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Functional data analysis for wearable sensor data: a systematic review

Nihan Acar‐Denizli & Pedro Delicado

AStA Advances in Statistical Analysis2025https://doi.org/10.1007/s10182-025-00531-8review
ABDC C
Weight
0.44

What the paper says

Abstract Wearable devices and sensors have recently become a popular way to collect data, especially in the health sciences. The use of sensors allows patients to be monitored over a period of time with a high observation frequency. Due to the continuous-on-time structure of the data, novel statistical methods are recommended for the analysis of sensor data. One of the popular approaches in the analysis of wearable sensor data is functional data analysis. The main objective of this paper is to review functional data analysis methods applied to wearable device data according to the type of sensor. In addition, we introduce several freely available software packages and open databases of wearable device data to facilitate access to sensor data in different fields.

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https://doi.org/https://doi.org/10.1007/s10182-025-00531-8

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@article{nihan2025,
  title        = {{Functional data analysis for wearable sensor data: a systematic review}},
  author       = {Nihan Acar‐Denizli & Pedro Delicado},
  journal      = {AStA Advances in Statistical Analysis},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10182-025-00531-8},
}

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Functional data analysis for wearable sensor data: a systematic review

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.