In this paper, we present a technique for recognising hand gestures using data obtained from wearable sensors. A Neural Network takes three input streams - accelerometer data, electromyography (EMG) data, and joint angle data - from sensors affixed to the user’s arm and estimates the gesture the user is performing based on the incoming data. Our proposed method handles each of the three input streams separately using multiple convolution and Long-Short Term Memory (LSTM) layers before concatenating them together and passing them through 3 layers of fully connected neurons. This work was trained on a subset of Database 2 (DB2) Exercise 3 from the Ninapro dataset, composed of data collected from 40 participants performing 23 gestures. This work achieved a classification accuracy of 82.11%. As this falls below the classification accuracy of established literature on similar data, we aim to improve its performance by training and validating the model with a larger quantity of training data.

Wildfires affect many regions across the world. The accelerated progression of global warming has amplified their frequency and scale, deepening their impact on human life, the economy, and the environment. The temperature rise has been driving wildfires to behave unpredictably compared to those previously observed, challenging researchers and fire management agencies to understand the factors behind this behavioral change. Furthermore, this change has rendered fire personnel training outdated and lost its ability to adequately prepare personnel to respond to these new fires. Immersive visualization can play a key role in tackling the growing issue of wildfires. Therefore, this survey reviews various studies that use immersive and non-immersive data visualization techniques to depict wildfire behavior and train first responders and planners. This paper identifies the most useful characteristics of these systems. While these studies support knowledge creation for certain situations, there is still scope to comprehensively improve immersive systems to address the unforeseen dynamics of wildfires.