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window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":78356325,"created_at":"2022-05-03T12:23:22.083-07:00","from_world_paper_id":204196782,"updated_at":"2025-02-02T08:43:33.985-08:00","_data":{"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","ai_title_tag":"Preventing Distracted Pedestrian Incidents","grobid_abstract":"Distracted pedestrians, akin to their distracted driver counterparts, are an increasingly dangerous threat and precursors to pedestrian accidents in urban communities, often resulting in grave injuries and fatalities. Mitigating such hazards to pedestrian safety requires employment of pedestrian safety systems and applications that are effective in detecting them. Designing effective pedestrian safety frameworks is possible with the availability of sophisticated mobile and wearable devices that are equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge in the design of such systems is accurate recognition of distractions with minimal resources in real-time, given the memory, computation and communication limitations of these devices. Several recently published works detect pedestrian activities by leveraging on complex activity recognition frameworks using mobile and wearable sensor data. The primary focus of these efforts, however, was on achieving high detection accuracy, and therefore most designs are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system, we design an efficient, and real-time pedestrian distraction detection technique that overcomes some of the shortcomings (of existing techniques). We demonstrate the practicality of the proposed technique by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from human subject participants in realistic pedestrian experiments. By means of these evaluations, we show that our technique achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption compared to some other techniques in the literature.","publication_date":"2018,,","publication_name":"IEEE Access","grobid_abstract_attachment_id":"85433033"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A Practical Framework for Preventing Distracted Pedestrian-related Incidents using Wrist Wearables","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [26678070]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":85433033,"attachmentType":"pdf"}"><img alt="First page of “A Practical Framework for Preventing Distracted Pedestrian-related Incidents using Wrist Wearables”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/85433033/mini_magick20220503-14138-makeet.png?1651605846" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A Practical Framework for Preventing Distracted Pedestrian-related Incidents using Wrist Wearables</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="26678070" href="https://iitj.academia.edu/HeenaRathore"><img alt="Profile image of Heena Rathore" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/26678070/8232471/15250726/s65_heena.rathore.jpg" />Heena Rathore</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2018, IEEE Access</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">15 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 78356325; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=78356325"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Distracted pedestrians, akin to their distracted driver counterparts, are an increasingly dangerous threat and precursors to pedestrian accidents in urban communities, often resulting in grave injuries and fatalities. Mitigating such hazards to pedestrian safety requires employment of pedestrian safety systems and applications that are effective in detecting them. Designing effective pedestrian safety frameworks is possible with the availability of sophisticated mobile and wearable devices that are equipped with high-precision on-board sensors capable of capturing fine-grained user movements and context, especially distracted activities. However, the key technical challenge in the design of such systems is accurate recognition of distractions with minimal resources in real-time, given the memory, computation and communication limitations of these devices. Several recently published works detect pedestrian activities by leveraging on complex activity recognition frameworks using mobile and wearable sensor data. The primary focus of these efforts, however, was on achieving high detection accuracy, and therefore most designs are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system, we design an efficient, and real-time pedestrian distraction detection technique that overcomes some of the shortcomings (of existing techniques). We demonstrate the practicality of the proposed technique by implementing prototypes on commercially-available mobile and wearable devices and evaluating them using data collected from human subject participants in realistic pedestrian experiments. By means of these evaluations, we show that our technique achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption compared to some other techniques in the literature.