This report describes the design and implementation of a fall detection algorithm for a fall detection system using a pressure based floor sensor. The goal of the system is to detect falls and alarm the relevant personnel when an elderly person has fallen. The fall detection algorithm has a strong connection with the interface subsystem, which uses the algorithm as a function. The interface subsystem supplies matrices containing the raw sensor values of the pressure floor. The algorithm has been divided into multiple sub-algorithms. First, pre-processing: data linearization was applied on the raw sensor values and the sensor matrix was processed such that an image formed that looked like the real world scenario. Second, image processing techniques were applied to detect contours. Contours were being tracked through time, and being grouped. The characteristics of the contours and groups were used to classify falls. Tests have been done to validate the behaviour of the algorithm, from which an average false negative ratio of 30% was achieved in a time window of 30 seconds. The created prototype proves that image processing is a viable tool for detecting falls with the use of a pressure-based floor sensor. Overall, this results in a strong alternative for fall detection that could be used to improve the time an elderly person can live at home safely without the need to move to a nursing home

Binary Neural Networks (BNNs) are receiving an upsurge of attention for bringing power-hungry deep learning towards edge devices. The traditional wisdom in this space is to employ sign(.) for binarizing featuremaps. We argue and illustrate that sign(.) is a uniqueness bottleneck, limiting information propagation throughout the network. To alleviate this, we propose to dispense sign(.), replacing it with a learnable activation binarizer (LAB), allowing the network to learn a fine-grained binarization kernel per layer - as opposed to global thresholding. LAB is a novel universal module that can seamlessly be integrated into existing architectures. To confirm this, we plug it into four seminal BNNs and show a considerable performance boost at the cost of tolerable increase in delay and complexity. Finally, we build an end-to-end BNN (coined as LAB-BNN) around LAB, and demonstrate that it achieves competitive performance on par with the state-of-the-art on ImageNet