734 research outputs found

    Classification of Arbitrary Motion into a Canonical Basis

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    The Empatica E4 wristwatch utilizes four sensors to capture medical data from its user - an accelerometer, a plethysmograph, an electro-dermal activity sensor, and an infrared thermophile. Utilizing these sensors, the device can provide detection-based feedback for patients suffering from various ailments. However, each sensor is coupled with the other readings, so any raw data will have a degree of noise accompanying the actual signal. After detailing a conceptual and programming knowledge of various industry-standard data processing techniques, we follow the appropriate steps to take in order to clean up a noisy E4 data signal, starting with supervised basis signals and ending with unsupervised, random samples. We conclude with a discussion of how one can decompose arbitrary motions into a canonical basis for proper data analysis, providing insight based on our results

    Three-dimensional Acceleration Measurement Using Videogrammetry Tracking Data

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    In order to evaluate the feasibility of multi-point, non-contact, acceleration measurement, a high-speed, precision videogrammetry system has been assembled from commercially-available components and software. Consisting of three synchronized 640 X 480 pixel monochrome progressive scan CCD cameras each operated at 200 frames per second, this system has the capability to provide surface-wide position-versus-time data that are filtered and twice-differentiated to yield the desired acceleration tracking at multiple points on a moving body. The oscillating motion of targets mounted on the shaft of a modal shaker were tracked, and the accelerations calculated using the videogrammetry data were compared directly to conventional accelerometer measurements taken concurrently. Although differentiation is an inherently noisy operation, the results indicate that simple mathematical filters based on the well-known Savitzky and Golay algorithms, implemented using spreadsheet software, remove a significant component of the noise, resulting in videogrammetry-based acceleration measurements that are comparable to those obtained using the accelerometers

    Investigation of a hybrid switching control system

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    Bibliography: pages 84-86.A servo motor is to be used to position the cutting arm in a hypothetical pattern generation application. The motor is controlled in closed-loop in order to track, with zero asymptotic error, a reference signal represented by either a sinusoidal, triangular, or square wave. In addition, the schedule of reference signal type changes is not known a priori and the controlled system must achieve asymptotic tracking without operator intervention. As no simple single controller can satisfy these requirements for all setpoint types, a Hybrid Switching Control System is proposed which combines intuitive logic with standard control techniques. Under the guidance of a simple supervisor, the controller corresponding to each type of setpoint is switched in and out of the active feedback loop as required. A simple Multi-layer Perceptron neural network was selected to identify the type of signal being tracked and hence initiate controller switching. This network performed very well even in the presence of measurement noise, and the hybrid system automatically tracked each of the three types of reference signal over a wide range of signal amplitude and frequency. However, the reconfiguration interval was quite long (although still acceptable in terms of the proposed application), and the size of the neural net structure had to be limited for the system to work in real-time

    Dance-the-music : an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates

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    In this article, a computational platform is presented, entitled “Dance-the-Music”, that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers’ models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method can determine the quality of a student’s performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures

    EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification

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    In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition Ⅳ and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code freely accessible at https://github.com/AbbasSalami/EEG-ITNet
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