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Anomaly Detection with Smartwatches as an Opportunity for Implicit Interaction

Published: 24 August 2015 Publication History

Abstract

In this paper we introduce application scenarios for implicit interaction with Smartwatches for the purpose of user assistance, to create awareness, and to enhance as well as simplify the interaction with Wearables. We envision three scenarios (1) the detection of sleep apnea, (2) the detection of epileptic seizures, and (3) a detection of accidents such as falling, car crashes etc., which are presented and discussed. Therefore, the recognition of all incidents described will be discussed under the meta-topic of anomaly detection.

References

[1]
Aloul, F., Zualkernan, I., Abu-Salma, R., Al-Ali, H., & Al-Merri, M. (2014). iBump: Smartphone application to detect car accidents. In Proc of IAICT 2015. IEEE.
[2]
Borazio, M., & Van Laerhoven, K. (2012). Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies. In Proc of SIGHIT 2012. ACM.
[3]
Haescher, M., Bieber, G., Trimpop, J., Urban, B., Kirste, T., & Salomon, R. (2014). Recognition of Low Amplitude Body Vibrations via Inertial Sensors for Wearable Computing. In Proc of HealthyIoT 2014.
[4]
Haescher, M., Trimpop, J., Matthies, D. J. C., Bieber, G., Urban, B., & Kirste, T. (2015). aHead: Considering the Head Position in a Multi-Sensory Setup of Wearables to Recognize Everyday Activities with Intelligent Sensor Fusions. In Proc of HCII 2015.
[5]
Haescher, M., Matthies, D. J. C., Trimpop, J., & Urban, B. (2015). A Study on Measuring Heart- and Respiration-Rate via Wrist-Worn Accelerometer-based Seismocardiography (SCG) in Comparison to Commonly Applied Technologies. In Proc of iWOAR 2015. ACM.
[6]
Halbower, A. C., Degaonkar, M., Barker, P. B., Earley, C. J., Marcus, C. L., Smith, P. L., ... & Mahone, E. M. (2006). Childhood obstructive sleep apnea associates with neuropsychological deficits and neuronal brain injury. In Proc of PLoS, 3(8), e301.
[7]
Hamid, R., Johnson, A., Batta, S., Bobick, A., Isbell, C., & Coleman, G. (2005). Detection and explanation of anomalous activities: Representing activities as bags of event n-grams. In Proc CVPR 2005.
[8]
Hsieh, S. L., Chen, C. C., Wu, S. H., & Yue, T. W. (2014). A wrist-worn fall detection system using accelerometers and gyroscopes. In Proc ICNSC 2014.
[9]
Lockman, J., Fisher, R. S., & Olson, D. M. (2011). Detection of seizure-like movements using a wrist accelerometer. In Epilepsy & Behavior, 20(4), 638--641.
[10]
Matthies, D. J.C., Haescher, M., Alm, R., & Urban, B. (2015). Properties Of A Peripheral Head-Mounted Display (PHMD). In Proc of HCII 2015. Springer.
[11]
Min, J. K., Doryab, A., Wiese, J., Amini, S., Zimmerman, J., & Hong, J. I. (2014). Toss'n'turn: smartphone as sleep and sleep quality detector. In Proc of CHI 2014, 477--486. ACM.
[12]
Schmidt, A. (2000). Implicit human computer interaction through context. In Proc of Personal technologies, 4(2--3), 191--199.
[13]
Schmidt, A. D., Peters, F., Lamour, F., Scheel, C., Çamtepe, S. A., & Albayrak, ŞŞ. (2009). Monitoring smartphones for anomaly detection. In Proc of Mobile Networks and Applications, 14(1), 92--106.

Cited By

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  • (2023)Unobtrusive interaction: a systematic literature review and expert surveyHuman–Computer Interaction10.1080/07370024.2022.216240439:5-6(380-416)Online publication date: Feb-2023
  • (2019)Health@Hand A Visual Interface for eHealth Monitoring2019 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC47284.2019.8969647(1093-1096)Online publication date: Jun-2019
  • (2018)Outlining a Novel Framework for Monitoring User's Vital Signs and Activity Data in Caregiving FacilitiesProceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3266157.3266224(1-2)Online publication date: 20-Sep-2018
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  1. Anomaly Detection with Smartwatches as an Opportunity for Implicit Interaction

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    Published In

    cover image ACM Conferences
    MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct
    August 2015
    697 pages
    ISBN:9781450336536
    DOI:10.1145/2786567
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 24 August 2015

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    Author Tags

    1. Activity Recognition
    2. Anomaly Detection
    3. Implicit Interaction
    4. Scenarios
    5. Smartwatch
    6. Vital Data

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    • Poster
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    • Refereed limited

    Funding Sources

    • German Federal State of Mecklenburg-Western Pomerania and the European Social Fund

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    MobileHCI '15
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    Overall Acceptance Rate 202 of 906 submissions, 22%

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    Cited By

    View all
    • (2023)Unobtrusive interaction: a systematic literature review and expert surveyHuman–Computer Interaction10.1080/07370024.2022.216240439:5-6(380-416)Online publication date: Feb-2023
    • (2019)Health@Hand A Visual Interface for eHealth Monitoring2019 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC47284.2019.8969647(1093-1096)Online publication date: Jun-2019
    • (2018)Outlining a Novel Framework for Monitoring User's Vital Signs and Activity Data in Caregiving FacilitiesProceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3266157.3266224(1-2)Online publication date: 20-Sep-2018
    • (2018)Mobile Assisted LivingProceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3266157.3266210(1-10)Online publication date: 20-Sep-2018
    • (2018)SleepGuardProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649082:3(1-34)Online publication date: 18-Sep-2018
    • (2016)Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User ScenariosSensors10.3390/s1609153816:9(1538)Online publication date: 21-Sep-2016
    • (2016)ubiSleep: An ubiquitous sensor system for sleep monitoring2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2016.7763192(1-4)Online publication date: Oct-2016

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