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":85433033,"attachmentType":"pdf","workUrl":"https://www.academia.edu/78356325/A_Practical_Framework_for_Preventing_Distracted_Pedestrian_related_Incidents_using_Wrist_Wearables"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":85433033,"attachmentType":"pdf","workUrl":"https://www.academia.edu/78356325/A_Practical_Framework_for_Preventing_Distracted_Pedestrian_related_Incidents_using_Wrist_Wearables"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Towards a Practical Pedestrian Distraction Detection Framework using Wearables","attachmentId":85433105,"attachmentType":"pdf","work_url":"https://www.academia.edu/78356340/Towards_a_Practical_Pedestrian_Distraction_Detection_Framework_using_Wearables","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/78356340/Towards_a_Practical_Pedestrian_Distraction_Detection_Framework_using_Wearables"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="107869997" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/107869997/StreetBit_A_Bluetooth_Beacon_based_Personal_Safety_Application_for_Distracted_Pedestrians">StreetBit: A Bluetooth Beacon-based Personal Safety Application for Distracted Pedestrians</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="43163" href="https://ua-birmingham.academia.edu/RagibHasan">Ragib Hasan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021 IEEE 18th Annual Consumer Communications &amp; Networking Conference (CCNC), 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">The safety of distracted pedestrians presents a significant public health challenge in the United States and worldwide. An estimated 6,704 American pedestrians died and over 200,000 pedestrians were injured in traffic crashes in 2018, according to the Centers for Disease Control and Prevention (CDC) [1]. This number is increasing annually and many researchers posit that distraction by smartphones is a primary reason for the increasing number of pedestrian injuries and deaths. One strategy to prevent pedestrian injuries and death is to use intrusive interruptions that warn distracted pedestrians directly on their smartphones. To this end, we developed StreetBit, a Bluetooth beacon-based mobile application that alerts distracted pedestrians with a visual and/or audio interruption when they are distracted by their smartphones and are approaching a potentially-dangerous traffic intersection. In this paper, we present the background, architecture, and operations of the StreetBit Application.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"StreetBit: A Bluetooth Beacon-based Personal Safety Application for Distracted Pedestrians","attachmentId":106412465,"attachmentType":"pdf","work_url":"https://www.academia.edu/107869997/StreetBit_A_Bluetooth_Beacon_based_Personal_Safety_Application_for_Distracted_Pedestrians","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/107869997/StreetBit_A_Bluetooth_Beacon_based_Personal_Safety_Application_for_Distracted_Pedestrians"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="95946437" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/95946437/Towards_City_Scale_Smartphone_Sensing_of_Potentially_Unsafe_Pedestrian_Movements">Towards City-Scale Smartphone Sensing of Potentially Unsafe Pedestrian Movements</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="255339749" href="https://independent.academia.edu/shubhamjain979">shubham jain</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper proposes large scale collection of pedestrian movement data to promote pedestrian safety in our rapidly developing urban environments. As a first step, we develop and test algorithms for sensing unsafe pedestrian movements. With distracted pedestrian fatalities on the rise, and larger than ever use of smart devices, we propose to use the smartphone to protect pedestrians by leveraging the in-built inertial sensors on the smartphone. We discuss how to use these sensors for recognizing user movements that could be potentially risky when walking on the street, while also accounting for different phone orientations. We introduce a simple path prediction technique and use this to compute potential street crossings. In order to evaluate our algorithms, we conducted walking trials and collected data from all relevant sensors. Initial tests indicate a 90.5% success rate in predicting that a pedestrians trajectory will cross a road.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Towards City-Scale Smartphone Sensing of Potentially Unsafe Pedestrian Movements","attachmentId":97983251,"attachmentType":"pdf","work_url":"https://www.academia.edu/95946437/Towards_City_Scale_Smartphone_Sensing_of_Potentially_Unsafe_Pedestrian_Movements","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/95946437/Towards_City_Scale_Smartphone_Sensing_of_Potentially_Unsafe_Pedestrian_Movements"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="71176622" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/71176622/A_Smartphone_Application_Designed_to_Detect_Obstacles_for_Pedestrians_Safety">A Smartphone Application Designed to Detect Obstacles for Pedestrians’ Safety</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="28743524" href="https://independent.academia.edu/ZenonasTheodosiou">Zenonas Theodosiou</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Encouraging people to walk rather than using other means of transportation is an important factor towards personal health and environmental sustainability. However, given the large number of pedestrian accidents recorded every year, the need for safe urban environments is increasing. Taking advantage of the potential of citizen-science for crowdsourcing data and creating awareness, we developed a smartphone application for enhancing the safety of pedestrians while walking in cities. Using the application, citizens will monitor the urban sidewalks and update a crowdsourcing platform with the detected barriers and damages that hinder safe walking, along with their location on a city map. To help users assign the correct type of obstacle, and authorities to assess the urgency, a Convolutional Neural Network (CNN) model for barrier and damage recognition is embedded in the application. The results of a user evaluation, based on a group of volunteers who used the application in real conditions, demonstrate the potential of using the application in conjunction with a smart city framework.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Smartphone Application Designed to Detect Obstacles for Pedestrians’ Safety","attachmentId":80636923,"attachmentType":"pdf","work_url":"https://www.academia.edu/71176622/A_Smartphone_Application_Designed_to_Detect_Obstacles_for_Pedestrians_Safety","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/71176622/A_Smartphone_Application_Designed_to_Detect_Obstacles_for_Pedestrians_Safety"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="31021937" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/31021937/A_pedestrian_walking_safety_system_based_on_smartphone_built_in_sensors_A_pedestrian_walking_safety_system_based_on_smartphone_built_in_sensors">A pedestrian walking safety system based on smartphone built-in sensors A pedestrian walking safety system based on smartphone built-in sensors</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="59118858" href="https://independent.academia.edu/nadiaaljamali">nadia al-jamali</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A pedestrian walking safety system based on smartphone built-in sensors A pedestrian walking safety system based on smartphone built-in sensors","attachmentId":51453925,"attachmentType":"pdf","work_url":"https://www.academia.edu/31021937/A_pedestrian_walking_safety_system_based_on_smartphone_built_in_sensors_A_pedestrian_walking_safety_system_based_on_smartphone_built_in_sensors","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/31021937/A_pedestrian_walking_safety_system_based_on_smartphone_built_in_sensors_A_pedestrian_walking_safety_system_based_on_smartphone_built_in_sensors"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="73842651" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/73842651/Smartphone_Based_Activity_Recognition_in_a_Pedestrian_Navigation_Context">Smartphone-Based Activity Recognition in a Pedestrian Navigation Context</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="43700451" href="https://independent.academia.edu/LudwigB">Bernd Ludwig</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Sensors</p><p class="ds-related-work--abstract ds2-5-body-sm">In smartphone-based pedestrian navigation systems, detailed knowledge about user activity and device placement is a key information. Landmarks such as staircases or elevators can help the system in determining the user position when located inside buildings, and navigation instructions can be adapted to the current context in order to provide more meaningful assistance. Typically, most human activity recognition (HAR) approaches distinguish between general activities such as walking, standing or sitting. In this work, we investigate more specific activities that are tailored towards the use-case of pedestrian navigation, including different kinds of stationary and locomotion behavior. We first collect a dataset of 28 combinations of device placements and activities, in total consisting of over 6 h of data from three sensors. We then use LSTM-based machine learning (ML) methods to successfully train hierarchical classifiers that can distinguish between these placements and activities...