Indoor localization without infrastructure using the acoustic background spectrum

Tarzia, Stephen P.; Dinda, Peter A.; Dick, Robert P.; Memik, Gokhan

doi:10.1145/1999995.2000011

Cited by 254 publications

(122 citation statements)

References 32 publications

(33 reference statements)

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“…Our trained classifier can distinguish stereotypical behaviors like foot tapping, jumping, hand waving, walking, sitting with an accuracy of 85% while our audio classifier can distinguish 7 audio categories with an accuracy of 78.6%. This accuracy is higher than what can be achieved using the approach described in [25].…”

Section: Introductionmentioning

confidence: 61%

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Smartphone Based Autism Social Alert System

Chuah

DiBlasio

2012

2012 8th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

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Abstract-Autism currently affects 1 in every 88 American children impairing their social interactions, communications and daily living. Often, parents, educators, and researchers need to purchase expensive equipment to help autistic children cope with challenges in their daily living. In this paper, we present the Smartphone-Based Autism Social Alert (SASA) system which we design to help such children. The SASA system uses the inexpensive sensors embedded within smartphones to facilitate the study of the autistic children's behaviors by recording and analyzing data collected from such embedded sensors in smartphones carried by autistic children. Our system can automatically detect their stereotypical behaviors such that early interventions can be taken by caregivers or teachers. In addition, the system can correlate environmental sensor data streams, e.g. audio background, with the occurrence of stereotypical behaviors so as to identify potential environmental factors that may trigger such behaviors. We also include some preliminary classification results on the sensor data which we have collected from Androidbased phones using the WEKA J.48 classifier. Our preliminary results show that simple features extracted from accelerometer readings are sufficient to give high accuracy rates when training is performed on a per user per device basis. Our audio classifier which uses 12 MFCC coefficients, average zero crossing rate, and energy can give an accuracy of 78.6% when evaluated using audio traces collected for seven audio categories. Additional extensive experiments will be carried out in the near future at a nearby secondary school for autistic children.

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Section: Introductionmentioning

confidence: 61%

“…In [25], the authors propose using an ambient sound fingerprint called Acoustic Background Spectrum (ABS) which can be easily computed to recognize the different rooms in campuses. Their indoor localization scheme based on ABS can yield an accuracy rate of 69%.…”

Section: Fig 7 Correlating Environmental Factor With Stereotypical Bementioning

confidence: 99%

Smartphone Based Autism Social Alert System

Chuah

DiBlasio

2012

2012 8th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)

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“…the device's) current environment, e.g., microphone, light detectors and cameras. These may provide useful information relating to the transportation mode, e.g., microphones can be used to detect specific sounds of vehicles [19], as well as be used for positioning [20], while cameras can be used for positioning and object detection [2]. Such sensors are generally available in smartphones and may be used both indoors and outdoors.…”

Section: Available Sensing Technologiesmentioning

confidence: 99%

Towards Indoor Transportation Mode Detection Using Mobile Sensing

Prentow

Blunck

Kjærgaard

et al. 2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Transportation mode detection is a growing field of research, in which a variety of methods have been developed for detecting transportation modes foremost for outdoor travels. It has been employed in application areas such as public transportation, environmental footprint profiling, and context-aware mobile assistants. For indoor travels the problem of transportation mode detection has received comparatively little attention, even though diverse transportation modes, such as biking, electric vehicles, and scooters, are used indoors, especially in large building complexes. The potential applications are diverse, and may also extend beyond indoor variants of the above outdoor applications, and include, e.g., scheduling and progress tracking for mobile workers, management of vehicular resources, and navigation support. However, for indoor transportation mode detection, the physical environment as well as the availability and reliability of sensing resources differ drastically from outdoor scenarios. Owing to these differences, many of the methods developed for outdoor transportation mode detection cannot be easily and reliably applied indoors. In this paper, we explore indoor transportation scenarios to arrive at a conceptual model of indoor transportation modes, and then compare challenges for outdoor and indoor transportation mode detection. In addition, we explore methods for transportation mode detection we deem suitable in indoor settings, and we perform an extensive real-world evaluation of (combinations of) such methods at a large hospital complex. The evaluation presented here utilizes Wi-Fi and accelerometer data collected through smartphones carried by several hospital workers throughout four days of work routines. The results show that the methods can distinguish between six common modes of transportation used by the hospital workers with an F-score of 84.2%.

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“…[23] presents a GSM indoor localization system that achieves a median accuracy of 4 m. EZ [6] uses the RSSI to indoor APs and yields a median accuracy of 2-7 m with no pre-deployment effort. There are other types of fingerprints or landmarks used to achieve roomlevel localization, e.g., [4], [7], [12], [14], [22], [25]. Most fingerprinting based localization methods cost an effort for site-survey.…”

Section: Related Workmentioning

confidence: 99%

Montage: Combine frames with movement continuity for realtime multi-user tracking

Zhang

Liu

Jiang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-In this work we design and develop Montage for real-time multi-user formation tracking and localization by offthe-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stridelength estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20 o . For real-time tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

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Indoor localization without infrastructure using the acoustic background spectrum

Cited by 254 publications

References 32 publications

Smartphone Based Autism Social Alert System

Smartphone Based Autism Social Alert System

Towards Indoor Transportation Mode Detection Using Mobile Sensing

Montage: Combine frames with movement continuity for realtime multi-user tracking

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