proved to simulate, very realistically, the usability, kinematics, and dynamics of a real EPW in a VE. Most subjects were able to improve their EPW driving performance in the training session. Furthermore, all skills learned are feasible to be transferred to a real EPW.

Characterization of a new polymer optical fiber with enhanced sensing capabilities using a Bragg grating.

We present results for the mechanical characterization of a bisphenol-A acrylate-based polymer optical fiber (POF) manufactured using a novel light polymerization spinning (LPS) process. The particular manufacturing process allows the development of POFs having unique mechanical characteristics, which result from an exceptionally low Young's modulus. The lower Young's modulus enables optical sensors for measuring stress or pressure with improved sensitivity and potentially a higher tunable mechanical range than conventional POFs. Moreover, properties such as the storage modulus variations with respect to the temperature and humidity were studied. Fiber Bragg gratings (FBGs), were inscribed in the POF using the plane-by-plane femtosecond laser, direct-write method for selective FBG mode excitation, and were characterized for changes to temperature, pressure, and relative humidity. The response of FBGs in this LPS-POF for all the three aforementioned measurands was several times higher than that measured for conventional POFs.

Measurement of Temperature and Relative Humidity with Polymer Optical Fiber Sensors Based on the Induced Stress-Optic Effect.

This paper presents a system capable of measuring temperature and relative humidity with polymer optical fiber (POF) sensors. The sensors are based on variations of the Young's and shear moduli of the POF with variations in temperature and relative humidity. The system comprises two POFs, each with a predefined torsion stress that resulted in a variation in the fiber refractive index due to the stress-optic effect. Because there is a correlation between stress and material properties, the variation in temperature and humidity causes a variation in the fiber's stress, which leads to variations in the fiber refractive index. Only two photodiodes comprise the sensor interrogation, resulting in a simple and low-cost system capable of measuring humidity in the range of 5-97% and temperature in the range of 21-46 °C. The root mean squared errors (RMSEs) between the proposed sensors and the reference were 1.12 °C and 1.36% for the measurements of temperature and relative humidity, respectively. In addition, fiber etching resulted in a sensor with a 2 s response time for a relative humidity variation of 10%, which is one of the lowest recorded response times for intrinsic POF humidity sensors.

Gait Shear and Plantar Pressure Monitoring: A Non-Invasive OFS Based Solution for e-Health Architectures.

In an era of unprecedented progress in sensing technology and communication, health services are now able to closely monitor patients and elderly citizens without jeopardizing their daily routines through health applications on their mobile devices in what is known as e-Health. Within this field, we propose an optical fiber sensor (OFS) based system for the simultaneous monitoring of shear and plantar pressure during gait movement. These parameters are considered to be two key factors in gait analysis that can help in the early diagnosis of multiple anomalies, such as diabetic foot ulcerations or in physical rehabilitation scenarios. The proposed solution is a biaxial OFS based on two in-line fiber Bragg gratings (FBGs), which were inscribed in the same optical fiber and placed individually in two adjacent cavities, forming a small sensing cell. Such design presents a more compact and resilient solution with higher accuracy when compared to the existing electronic systems. The implementation of the proposed elements into an insole is also described, showcasing the compactness of the sensing cells, which can easily be integrated into a non-invasive mobile e-Health solution for continuous remote gait monitoring of patients and elder citizens. The reported results show that the proposed system outperforms existing solutions, in the sense that it is able to dynamically discriminate shear and plantar pressure during gait.

Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors.

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users' anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase.

Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing.

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 ) improved for most of the subjects ( A C C ≥ 74.79 % ) , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.

Envelope-based technique for liquid level sensors using an in-line fiber Mach-Zehnder interferometer.

This paper proposes a sensor interrogation technique based on the analysis of the overall spectrum envelope of an in-line Mach-Zehnder interferometer structure with three different fibers. The interference pattern created by a level sensor of 120 mm was evaluated with both the traditional and the proposed interrogation techniques. The result shows that the technique here proposed improves the sensitivity of the sensor by more than an order of magnitude. Moreover, our new interrogation technique allows the length of an in-line Mach-Zehnder interferometer to be extended up to 470 mm while maintaining high linearity and sensitivity.

A New Controller for a Smart Walker Based on Human-Robot Formation.

This paper presents the development of a smart walker that uses a formation controller in its displacements. Encoders, a laser range finder and ultrasound are the sensors used in the walker. The control actions are based on the user (human) location, who is the actual formation leader. There is neither a sensor attached to the user's body nor force sensors attached to the arm supports of the walker, and thus, the control algorithm projects the measurements taken from the laser sensor into the user reference and, then, calculates the linear and angular walker's velocity to keep the formation (distance and angle) in relation to the user. An algorithm was developed to detect the user's legs, whose distances from the laser sensor provide the information necessary to the controller. The controller was theoretically analyzed regarding its stability, simulated and validated with real users, showing accurate performance in all experiments. In addition, safety rules are used to check both the user and the device conditions, in order to guarantee that the user will not have any risks when using the smart walker. The applicability of this device is for helping people with lower limb mobility impairments.

Corrosion Resistant FBG-Based Quasi-Distributed Sensor for Crude Oil Tank Dynamic Temperature Profile Monitoring.

This article presents a corrosion resistant, maneuverable, and intrinsically safe fiber Bragg grating (FBG)-based temperature optical sensor. Temperature monitoring is a critical activity for the oil and gas industry. It typically involves acquiring the desired parameters in a hazardous and corrosive environment. The use of polytetrafluoroethylene (PTFE) was proposed as a means of simultaneously isolating the optical fiber from the corrosive environment and avoiding undesirable mechanical tensions on the FBGs. The presented sensor head is based on multiple FBGs inscribed in a lengthy single mode fiber. The sensor presents an average thermal sensitivity of 8.82 ± 0.09 pm/°C, resulting in a typical temperature resolution of ~0.1 °C and an average time constant value of 6.25 ± 0.08 s. Corrosion and degradation resistance were verified by infrared spectroscopy and scanning electron microscopy during 90 days exposure to high salinity crude oil samples. The developed sensor was tested in a field pilot test, mimicking the operation of an inland crude tank, demonstrating its abilities to dynamically monitor temperature profile.

