Humans observe and infer things in a disentanglement way. Instead of remembering all pixel by pixel, learn things with factors like shape, scale, colour etc. Robot task learning is an open problem in the field of robotics. The task planning in the robot workspace with many constraints makes it even more challenging. In this work, a disentanglement learning of robot tasks with Convolutional Variational Autoencoder is learned, effectively capturing the underlying variations in the data. A robot dataset for disentanglement evaluation is generated with the Selective Compliance Assembly Robot Arm. The disentanglement score of the proposed model is increased to 0.206 with a robot path position accuracy of 0.055, while the state-of-the-art model (VAE) score was 0.015, and the corresponding path position accuracy is 0.053. The proposed algorithm is developed in Python and validated on the simulated robot model in Gazebo interfaced with Robot Operating System.

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Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.