Evidence map›Paper›PMID 30501111›Full record

ArticleSensors (Basel, Switzerland)2018

Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors.

Samanta Rosati, Gabriella Balestra, Marco Knaflitz

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Intelligent Sports Weights.Sensors (Basel, Switzerland) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Samanta RosatiDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy. samanta.rosati@polito.it.ORCID 0000-0003-0620-594X
Gabriella BalestraDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy. gabriella.balestra@polito.it.ORCID 0000-0003-2717-648X
Marco KnaflitzDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy. marco.knaflitz@polito.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to compare two different feature sets for HAR. Particularly, we compared a set including time, frequency, and time-frequency domain features widely used in literature (

Indexed as

Human ActivitiesWearable Electronic DevicesBiosensing TechniquesHealthy VolunteersHumansMilitary PersonnelMonitoring, PhysiologicSupport Vector Machineclassifier optimizationfeature selectiongenetic algorithmhuman activity recognitionmachine learningMIMUwearable sensors

Identifiers

PMID30501111
PMCPMC6308535

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.