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals.

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

EEG motor imagery classification using deep learning approaches in naïve BCI users.

Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p< 0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.

Do different robot appearances change emotion recognition in children with ASD?

Introduction: Socially Assistive Robotics has emerged as a potential tool for rehabilitating cognitive and developmental disorders in children with autism. Social robots found in the literature are often able to teach critical social skills, such as emotion recognition and physical interaction. Even though there are promising results in clinical studies, there is a lack of guidelines on selecting the appropriate robot and how to design and implement the child-robot interaction. Methods: This work aims to evaluate the impacts of a social robot designed with three different appearances according to the results of a participatory design (PD) process with the community. A validation study in the emotion recognition task was carried out with 21 children with autism. Results: Spectrum disorder results showed that robot-like appearances reached a higher percentage of children's attention and that participants performed better when recognizing simple emotions, such as happiness and sadness. Discussion: This study offers empirical support for continuing research on using SAR to promote social interaction with children with ASD. Further long-term research will help to identify the differences between high and low-functioning children.

Virtual reality system in conjunction with neurorobotics and neuroprosthetics for rehabilitation of motor disorders.

Cerebrovascular accidents (CVA) and spinal cord injuries (SCI) are the most common causes of paralysis and paresis with reported prevalence of 12,000 cases per million and 800 cases per million, respectively. Disabilities that follow CVA (hemiplegia) or SCI (paraplegia, tetraplegia) severely impair motor functions (e.g., standing, walking, reaching and grasping) and prevent the affected individuals from healthy-like, full and autonomous participation in daily activities. Our research focuses on the development of a new virtual reality (VR) system combined with wearable neurorobotics (NR), motor-neuroprosthetics (MNP) and brain neuro-machine interface (BNMI) to overcome the major limitations of current rehabilitation solutions.

Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System.

The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The system consists of merging data from two inertial measurement units (IMUs) and an intensity-variation based Polymer Optical Fiber (POF) curvature sensor using a quaternion-based Multiplicative Extended Kalman Filter (MEKF). The proposed data fusion method is magnetometer-free and deals with sensors' uncertainties through reliability intervals defined during gait. Walking trials were performed by twelve healthy participants using our knee sleeve system and results were validated against a gold standard motion capture system. Additionally, a comparison with other three knee angle estimation methods, which are exclusively based on IMUs, was carried out. The proposed system presented better performance (mean RMSE 3.3 °, LFM coefficients, a1 = 0.99 ± 0.04, a0 = 0.70 ± 2.29, R2 = 0.98 ± 0.01 and ρC 0.99) when compared to the other evaluated methods. Experimental results demonstrate the usability and feasibility of our system to estimate knee motion with high accuracy, repeatability, and reproducibility. This wearable system may be suitable for motion assessment in rehabilitation labs in future studies.

Extraction of user's navigation commands from upper body force interaction in walker assisted gait.

BACKGROUND: The advances in technology make possible the incorporation of sensors and actuators in rollators, building safer robots and extending the use of walkers to a more diverse population. This paper presents a new method for the extraction of navigation related components from upper-body force interaction data in walker assisted gait. A filtering architecture is designed to cancel: (i) the high-frequency noise caused by vibrations on the walker's structure due to irregularities on the terrain or walker's wheels and (ii) the cadence related force components caused by user's trunk oscillations during gait. As a result, a third component related to user's navigation commands is distinguished. RESULTS: For the cancelation of high-frequency noise, a Benedict-Bordner g-h filter was designed presenting very low values for Kinematic Tracking Error ((2.035 +/- 0.358).10(-2) kgf) and delay ((1.897 +/- 0.3697).10(1)ms). A Fourier Linear Combiner filtering architecture was implemented for the adaptive attenuation of about 80% of the cadence related components' energy from force data. This was done without compromising the information contained in the frequencies close to such notch filters. CONCLUSIONS: The presented methodology offers an effective cancelation of the undesired components from force data, allowing the system to extract in real-time voluntary user's navigation commands. Based on this real-time identification of voluntary user's commands, a classical approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion.

A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation.

A low-cost Brain-Machine Interface (BMI) based on electroencephalography for lower-limb motor recovery of post-stroke patients is proposed here, which provides passive pedaling as feedback, when patients trigger a Mini-Motorized Exercise Bike (MMEB) by executing pedaling motor imagery (MI). This system was validated in an On-line phase by eight healthy subjects and two post-stroke patients, which felt a closed-loop commanding the MMEB due to the fast response of our BMI. It was developed using methods of low-computational cost, such as Riemannian geometry for feature extraction, Pair-Wise Feature Proximity (PWFP) for feature selection, and Linear Discriminant Analysis (LDA) for pedaling imagery recognition. The On-line phase was composed of two sessions, where each participant completed a total of 12 trials per session executing pedaling MI for triggering the MMEB. As a result, the MMEB was successfully triggered by healthy subjects for almost all trials (ACC up to 100%), while the two post-stroke patients, PS1 and PS2, achieved their best performance (ACC of 41.67% and 91.67%, respectively) in Session #2. These patients improved their latency (2.03 ± 0.42 s and 1.99 ± 0.35 s, respectively) when triggering the MMEB, and their performance suggests the hypothesis that our system may be used with chronic stroke patients for lower-limb recovery, providing neural relearning and enhancing neuroplasticity.