Integrating Haptic Feedback into Mobile Location Based Services

Haptics is a feedback technology that takes advantage of the human sense of touch by applying forces, vibrations, and/or motions to a haptic-enabled device such as a mobile phone. Historically, human-computer interaction has been visual - text and images on the screen. Haptic feedback can be an important additional method especially in Mobile Location Based Services such as knowledge discovery, pedestrian navigation and notification systems. A knowledge discovery system called the Haptic GeoWand is a low interaction system that allows users to query geo-tagged data around them by using a point-and-scan technique with their mobile device. Haptic Pedestrian is a navigation system for walkers. Four prototypes have been developed classified according to the user’s guidance requirements, the user type (based on spatial skills), and overall system complexity. Haptic Transit is a notification system that provides spatial information to the users of public transport. In all these systems, haptic feedback is used to convey information about location, orientation, density and distance by use of the vibration alarm with varying frequencies and patterns to help understand the physical environment. Trials elicited positive responses from the users who see benefit in being provided with a “heads up” approach to mobile navigation. Results from a memory recall test show that the users of haptic feedback for navigation had better memory recall of the region traversed than the users of landmark images. Haptics integrated into a multi-modal navigation system provides more usable, less distracting but more effective interaction than conventional systems. Enhancements to the current work could include integration of contextual information, detailed large-scale user trials and the exploration of using haptics within confined indoor spaces

Monikulkutapaisten reittien kuvaus älyliikennejärjestelmässä

Automatically detecting individuals’ door-to-door multimodal trips has important applications in an intelligent transport system. These include assisting users in multimodal navigation, optimizing the transit network, and more. Smartphones and other mobile devices today carry a multitude of radios and sensors, including those suitable for detecting location via e.g. Wi-Fi access point mapping or satellite navigation systems, and for detecting motion activity including modes of transport using the accelerometer. Combining these sources with open data from public transport operators, such as static timetables and mass transit vehicle location time series, it is possible to also detect use of mass transit by the smartphone user. In this thesis project a representation for multimodal routes was developed, suitable for analysis of mobility patterns. The modeling includes prerequisite identification of stops and trips, trip origin and destination, mode of trans- port and use of mass transit in trip legs, and recognizing the user’s regular destinations and routes. The discovered mobility patterns can further be combined with data from other sources to produce relevant notifications of exceptions in traffic conditions, such as traffic jams, accidents, or public transport disruptions.Ihmisten monikulkutapaisten reittien automaattisella havaitsemisella ovelta ovelle on olennaisia sovelluksia älykkäässä liikennejärjestelmässä. Näihin lukeutuvat mm. dynaaminen opastus monikulkutapaisella reitillä, ja tietojen mahdollistama liikennejärjestelmän optimointi. Nykyiset älypuhelimet ja muut mobiililaitteet sisältävät moninaisia antureita ja radiolaitteistoa, joita voidaan käyttää laitteen paikannukseen kartoitettujen Wi-Fi -tukiasemien tai satelliittipaikannuksen avulla, sekä liikeaktiviteetin tunnistukseen kiihtyvyysanturin avulla. Kun näitä tietoja yhdistetään julkisen liikenteen palveluntarjoajien tuottamaan avoimeen dataan kuten joukkoliikennevälineiden ajantasaiseen paikannustietoon sekä aikatauluihin, voidaan myös tunnistaa puhelimen käyttäjän joukkoliikennematkoja. Tässä diplomityöprojektissa kehitettiin monikulkutapaisten reittien kuvaamiseen malli, jota voidaan käyttää liikkumistapojen analyysiin. Mallinnukseen sisältyy edellytyksinä pysähdysten ja matkojen havaitseminen, matkojen alku- ja loppupaikkojen kokoaminen, liikkumismuodon ja joukkoliikennematkojen tunnistaminen, sekä käyttäjän toistuvien päämäärien ja reittien jäsentäminen. Liikkumistapamallin tietoja muihin tietolähteisiin yhdistämällä voidaan myös tarjota käyttäjälle relevantteja ilmoituksia poikkeustilanteista liikenteessä kuten merkittävistä ruuhkista, onnettomuuksista, tai joukkoliikennehäiriöistä

Server-side navigation service benchmarking tool

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Smart Signs: Showing the way in Smart Surroundings

This paper presents a context-aware guidance and messaging system for large buildings and surrounding venues. Smart Signs are a new type of electronic door- and way-sign based on wireless sensor networks. Smart Signs present in-situ personalized guidance and messages, are ubiquitous, and easy to understand. They combine the easiness of use of traditional static signs with the flexibility and reactiveness of navigation systems. The Smart Signs system uses context information such as user’s mobility limitations, the weather, and possible emergency situations to improve guidance and messaging. Minimal infrastructure requirements and a simple deployment tool make it feasible to easily deploy a Smart Signs system on demand. An important design issue of the Smart Signs system is privacy: the system secures communication links, does not track users, allow almost complete anonymous use, and prevent the system to be used as a tool for spying on users

Don’t Beep At Me: Using Google Maps APIs to Reduce Driving Anxiety

Stress while driving is a significant issue that causes automobile incidents. Along with the physical injuries, there is often baggage and trauma associated with these accidents. Wearable health monitoring technology, like Smartwatches, has a real possibility to help people further understand the stress inducing processes of driving. Thus to help with this issue, I propose a Google Maps app extension called: Don\u27t Beep At Me . This project creates a map that is layered by heart rate instead of speed limit and has great potential to be useful for reducing driving anxiety

CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network

Mobile phone data have recently become an attractive source of information about mobility behavior. Since cell phone data can be captured in a passive way for a large user population, they can be harnessed to collect well-sampled mobility information. In this paper, we propose CT-Mapper, an unsupervised algorithm that enables the mapping of mobile phone traces over a multimodal transport network. One of the main strengths of CT-Mapper is its capability to map noisy sparse cellular multimodal trajectories over a multilayer transportation network where the layers have different physical properties and not only to map trajectories associated with a single layer. Such a network is modeled by a large multilayer graph in which the nodes correspond to metro/train stations or road intersections and edges correspond to connections between them. The mapping problem is modeled by an unsupervised HMM where the observations correspond to sparse user mobile trajectories and the hidden states to the multilayer graph nodes. The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations. To evaluate CT-Mapper we collected cellular traces with their corresponding GPS trajectories for a group of volunteer users in Paris and vicinity (France). We show that CT-Mapper is able to accurately retrieve the real cell phone user paths despite the sparsity of the observed trace trajectories. Furthermore our transition probability model is up to 20% more accurate than other naive models.Comment: Under revision in Computer Communication Journa

Haptic Transit: Tactile feedback to notify public transport users

To attract people to use public transport, efficient transit information systems providing accurate, real-time, easy-tounderstand information must be provided to users. In this paper we introduce HapticTransit, a tactile feedback based alert/notification model of a system, which provides spatial information to the public transport user. The model uses real-time bus location with other spatial information to provide feedback about the user as their journey is in progress. The system allows users make better use of „in-bus‟ time. It allows the user be involved with other activities and not be anxious about the arrival at their destination bus stop. Our survey shows a majority of users have missed a bus stop/station whilst undertaking a transit journey in an unfamiliar location. The information provided by our system can be of great advantage to certain user groups. The vibration alarm is used to provide tactile feedback. Visual feedback, in the form of colour coded buttons and textual description, is also provided. This model forms the basis for further research for developing information systems for public transport users with special needs – deaf, visually impaired and those with poor spatial abilities