Functional electrical stimulation (FES) applied via transcutaneous electrodes is a common rehabilitation technique for assisting grasp in patients with central nervous system lesions. To improve the stimulation effectiveness of conventional FES, we introduce multi-pad electrodes and a new stimulation paradigm.

Background: Despite important advancements in control and mechatronics of myoelectric prostheses, the communication between the user and his/her bionic limb is still unidirectional, as these systems do not provide somatosensory feedback. Electrotactile stimulation is an attractive technology to close the control loop since it allows flexible modulation of multiple parameters and compact interface design via multi-pad electrodes. However, the stimulation interferes with the recording of myoelectric signals and this can be detrimental to control. Methods: We present a novel compact solution for simultaneous recording and stimulation through dynamic blanking of stimulation artefacts. To test the system, a feedback coding scheme communicating wrist rotation and hand aperture was developed specifically to stress the myoelectric control while still providing meaningful information to the subjects. Ten subjects participated in an experiment, where the quality of closed-loop myoelectric control was assessed by controlling a cursor in a two degrees of freedom target-reaching task. The benchmark performance with visual feedback was compared to that achieved by combining visual feedback and electrotactile stimulation as well as by using electrotactile feedback only. Results: There was no significant difference in performance between visual and combined feedback condition with regards to successfully reached targets, time to reach a target, path efficiency and the number of overshoots. Therefore, the quality of myoelectric control was preserved in spite of the stimulation. As expected, the tactile condition was significantly poorer in completion rate (100/4% and 78/25% for combined and tactile condition, respectively) and time to reach a target (9/2 s and 13/4 s for combined and tactile condition, respectively). However, the performance in the tactile condition was still good, with no significant difference in path efficiency (38/8%) and the number of overshoots (0.5/0.4 overshoots), indicating that the stimulation was meaningful for the subjects and useful for closed-loop control. Conclusions: Overall, the results demonstrated that the developed system can provide robust closed-loop control using electrotactile stimulation. The system supports different encoding schemes and allows placing the recording and stimulation electrodes next to each other. This is an important step towards an integrated solution where the developed unit will be embedded into a prosthetic socket.

Self-excited vibrations represent one of the most unfavorable phenomena in the cutting process because they can lead to the accelerated wear or breakage of the tool, a sudden deterioration in the quality of the machined surface, and an increase in noise and energy consumption. To avoid these negative effects, stability diagrams are used when defining the cutting regimes, which, depending on the main spindle speed and the cutting depth, show the border between the stable and unstable machine tool operation states from the aspect of self-excited vibrations. These diagrams, known as “stability lobe diagrams”, can be defined using mathematical models (analytical or numerical) or through experimental methods. However, when machining at relatively low main spindle revolutions, process damping occurs, which increases the system stability, i.e., enables a greater cutting depth limit. For the stability diagram to be effectively used for predicting the cutting depth limits at low machining speeds, it is necessary to take the effect of process damping into account. This paper introduces an experimental method for the determination of process damping and its integration into the mathematical framework of the Fourier series method, commonly utilized for the construction of stability lobe diagrams.

Electrotactile stimulation is a technology that reproducibly elicits tactile sensations and can be used as an alternative channel to communicate information to the user. The presented work is a part of an effort to develop this technology into an unobtrusive communication tool for first responders. In this study, the aim was to compare the success rate (SR) between discriminating stimulation at six spatial locations (static encoding) and recognizing six spatio-temporal patterns where pads are activated sequentially in a predetermined order (dynamic encoding). Additionally, a procedure for a fast amplitude calibration, that includes a semi-automated initialization and an optional manual adjustment, was employed and evaluated. Twenty subjects, including twelve first responders, participated in the study. The electrode comprising the 3 × 2 matrix of pads was placed on the lateral torso. The results showed that high SRs could be achieved for both types of message encoding after a short learning phase; however, the dynamic approach led to a statistically significant improvement in messages recognition (SR of 93.3%), compared to static stimulation (SR of 83.3%). The proposed calibration procedure was also effective since in 83.8% of the cases the subjects did not need to adjust the stimulation amplitude manually.

Soft sensors are increasingly being used to provide important information about production processes that is otherwise only available through off-line laboratory analysis. However, usually, they are developed for a specific application, for which thorough process analysis is performed to provide information for the appropriate selection of model type and model structure. Wide industrial application of soft sensors, however, requires a method for soft sensor development that has a high level of automatism and is applicable to a significant number of industrial processes. A class of processes that is very common in the industry are processes with distinct operating conditions. In this paper, an algorithm that is suitable for the development of soft sensors for this class of processes is presented. The algorithm possesses a high level of automatism, as it requires minimal user engagement regarding the structure of the model, which makes it suitable for implementation as a customary industrial solution. The algorithm is based on a radial basis function artificial neural network, and it enables the automatic selection of the model structure and the determination of model parameters, only based on the training data set. The testing of the presented algorithm is done on the cement production process, since it represents a process with distinct operating conditions. The results of the test show that, besides providing a high level of automatism in model development, the presented algorithm generates a soft sensor with high estimation performance.