Assistive technologies aim at improving personal mobility of individuals with disabilities, increasing their independence and their access to social life. They include mechanical mobility aids that are increasingly employed amongst the older people who rely on them. However, these devices might fail to prevent falls due to the under-estimation of approaching hazards. Stairs and curbs are among these potential dangers present in urban environments and living accommodations, which increase the risk of an accident. We present and evaluate a low-complexity algorithm to detect descending stairs and curbs of any shape, specifically designed for low-power real-time embedded platforms. Based on a passive stereo camera, as opposed to a 3D active sensor, we assessed the detection accuracy, processing time and power consumption. Our goal being to decide on three possible situations (safe, dangerous and potentially unsafe), we achieve to distinguish more than 94 % dangers from safe scenes within a 91 % overall recognition rate at very low resolution. This is accomplished in real-time with robustness to indoor/outdoor lighting conditions. We show that our method can run for a day on a smartphone battery.

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This study investigates the design of a novel real-time system to detect walking behavior changes using an accelerometer on a rollator. No sensor is required on the user. We propose a new non-invasive approach to detect walking behavior based on the motion transfer by the user on the walker. Our method has two main steps; the first is to extract a gait feature vector by analyzing the three-axis accelerometer data in terms of magnitude, gait cycle and frequency. The second is to classify gait with the use of a decision tree of multilayer perceptrons. To assess the performance of our technique, we evaluated different sampling window lengths of 1, 3 an 5 seconds and four different Neural Network architectures. The results revealed that the algorithm can distinguish walking behavior such as normal, slow and fast with an accuracy of about 86%. This research study is part of a project aiming at providing a simple and non-invasive walking behavior detector for elderly who use rollators.

A robust, real-time ground change detector for a "smart" walker Keywords: Ground change detection, colour and texture segmentation, Local Edge Patterns (LEP), Artificial Neural Network (ANN), Elderly care, Gerontechnology.Abstract: Nowadays, there are many different types of mobility aids for elderly people. Nevertheless, these devices may lead to accidents, depending on the terrain where they are being used. In this paper, we present a robust ground change detector that will warn the user before entering dangerous terrains or hostile situations. Specifically, we propose a robust classification algorithm to detect ground changes based on colour histograms and texture descriptors. In our design, we compare the current frame and the average of the k previous frames using different colour systems and Local Edge Patterns. To assess the performance of our algorithm, we evaluated different Artificial Neural Networks architectures. The best results were obtained by representing in the input neurons measures related to Histogram Intersections, Kolmogorov-Smirnov distance, Cumulative Integrals and Earth mover's distance. Under real environmental conditions our results indicated that our proposed detector can accurately distinguish the grounds changes in real-time.

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