Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification

We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type. We train a support vector machine (SVM) classifier using inertial data recorded by a single foot-mounted sensor to differentiate between six motion types (walking, jogging, running, sprinting, crouch-walking, and ladder-climbing) and report mean test classification accuracy of over 90% on a dataset with five different subjects. From these motion types, we select two of the most common (walking and running), and describe a method to compute optimal zero-velocity detection parameters tailored to both a specific user and motion type by maximizing the detector F-score. By combining the motion classifier with a set of optimal detection parameters, we show how we can reduce INS position error during mixed walking and running motion. We evaluate our adaptive system on a total of 5.9 km of indoor pedestrian navigation performed by five different subjects moving along a 130 m path with surveyed ground truth markers.Comment: In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN'17), Sapporo, Japan, Sep. 18-21, 201

Micro-doppler-based in-home aided and unaided walking recognition with multiple radar and sonar systems

Published in IET Radar, Sonar and Navigation. Online first 21/06/2016.The potential for using micro-Doppler signatures as a basis for distinguishing between aided and unaided gaits is considered in this study for the purpose of characterising normal elderly gait and assessment of patient recovery. In particular, five different classes of mobility are considered: normal unaided walking, walking with a limp, walking using a cane or tripod, walking with a walker, and using a wheelchair. This presents a challenging classification problem as the differences in micro-Doppler for these activities can be quite slight. Within this context, the performance of four different radar and sonar systems – a 40 kHz sonar, a 5.8 GHz wireless pulsed Doppler radar mote, a 10 GHz X-band continuous wave (CW) radar, and a 24 GHz CW radar – is evaluated using a broad range of features. Performance improvements using feature selection is addressed as well as the impact on performance of sensor placement and potential occlusion due to household objects. Results show that nearly 80% correct classification can be achieved with 10 s observations from the 24 GHz CW radar, whereas 86% performance can be achieved with 5 s observations of sonar

ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via Graph Optimization

Accurately and reliably positioning pedestrians in satellite-denied conditions remains a significant challenge. Pedestrian dead reckoning (PDR) is commonly employed to estimate pedestrian location using low-cost inertial sensor. However, PDR is susceptible to drift due to sensor noise, incorrect step detection, and inaccurate stride length estimation. This work proposes ReLoc-PDR, a fusion framework combining PDR and visual relocalization using graph optimization. ReLoc-PDR leverages time-correlated visual observations and learned descriptors to achieve robust positioning in visually-degraded environments. A graph optimization-based fusion mechanism with the Tukey kernel effectively corrects cumulative errors and mitigates the impact of abnormal visual observations. Real-world experiments demonstrate that our ReLoc-PDR surpasses representative methods in accuracy and robustness, achieving accurte and robust pedestrian positioning results using only a smartphone in challenging environments such as less-textured corridors and dark nighttime scenarios.Comment: 11 pages, 14 figure

Landmark Visualization on Mobile Maps – Effects on Visual Attention, Spatial Learning, and Cognitive Load during Map-Aided Real-World Navigation of Pedestrians

Even though they are day-to-day activities, humans find navigation and wayfinding to be cognitively challenging. To facilitate their everyday mobility, humans increasingly rely on ubiquitous mobile maps as navigation aids. However, the over-reliance on and habitual use of omnipresent navigation aids deteriorate humans' short-term ability to learn new information about their surroundings and induces a long-term decline in spatial skills. This deterioration in spatial learning is attributed to the fact that these aids capture users' attention and cause them to enter a passive navigation mode. Another factor that limits spatial learning during map-aided navigation is the lack of salient landmark information on mobile maps. Prior research has already demonstrated that wayfinders rely on landmarks—geographic features that stand out from their surroundings—to facilitate navigation and build a spatial representation of the environments they traverse. Landmarks serve as anchor points and help wayfinders to visually match the spatial information depicted on the mobile map with the information collected during the active exploration of the environment. Considering the acknowledged significance of landmarks for human wayfinding due to their visibility and saliency, this thesis investigates an open research question: how to graphically communicate landmarks on mobile map aids to cue wayfinders' allocation of attentional resources to these task-relevant environmental features. From a cartographic design perspective, landmarks can be depicted on mobile map aids on a graphical continuum ranging from abstract 2D text labels to realistic 3D buildings with high visual fidelity. Based on the importance of landmarks for human wayfinding and the rich cartographic body of research concerning their depiction on mobile maps, this thesis investigated how various landmark visualization styles affect the navigation process of two user groups (expert and general wayfinders) in different navigation use contexts (emergency and general navigation tasks). Specifically, I conducted two real-world map-aided navigation studies to assess the influence of various landmark visualization styles on wayfinders' navigation performance, spatial learning, allocation of visual attention, and cognitive load. In Study I, I investigated how depicting landmarks as abstract 2D building footprints or realistic 3D buildings on the mobile map affected expert wayfinders' navigation performance, visual attention, spatial learning, and cognitive load during an emergency navigation task. I asked expert navigators recruited from the Swiss Armed Forces to follow a predefined route using a mobile map depicting landmarks as either abstract 2D building footprints or realistic 3D buildings and to identify the depicted task-relevant landmarks in the environment. I recorded the experts' gaze behavior with a mobile eye-tracer and their cognitive load with EEG during the navigation task, and I captured their incidental spatial learning at the end of the task. The wayfinding experts' exhibited high navigation performance and low cognitive load during the map-aided navigation task regardless of the landmark visualization style. Their gaze behavior revealed that wayfinding experts navigating with realistic 3D landmarks focused more on the visualizations of landmarks on the mobile map than those who navigated with abstract 2D landmarks, while the latter focused more on the depicted route. Furthermore, when the experts focused for longer on the environment and the landmarks, their spatial learning improved regardless of the landmark visualization style. I also found that the spatial learning of experts with self-reported low spatial abilities improved when they navigated with landmarks depicted as realistic 3D buildings. In Study II, I investigated the influence of abstract and realistic 3D landmark visualization styles on wayfinders sampled from the general population. As in Study I, I investigated wayfinders' navigation performance, visual attention, spatial learning, and cognitive load. In contrast to Study I, the participants in Study II were exposed to both landmark visualization styles in a navigation context that mimics everyday navigation. Furthermore, the participants were informed that their spatial knowledge of the environment would be tested after navigation. As in Study I, the wayfinders in Study II exhibited high navigation performance and low cognitive load regardless of the landmark visualization style. Their visual attention revealed that wayfinders with low spatial abilities and wayfinders familiar with the study area fixated on the environment longer when they navigated with realistic 3D landmarks on the mobile map. Spatial learning improved when wayfinders with low spatial abilities were assisted by realistic 3D landmarks. Also, when wayfinders were assisted by realistic 3D landmarks and paid less attention to the map aid, their spatial learning improved. Taken together, the present real-world navigation studies provide ecologically valid results on the influence of various landmark visualization styles on wayfinders. In particular, the studies demonstrate how visualization style modulates wayfinders' visual attention and facilitates spatial learning across various user groups and navigation use contexts. Furthermore, the results of both studies highlight the importance of individual differences in spatial abilities as predictors of spatial learning during map-assisted navigation. Based on these findings, the present work provides design recommendations for future mobile maps that go beyond the traditional concept of "one fits all." Indeed, the studies support the cause for landmark depiction that directs individual wayfinders' visual attention to task-relevant landmarks to further enhance spatial learning. This would be especially helpful for users with low spatial skills. In doing so, future mobile maps could dynamically adapt the visualization style of landmarks according to wayfinders' spatial abilities for cued visual attention, thus meeting individuals' spatial learning needs

Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future