Séverine Cloix

Descending Stairs Detection with Low-Power Sensors

With the increasing proportion of senior citizens, many mobility aid devices were developed such as the rollator. However among walker’s users, 87% of their falls is attributed to rollators. The EyeWalker project aims at developing a small device for rollators to protect elderly people from such dangers. Descending stairs are ones of the potential hazards rollator users have to daily face. We propose a method to detect them in real-time using a passive stereo camera. To meet the requirements of low-power consumption, we examined the performance of our stereo vision based detector with regard to the camera resolution. It succeeds in differentiating dangerously approaching stairs from safe situations at low resolutions. In the future, our detector will be ported on an embedded platform equipped with a pair of low-resolution and high dynamic range stereo camera for both indoor and outdoor usage with a battery-life of several days.

Real-time Scale-invariant Object Recognition from Light Field Imaging

We present a novel light field dataset along with a real-time and scale-invariant object recognition system. Our method is based on bag-of-visual-words and codebook approaches. Its evaluation was carried out on a subset of our dataset of unconventional images. We show that the low variance in scale inferred from the specificities of a plenoptic camera allows high recognition performance. With one training image per object to recognise, recognition rates greater than 90 % are demonstrated despite a scale variation of up to 178 %. Our versatile light-field image dataset, CSEM-25, is composed of five classes of five instances captured with the recent industrial Raytrix R5 camera at different distances with several poses and backgrounds. We make it available for research purposes.

An Embedded Ground Change Detector for a "Smart Walker"

Millions of elderly people around the world use the walker for their mobility; nevertheless, these devices may lead to an accident. One of the cause of these accidents is misjudge the terrain. The main objective of this work is the implementation of a ground change detector in real time on a small and light embedded system that can be clipped on a rollator. As a long-term goal, this device will allow users to anticipate entering dangerous situations. We implemented an algorithm to detect ground changes based on color histograms and texture descriptor given as inputs to multi-layer perceptrons. Experiments were performed both off-line and with an embedded system. The obtained results indicated that it is possible to have an accurate detector which is able to distinguish ground changes in real-time.

Walking behavior change detector for a "smart" walker

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.