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Analysis of institutional authors

Martin, HenarCorresponding AuthorBernardos, Ana M.AuthorCasar, Jose R.Author

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June 9, 2019
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Activity logging using lightweight classification techniques in mobile devices

Publicated to: Personal and Ubiquitous Computing. 17 (4): 675-695 - 2013-04-01 17(4), DOI: 10.1007/s00779-012-0515-4

Authors:

Martín, H; Bernardos, AM; Iglesias, J; Casar, JR
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Affiliations

Univ Politecn Madrid, Telecommun Sch, E-28040 Madrid, Spain - Author

Abstract

Automated activity recognition enables a wide variety of applications related to child and elderly care, disease diagnosis and treatment, personal health or sports training, for which it is key to seamlessly determine and log the user's motion. This work focuses on exploring the use of smartphones to perform activity recognition without interfering in the user's lifestyle. Thus, we study how to build an activity recognition system to be continuously executed in a mobile device in background mode. The system relies on device's sensing, processing and storing capabilities to estimate significant movements/postures (walking at different paces-slow, normal, rush, running, sitting, standing). In order to evaluate the combinations of sensors, features and algorithms, an activity dataset of 16 individuals has been gathered. The performance of a set of lightweight classifiers (Naive Bayes, Decision Table and Decision Tree) working on different sensor data has been fully evaluated and optimized in terms of accuracy, computational cost and memory fingerprint. Results have pointed out that a priori information on the relative position of the mobile device with respect to the user's body enhances the estimation accuracy. Results show that computational low-cost Decision Tables using the best set of features among mean and variance and considering all the sensors (acceleration, gravity, linear acceleration, magnetometer, gyroscope) may be enough to get an activity estimation accuracy of around 88 % (78 % is the accuracy of the Naive Bayes algorithm with the same characteristics used as a baseline). To demonstrate its applicability, the activity recognition system has been used to enable a mobile application to promote active lifestyles.
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Keywords

Activity recognitionContext awarenessEnergy-expenditureLife loggingMobile applicationPattern recognitionPersonal healthPhone

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Personal and Ubiquitous Computing due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2013, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Hardware and Architecture. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 5.03. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 4.83 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-03, the following number of citations:

  • WoS: 120
  • Scopus: 159
  • Google Scholar: 234
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 176.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 176 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MARTIN SANTOS, HENAR) and Last Author (CASAR CORREDERA, JOSE RAMON).

the author responsible for correspondence tasks has been MARTIN SANTOS, HENAR.

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Awards linked to the item

Authors acknowledge their lab colleagues for the help provided by the persons who have collaborated on building the activities datasets, and Jesus Cano for his help developing the Activity Monitor. Henar Martin acknowledges the Spanish Ministry of Education for her grant. This work has been supported by the Spanish Ministry for Science and Innovation under grant TIN2011-28620- C02-02 and IPT2011-1052-390000 and the Government of Madrid under grant S2009/TIC-1485 (CONTEXTS).
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