Low-power depth-based descending stair detection for smart assistive devices

Abstract

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.

The effect of sports VR training for improving human body composition

“…Therefore, appropriate sports VR training (game-based) is seen as an important factor in the trunk alignment, and it also affects the strength, balance, and quality of life of the lower extremities. Recently, studies with various age groups and materials included VR gears [14,[16][17][18] have been continuing and have been taking place as an effective training method. In this study, sports VR training which may be helpful for aerobic exercise and may strengthen cardiopulmonary showed better effect on coordination (body composition).…”

A software tool for the semi-automatic segmentation of architectural 3D models with semantic annotation and Web fruition

“…Stairs detection is important in several different fields, such as multi-storey path finding for explorer robots venturing into buildings [13][14][15], as an aid for the visually impaired [16][17][18], and last but not least in the semantic segmentation of 3D models of Architectural Heritage buildings [19,20]. Algorithms developed for the first two cases are usually not directly applicable to semantic segmentation for CH, [15] because they generally work on organized point clouds derived from RGB-D data, while CH applications use unorganized clouds coming from photogrammetry or laser scanners.…”

“…In [16] walls, doors, stairs, and a residual generic class of obstacles on the floor are detected in RGB-D data; stairs are found by searching for points on planes at increasing height from the ground, with a given step height and a tolerance. In [17] depth maps, calculated from RGB-D or stereo data, are used to feed a classifier. In [18] the Authors propose a staircase detection algorithm in RGB-D data, based on a support vector machine (SVM): the Hough transform is used to extract parallel lines in RGB frames so as to detect stairs candidates, and the depth frames are employed to classify the staircase candidates as upstairs, downstairs, and negatives (such as corridors).…”

Unifying Terrain Awareness for the Visually Impaired through Real-Time Semantic Segmentation

“…These trends have accelerated the proliferation of monocular detectors and cost-effective RGB-Depth (RGB-D) sensors [ 5 ], supposing essential prerequisites to aid perception and navigation in visually-impaired individuals by leveraging robotic vision [ 7 ]. Along this line, a broad variety of navigational assistive technologies have been developed to accomplish specific goals including avoiding obstacles [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], finding paths [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], locating sidewalks [ 30 , 31 , 32 , 33 ], ascending stairs [ 34 , 35 , 36 , 37 , 38 ] or descending steps [ 39 , 40 ] and negotiating water hazards [ 41 ].…”