Outlier detection in weight time series of connected scales.

Saeed Mehrang, Elina Helander, Misha Pavel, Angela Chieh, Ilkka Korhonen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    4 Citations (Scopus)

    Abstract

    In principle, connected sensors allow effortless long-term self-monitoring of health and wellness that can help maintain health and quality of life. However, data collected in the ”wild” may be noisy and contain outliers, e.g., due to uncontrolled sources or data from different persons using the same device. The removal of the ”outliers” is therefore critical for accurate interpretation of the data. In this paper we study the detection and elimination of outliers in selfweighing time series data obtained from connected weight scales. We examined three techniques: (1) a method based on autoregressive integrated moving average (ARIMA) time series modelling, (2) median absolute deviation (MAD) scale estimate, and (3) a method based on Rosner statistics. We applied these methods to both a data set with real outliers and a clean data set corrupted with simulated outliers. The results suggest that the simple MAD algorithm and ARIMA performed well with both test sets while the Rosner statistics was significantly less effective. In addition, the ARIMA approach appeared to be significantly less sensitive to long periods of missing data than
    MAD and Rosner statistics.
    Original languageEnglish
    Title of host publication2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
    Subtitle of host publicationSecond International Workshop on the Role for Quantified Self for Personal Healthcare (QSPH’15)
    PublisherInstitute of Electrical and Electronics Engineers
    Pages1489-1496
    Number of pages8
    ISBN (Print)978-1-4673-6799-8
    DOIs
    Publication statusPublished - 2015
    Publication typeA4 Article in a conference publication
    EventIEEE International Conference on Bioinformatics and Biomedicine - , United States
    Duration: 1 Jan 2015 → …

    Conference

    ConferenceIEEE International Conference on Bioinformatics and Biomedicine
    CountryUnited States
    Period1/01/15 → …

    Publication forum classification

    • No publication forum level

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