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Smartphone-Based Activity Recognition in a Pedestrian Navigation Context","attachmentId":82209945,"attachmentType":"pdf","work_url":"https://www.academia.edu/73842651/Smartphone_Based_Activity_Recognition_in_a_Pedestrian_Navigation_Context","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/73842651/Smartphone_Based_Activity_Recognition_in_a_Pedestrian_Navigation_Context"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="123195989" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/123195989/Smombie_Guardian_We_watch_for_potential_obstacles_while_you_are_walking_and_conducting_smartphone_activities">Smombie Guardian: We watch for potential obstacles while you are walking and conducting smartphone activities</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="1597623" href="https://ucla.academia.edu/YoungtaeNoh">Youngtae Noh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">PloS one, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">With the growing dependence on smartphones for everyday activities, a large number of pedestrians nowadays are constantly fixated on their smartphone screens, and hence are susceptible to walking off pavements or colliding with other pedestrians. Reduced attention and situational awareness can render smartphone-occupied users, or smombies, oblivious to potential risks when using their smartphones while walking or driving. In this paper, we introduce a smartphone application, called Smombie Guardian, that detects obstacles and alerts smombies as they walk while viewing their smartphone screens to prevent potential collisions. Based on a user study with 74 participants who used Smombie Guardian in a real-life scenario, we highlight the effectiveness, usefulness, and unobtrusiveness of the algorithm and Smombie Guardian in helping users to avoid potential obstacles.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Smombie Guardian: We watch for potential obstacles while you are walking and conducting smartphone activities","attachmentId":117686779,"attachmentType":"pdf","work_url":"https://www.academia.edu/123195989/Smombie_Guardian_We_watch_for_potential_obstacles_while_you_are_walking_and_conducting_smartphone_activities","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/123195989/Smombie_Guardian_We_watch_for_potential_obstacles_while_you_are_walking_and_conducting_smartphone_activities"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="83932722" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83932722/Improving_Detection_Accuracy_and_Reducing_Energy_Consumption_for_Alerting_Pedestrian_Mobile_Phone_Users_In_Crowded_Area">Improving Detection Accuracy and Reducing Energy Consumption for Alerting Pedestrian Mobile Phone Users In Crowded Area</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="21558033" href="https://kedarunipune.academia.edu/ShivaniDeorukhakar">Shivani Deorukhakar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2017</p><p class="ds-related-work--abstract ds2-5-body-sm">Mobile phone use while walking cause head down and not focusing on the scenery in front of mobile phone user, the potential for disaster unfolds. To avoid colliding with obstacles and to avoid hazards related to the surface change, a focus has to be taken off the screen at regular intervals. Proposed innovation UltraAlert, a Pedestrian safety android application that notifies pedestrian about the upcoming obstacle and abrupt changes of surface like the manhole, staircase etc. through beep alarm. UltraAlert augments Smartphone with a small ultrasonic sensor which measures the distance of the ground surface from the sensor. The temporal variation of distance can provide information about the changes of ground surface ahead in the crowded and non-crowded area. Various sensors of the Smartphone are used with GPS for more accuracy. Accelerometer, gyroscope, magnetometer gives the orientation angles and values; pedometer provides the walk step coordinates. Walking speed estimation, GPS-ba...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Improving Detection Accuracy and Reducing Energy Consumption for Alerting Pedestrian Mobile Phone Users In Crowded Area","attachmentId":89121180,"attachmentType":"pdf","work_url":"https://www.academia.edu/83932722/Improving_Detection_Accuracy_and_Reducing_Energy_Consumption_for_Alerting_Pedestrian_Mobile_Phone_Users_In_Crowded_Area","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/83932722/Improving_Detection_Accuracy_and_Reducing_Energy_Consumption_for_Alerting_Pedestrian_Mobile_Phone_Users_In_Crowded_Area"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="66618163" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/66618163/When_Vehicles_See_Pedestrians_With_Phones_A_Multicue_Framework_for_Recognizing_Phone_Based_Activities_of_Pedestrians">When Vehicles See Pedestrians With Phones: A Multicue Framework for Recognizing Phone-Based Activities of Pedestrians</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="121190774" href="https://ucsd.academia.edu/MohanTrivedi">Mohan Trivedi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Intelligent Vehicles</p><p class="ds-related-work--abstract ds2-5-body-sm">The intelligent vehicle community has devoted considerable efforts to model driver behavior, and in particular to detect and overcome driver distraction in an effort to reduce accidents caused by driver negligence. However, as the domain increasingly shifts towards autonomous and semi-autonomous solutions, the driver is no longer integral to the decision making process, indicating a need to refocus efforts elsewhere. To this end, we propose to study pedestrian distraction instead. In particular, we focus on detecting pedestrians who are engaged in secondary activities involving their cellphones and similar handheld multimedia devices from a purely vision-based standpoint. To achieve this