Fall detection by egocentric vision

Abstract

IoT Internet of Things LBP Local Binary Pattern LSTM Long Short Term Memory LIF Leaky Integrate and Firing MLP Multi-layer Perceptron MACs Multiply and Accumulates NN Neural Network NNI Neural Network Intelligence RF Random Forest ROC Receiver Operating Characteristic RSNN Recurrent Spiking Neural Network SVM Support Vector Machine TP True Positives TN True Negatives Chapter 1capture data in diverse conditions, two cameras, namely RGB and infrared, were affixed to the subjects' neck and waist positions. This positioning facilitated data collection under varying illumination levels, both low and high, as well as in both indoor and outdoor environments. Consequently, this dataset lends itself to be utilized as a multi-modality resource, catering to both visual and audio-based analysis approaches. Thesis structureThe rest of the thesis is organized as follows. Chapter 2 provides a thorough review of the literature, describing the current state-of-the-art methodologies in fall detection. Chapter 3 elucidates a case study wherein a pre-trained CNN method is applied to data from a singular subject. In Chapter 4, we meticulously craft a late decision fusion methodology, amalgamating both traditional handcrafted and deep learning features. Chapter 5 introduces a streamlined Recurrent Spiking Neural Network, applied to transformed event-based data, and sets it in juxtaposition with a CNN + LSTM approach on the original RGB data. The thesis culminates in Chapter 6, where we present the summary, conclusions, and future perspectives.Additionally, the final part of the thesis includes the Appendix describing a new dataset, which is aimed at being used for benchmarking purposes.