Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic

Advanced photonic and electronic systems WILGA 2018

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers around 400 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2018 was the XLII edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2018 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445. WILGA 2018 works were published in Proc. SPIE vol.10808

A Novel Deep Learning based Automatic Auscultatory Method to Measure Blood Pressure

Background: It is clinically important to develop innovative techniques that can accurately measure blood pressures (BP) automatically. Objectives: This study aimed to present and evaluate a novel automatic BP measurement method based on deep learning method, and to confirm the effects on measured BPs of the position and contact pressure of stethoscope. Methods: 30 healthy subjects were recruited. 9 BP measurements (from three different stethoscope contact pressures and three repeats) were performed on each subject. The convolutional neural network (CNN) was designed and trained to identify the Korotkoff sounds at a beat-by-beat level. Next, a mapping algorithm was developed to relate the identified Korotkoff beats to the corresponding cuff pressures for systolic and diastolic BP (SBP and DBP) determinations. Its performance was evaluated by investigating the effects of the position and contact pressure of stethoscope on measured BPs in comparison with reference manual auscultatory method. Results: The overall measurement errors of the proposed method were 1.4 ± 2.4 mmHg for SBP and 3.3 ± 2.9 mmHg for DBP from all the measurements. In addition, the method demonstrated that there were small SBP differences between the 2 stethoscope positions, respectively at the 3 stethoscope contact pressures, and that DBP from the stethoscope under the cuff was significantly lower than that from outside the cuff by 2.0 mmHg (P < 0.01). Conclusion: Our findings suggested that the deep learning based method was an effective technique to measure BP, and could be developed further to replace the current oscillometric based automatic blood pressure measurement method

Measurements, Models, Systems and Design

531 s.

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results

ACTIVE NOISE CONTROL USING CARBON NANOTUBE THERMOPHONES: CASE STUDY FOR AN AUTOMOTIVE HVAC APPLICATION

The goal of this project was to reduce the overall noise levels emitted by the HVAC components in a vehicle’s cabin. More specifically, the feasibility of achieving this goal using two key technologies was investigated. The first of these technologies, Active Noise Control (ANC), is a noise attenuation technique that relies on destructive interference that “cancels” unwanted noise. Typically used in situations where physical constraints prevent passive attenuation techniques from being used, ANC is known for its high size-to-effectiveness ratio. This benefit cannot be gained without a cost however; the complexity of ANC systems is significantly higher than their passive counterparts. This is due to the signal processing and actuator designs required. These actuators often take the form of moving-coil loudspeakers which, while effective, are often bulky. Because of this they are difficult to “drop in” to an existing system. This is where the second technology comes in. Carbon Nanotube (CNT) Thermophones are solid-state speakers that operate by using rapid heat fluctuations to create sound. Called the “thermoacoustic effect,” (TE) the theory of this operating principle dates to the turn of the 20th century. Useful demonstration of TE did not occur until 2008, however, when researchers first developed the first CNT thermophones. The hallmark characteristics of these transducers are their small size and flexible nature. Compared to traditional loudspeakers they have a much smaller form factor and are more versatile in terms of where they can be placed in a cramped system. The marriage of CNT transducers to ANC technology shows promise in improving the application space and ease of installation of ANC systems. Getting these two to cooperate, however, is not without challenges. A case study for this union is presented here; the application space being the ducted environment of vehicle HVAC systems

An investigation of the applicability of fuzzy logic techniques to real-time temporal classification for threat warning systems

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 62).by Timothy James Boyd.M.S