objective, we propose a pipeline incorporating articulated human pose estimation, followed by a soft object label transfer from an ensemble of exemplar SVMs trained on the nearest neighbors in pose feature space. We additionally incorporate head gaze features and prior pose information to carry out cellphone related pedestrian activity recognition. Finally, we offer a method to reliably track the articulated pose of a pedestrian through a sequence of images using a particle filter with a Gaussian Process Dynamical Model (GPDM), which can then be used to estimate sequentially varying activity scores at a very low computational cost. The entire framework is fast (especially for sequential data) and accurate, and easily extensible to include other secondary activities and sources of distraction.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"When Vehicles See Pedestrians With Phones: A Multicue Framework for Recognizing Phone-Based Activities of Pedestrians","attachmentId":77738399,"attachmentType":"pdf","work_url":"https://www.academia.edu/66618163/When_Vehicles_See_Pedestrians_With_Phones_A_Multicue_Framework_for_Recognizing_Phone_Based_Activities_of_Pedestrians","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/66618163/When_Vehicles_See_Pedestrians_With_Phones_A_Multicue_Framework_for_Recognizing_Phone_Based_Activities_of_Pedestrians"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="105423225" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/105423225/Pedestrian_Safety_Exploring_the_Different_Applications_for_Pedestrian_Sensing_Detection">Pedestrian Safety - Exploring the Different Applications for Pedestrian Sensing Detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="109463628" href="https://bits-goa.academia.edu/satishullatil">satish ullatil</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering Research and, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">According to the National Highway Traffic Safety Administration about 5,400 pedestrians were killed and over 70,000 were injured in traffic accidents in 2015. On average, a pedestrian was killed every 1.6 hours and injured every 7.5 minutes in the U.S. that year. Clearly, pedestrians are in danger, and drivers need to do more to protect them. Car manufacturers have stepped up to help by offering systems that detect pedestrians and bicyclists and warn the driver.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Pedestrian Safety - Exploring the Different Applications for Pedestrian Sensing Detection","attachmentId":104882536,"attachmentType":"pdf","work_url":"https://www.academia.edu/105423225/Pedestrian_Safety_Exploring_the_Different_Applications_for_Pedestrian_Sensing_Detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/105423225/Pedestrian_Safety_Exploring_the_Different_Applications_for_Pedestrian_Sensing_Detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":85433033,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":85433033,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_85433033" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="82014999" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/82014999/An_overview_of_human_activity_detection_technologies_for_pervasive_systems">An overview of human activity detection technologies for pervasive systems</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="95749199" href="https://independent.academia.edu/WildeAdriana">Adriana Wilde</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Seminar paper, Department of Informatics, University of 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translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="1" data-entity-id="76981659" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/76981659/Body_sensor_networks_for_driver_distraction_identification">Body sensor networks for driver distraction identification</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="38406114" href="https://independent.academia.edu/JohnHansen32">John Hansen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2008 IEEE International Conference on Vehicular Electronics and Safety, 2008</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Body sensor networks for driver distraction identification","attachmentId":84502761,"attachmentType":"pdf","work_url":"https://www.academia.edu/76981659/Body_sensor_networks_for_driver_distraction_identification","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/76981659/Body_sensor_networks_for_driver_distraction_identification"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="2" data-entity-id="88932935" data-sort-order="default"><a 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Safety","attachmentId":92820633,"attachmentType":"pdf","work_url":"https://www.academia.edu/88932935/PAWS_A_Wearable_Acoustic_System_for_Pedestrian_Safety","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/88932935/PAWS_A_Wearable_Acoustic_System_for_Pedestrian_Safety"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="3" data-entity-id="67128547" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" 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