In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural network (CNN) strategy is applied to a single user movement recognition using a smartphone. Three parallel CNNs are used for local feature extraction, and latter they are fused in the classification task stage. The whole CNN scheme is based on a feature fusion of a fine-CNN, a medium-CNN, and a coarse-CNN. A tri-axial accelerometer and a tri-axial gyroscope sensor embedded in a smartphone are used to record the acceleration and angle signals. Six human activities successfully classified are walking, walking-upstairs, walking-downstairs, sitting, standing and laying. Performance evaluation is presented for the proposed CNN.

The Jupyter–Python Notebook open source is currently used world‐wide as a programming environment for courses and labs. This paper describes an application of a set of Python computer programs implemented in the form of Jupyter notebooks to learn the course of digital signal processing and also seeks to contribute to the development of innovative teaching methodologies. A set of user‐friendly Jupyter notebooks workflows allows teachers–students to teach and learn theoretical concepts, description of problems, computer code, and simulation results with a graphical output. The proposed environment allows teachers/students to code their simulations in Python or Octave simulating languages. Additionally, a new approach involves student‐centered learning to teach theoretical and lab concepts.

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