Purpose
– The purpose of this paper is to develop textile-based transcutaneous electrical nerve stimulation (TENS) electrodes using conductive yarn to bring a solution to uncomfortable feelings and hygiene problems of conventional conductive hydrogel electrodes. It proposes washing process, resistance measurements and subjective tests to evaluate the performance of the developed textile-based electrode.
Design/methodology/approach
– In this study, six different textile electrode pairs were designed and produced with different patterns. Designed electrodes were washed for ten times. In order to evaluate the effect of pattern differences and washing process on electrode performances, two different tests were realized before and after washing. The first of these tests is resistance measurement with a multimeter, and the second one is subjective test carried out on subjects.
Findings
– The results obtained from resistance measurements indicated that the pattern differences cause resistance values of electrodes to change. It is reported that subjects had electrical stimulation from all electrode samples in conducted trials and it is noticed that washing process does not cause any stimulation problems.
Originality/value
– In this study, textile-based TENS electrodes having different patterns were produced by machine stitching technique and their long-term usage behaviors were examined with repeated washing processes and trials on the subjects.
Human activity monitoring and recognition systems assist experts in evaluating various health problems including obesity, cardiac diseases and, sports injury detection. However, these systems have two challenging points; monitoring activities for outdoor applications and extracting relevant features using hand-crafted techniques from multi-dimensional and large datasets. To address these challenges, we have focused on new dataset generation for activity recognition, a novel design of a sensor-based wireless activity monitoring system, and its application to deep learning neural networks. The designed monitoring system consists of one master and four slave devices, and can collect and record acceleration and gyroscope information. The slave devices were attached on arm, chest, thigh, and shank areas of the human body. Activity data were collected and recorded from sixty healthy people for thirteen activity types including drink from cup and cleaning table. These activities were divided into three activity categories as basic, complex, and all, which is the combination of basic and complex activities. Obtained datasets were fed into deep learning neural networks namely convolutional neural network (CNN), long-short term memory (LSTM) neural networks, and convolutional LSTM (ConvLSTM) neural networks. The performance of each neural network for each category type was separately examined. The results show that ConvLSTM outperforms CNN and LSTM as far as activity recognition is concerned.
Neural network-based image registration using global image features is relatively a new research subject, and the schemes devised so far use a feedforward neural network to find the geometrical transformation parameters. In this work, we propose to use a radial basis function neural network instead of feedforward neural network to overcome lengthy pre-registration training stage. This modification has been tested on the neural network-based registration approach using discrete cosine transformation features in the presence of noise. The experimental registration work is conducted in two different levels: estimation of transformation parameters from a local range for fine registration and from a medium range for coarse registration. For both levels, the performances of the feedforward neural network-based and radial basis function neural network-based schemes have been obtained and compared to each other. The proposed scheme does not only speed up the training stage enormously but also increases the accuracy and gives robust results in the presence of additive Gaussian noise owing to the better generalization ability of the radial basis function neural networks.
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