Walking Behavior Change Detector for a “Smart” Walker

AbstractThis 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 5seconds 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

Sparse multi-view 3D computer vision : application to embedded assistive technologies

In the framework of 3D computer vision dedicated to assistive technologies, the research studies reported in this thesis have the objective to design new computer-vision-based approaches dedicated to embedded and real-time applications with limited resources. This thesis proposes novel strategies for rapid object detection and recognition under practical constraints. These limitations are for example the number of sensors and their resolution, algorithm complexity and mobile battery-life. We narrowed the research scope to specific objects and obstacles detection from off-the-shelf stereo and plenoptic cameras. The research work thus investigates two areas of computer vision from multi-view imaging, namely exploiting (i) sparse 3D keypoint clouds from stereo vision and (ii) light field imaging for low-complexity and efficient algorithms

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

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

Walking behavior change detector for a "smart" walker : 6th International conference on Intelligent Human Computer Interaction, IHCI 2014

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

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 of potentially risky 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

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

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

Obstacle and planar object detection using sparse 3D information for a smart walker

With the increasing proportion of senior citizens, many mobility aid devices have been developed such as the rollator. However, under some circumstances, the latter may cause accidents. The EyeWalker project aims to develop a small and autonomous device for rollators to help elderly people, especially those with some degree of visual impairment, avoiding common dangers like obstacles and hazardous ground changes, both outdoors and indoors. We propose amethod of real-time stereo obstacle detection using sparse 3D information. Working with sparse 3D points, in opposition to dense 3D maps, is computationally more efficient and more appropriate for a long battery-life. In our approach, 3D data are extracted from a stereo-rig of two 2D high dynamic range cameras developed at the CSEM (Centre Suisse d'Electronique et de Microtechnique) and processed to perform a boosting classification. We also present a deformable 3D object detector for which the 3D points are combined in several different ways and result in a set of pose estimates used to execute a less ill-posed classification. The evaluation, carried out on real stereo images of obstacles described with both 2D and 3D features, shows promising results for a future use in real-world conditions