Evidence map›Paper›PMID 36540410›Full record

ArticleMeasurement : journal of the International Measurement Confederation2021

Development of a measurement setup to detect the level of physical activity and social distancing of ageing people in a social garden during COVID-19 pandemic.

Sara Casaccia, Riccardo Naccarelli, Sara Moccia, Lucia Migliorelli, Emanuele Frontoni, Gian Marco Revel

Open access · greenAbstract read
In one paragraph

Article in Measurement : journal of the International Measurement Confederation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
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

3 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence.Measurement : journal of the International Measurement Confederation · 2022
    Article
  3. Article
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

6 authors at 2 institutions in 1 country.

Sara CasacciaDipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche, Ancona, Italy.
Riccardo NaccarelliDipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche, Ancona, Italy.
Sara MocciaBioRobotics Institute, Scuola Superiore Sant'Anna and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy.
Lucia MigliorelliDipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Ancona, Italy.
Emanuele FrontoniDipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Ancona, Italy.
Gian Marco RevelDipartimento di Ingegneria Industriale e Scienze Matematiche, Università Politecnica delle Marche, Ancona, Italy.
Marche Polytechnic University · ITScuola Superiore Sant'Anna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study defines a methodology to measure physical activity (PA) in ageing people working in a social garden while maintaining social distancing (SD) during COVID-19 pandemic. A real-time location system (RTLS) with embedded inertial measurement unit (IMU) sensors is used for measuring PA and SD. The position of each person is tracked to assess their SD, finding that the RTLS/IMU can measure the time in which interpersonal distance is not kept with a maximum uncertainty of 1.54 min, which compared to the 15-min. limit suggested to reduce risk of transmission at less than 1.5 m, proves the feasibility of the measurement. The data collected by the accelerometers of the IMU sensors are filtered using discrete wavelet transform and used to measure the PA in ageing people with an uncertainty-based thresholding method. PA and SD time measurements were demonstrated exploiting the experimental test in a pilot case with real users.

Indexed as

Acceleration signalsDiscrete wavelet transformInertial measurement sensorsPhysical activitySocial distancing

Identifiers

PMID36540410
PMCPMC9756140
OpenAlexW3190546904

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.