Lossy Compression Methods for Performance-Restricted Wearable Devices

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With the increasing popularity, diversity, and utilization of wearable devices, the data transfer-and-storage efficiency becomes increasingly important. This paper evaluates a set of compression techniques regarding their utilization in crowdsourced wearable data. Transform-based Discrete Cosine Transform (DCT), interpolation-based Lightweight Temporal Compression (LTC) and dimensionality reduction-focused Symbolic Aggregate Approximation (SAX) were chosen as traditional methods. Additionally, an altered SAX (ASAX) is proposed by the authors and implemented to overcome some of the shortcomings of the traditional methods. As one of the most commonly measured entities in wearable devices, heart rate data were chosen to compare the performance and complexity of the selected compression methods. Main results suggest that best compression results are obtained with LTC, which is also the most complex of the studied methods. The best performance-complexity trade-off is achieved with SAX. Our proposed ASAX has the best dynamic properties among the evaluated methods.
Original languageEnglish
Title of host publicationWiP Proceedings of the International Conference on Localization and GNSS (ICL-GNSS 2020)
Number of pages14
Publication statusPublished - 2020
Publication typeA4 Article in conference proceedings
EventInternational Conference on Localization and GNSS - Tampere, Finland
Duration: 2 Jun 20204 Jun 2020

Publication series

NameCEUR workshop proceedings
ISSN (Electronic)1613-0073


ConferenceInternational Conference on Localization and GNSS
Abbreviated titleICL-GNSS 2020
Internet address


  • Compression
  • Discrete Cosine Transform (DCT)
  • Lightweight Temporal Compression (LTC)
  • Heart Rate
  • Symbolic Aggregation Approximation (SAX)
  • Wearables

Publication forum classification

  • Publication forum level 1


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