scholarly journals Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

2020 ◽  
Author(s):  
Doreen Jirak ◽  
Stephan Tietz ◽  
Hassan Ali ◽  
Stefan Wermter
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Gesture Recognition ◽  
Network Architecture ◽  
Short Term Memory ◽  
Medical Rehabilitation ◽  
Measurement Unit ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Recent developments of sensors that allow tracking of human movements and gestures enable rapid progress of applications in domains like medical rehabilitation or robotic control. Especially the inertial measurement unit (IMU) is an excellent device for real-time scenarios as it rapidly delivers data input. Therefore, a computational model must be able to learn gesture sequences in a fast yet robust way. We recently introduced an echo state network (ESN) framework for continuous gesture recognition (Tietz et al., 2019) including novel approaches for gesture spotting, i.e., the automatic detection of the start and end phase of a gesture. Although our results showed good classification performance, we identified significant factors which also negatively impact the performance like subgestures and gesture variability. To address these issues, we include experiments with Long Short-Term Memory (LSTM) networks, which is a state-of-the-art model for sequence processing, to compare the obtained results with our framework and to evaluate their robustness regarding pitfalls in the recognition process. In this study, we analyze the two conceptually different approaches processing continuous, variable-length gesture sequences, which shows interesting results comparing the distinct gesture accomplishments. In addition, our results demonstrate that our ESN framework achieves comparably good performance as the LSTM network but has significantly lower training times. We conclude from the present work that ESNs are viable models for continuous gesture recognition delivering reasonable performance for applications requiring real-time performance as in robotic or rehabilitation tasks. From our discussion of this comparative study, we suggest prospective improvements on both the experimental and network architecture level.

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer

2021 ◽  
pp. 001872082199500
Author(s):  
Ziyang Xie ◽  
Li Li ◽  
Xu Xu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Driver Distraction ◽  
Distracted Driving ◽  
Measurement Unit ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Objective We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). Background Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. Methods Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver’s right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. Results The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. Conclusion The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network

2019 ◽  
Vol 31 (6) ◽  
pp. 1085-1113 ◽  
Author(s):  
Po-He Tseng ◽  
Núria Armengol Urpi ◽  
Mikhail Lebedev ◽  
Miguel Nicolelis
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Unscented Kalman Filter ◽  
Bipedal Walking ◽  
Motor Tasks ◽  
Short Term ◽  
Term Memory ◽  
Cortical Areas ◽  
Long Short Term Memory ◽  
Ensemble Activity

Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large ( N = 134–402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.

scholarly journals Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5611 ◽  
Author(s):  
Mihail Burduja ◽  
Radu Tudor Ionescu ◽  
Nicolae Verga
Keyword(s):  
Neural Network ◽  
Intracranial Hemorrhage ◽  
Network Architecture ◽  
Short Term Memory ◽  
Ct Scans ◽  
Short Term ◽  
Term Memory ◽  
Deep Model ◽  
Long Short Term Memory ◽  
Ct Slices

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.

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