The human-robot interaction has played an important role in rehabilitation robotics and impedance control has been used in the regulation of interaction forces between the robot actuator and human limbs. Series elastic actuators (SEAs) have been an efficient solution in the design of this kind of robotic application. Standard implementations of impedance control with SEAs require an internal force control loop for guaranteeing the desired impedance output. However, nonlinearities and uncertainties hamper such a guarantee of an accurate force level in this human-robot interaction. This paper addresses the dependence of the impedance control performance on the force control and proposes a control approach that improves the force control robustness. A unified model of the human-robot system that considers the ankle impedance by a second-order dynamics subject to uncertainties in the stiffness, damping, and inertia parameters has been developed. Fixed, resistive, and passive operation modes of the robotics system were defined, where transition probabilities among the modes were modeled through a Markov chain. A robust regulator for Markovian jump linear systems was used in the design of the force control. Experimental results show the approach improves the impedance control performance. For comparison purposes, a standard H∞ force controller based on the fixed operation mode has also been designed. The Markovian control approach outperformed the H∞ control when all operation modes were taken into account.
Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.
The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.
In human-robot interaction, sensors are relevant in guaranteeing stability and high performance in real-time applications. Nonetheless, accuracy and portable sensors for robots usually have high costs and little flexibility to process signals with free software. Therefore, we propose a wearable sensor network to measure lower limb angular position in human-robot interaction systems. The methodology employed to achieve the aim consisted in implementing a wireless network using low-cost devices, verifying design requirements, and making a validation via a proof of concept. The requirements to design the network include low loss of information, real-time communication, and sensor fusion to estimate the angular position using a gyroscope and accelerometer. Hence, the sensor network developed has a client-server architecture based on ESP8266 microcontrollers. In addition, this network uses the standard 802.11 b/g/n to transmit angular velocity and acceleration measures. Furthermore, we implement the user datagram protocol (UDP) protocol to operate in real-time with a sample time of 10 ms. Finally, we implement a proof of concept to show the system's effectiveness. Thus, we use the Kalman filter to estimate the angular position of the foot, shin, thigh, and hip. Results indicate that the implemented sensor network is suitable for real-time robotic applications.
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