Evidence map›Paper›PMID 28972556›Full record

ArticleSensors (Basel, Switzerland)2017

Activity Level Assessment Using a Smart Cushion for People with a Sedentary Lifestyle.

Congcong Ma, Wenfeng Li, Raffaele Gravina, Jingjing Cao, Qimeng Li, Giancarlo Fortino

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.5field-weighted citation impact, top 10% 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

12 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Work Engagement Recognition in Smart Office.Procedia computer science · 2022
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. How to Measure Sedentary Behavior at Work?Frontiers in public health · 2019
    Review
  11. Article
  12. 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 2 countries.

Congcong MaSchool of Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China. macc@whut.edu.cn.ORCID 0000-0002-1579-2229
Wenfeng LiSchool of Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China. liwf@whut.edu.cn.
Raffaele GravinaDepartment of Informatics, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, Italy. rgravina@dimes.unical.it.
Jingjing CaoSchool of Logistics Engineering, Wuhan University of Technology, Wuhan 430070, China. bettycao@whut.edu.cn.
Qimeng LiDepartment of Informatics, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, Italy. liqimeng@msn.com.
Giancarlo FortinoDepartment of Informatics, Modeling, Electronics and Systems, University of Calabria, 87036 Rende, Italy. g.fortino@unical.it.ORCID 0000-0002-4039-891X
University of Calabria · ITWuhan University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a sedentary lifestyle leads to numerous health problems, it is important to keep constant motivation for a more active lifestyle. A large majority of the worldwide population, such as office workers, long journey vehicle drivers and wheelchair users, spends several hours every day in sedentary activities. The postures that sedentary lifestyle users assume during daily activities hide valuable information that can reveal their wellness and general health condition. Aiming at mining such underlying information, we developed a cushion-based system to assess their activity levels and recognize the activity from the information hidden in sitting postures. By placing the smart cushion on the chair, we can monitor users' postures and body swings, using the sensors deployed in the cushion. Specifically, we construct a body posture analysis model to recognize sitting behaviors. In addition, we provided a smart cushion that effectively combine pressure and inertial sensors. Finally, we propose a method to assess the activity levels based on the evaluation of the activity assessment index (AAI) in time sliding windows. Activity level assessment can be used to provide statistical results in a defined period and deliver recommendation exercise to the users. For practical implications and actual significance of results, we selected wheelchair users among the participants to our experiments. Features in terms of standard deviation and approximate entropy were compared to recognize the activities and activity levels. The results showed that, using the novel designed smart cushion and the standard deviation features, we are able to achieve an accuracy of (>89%) for activity recognition and (>98%) for activity level recognition.

Indexed as

Sedentary BehaviorExerciseHousehold ArticlesHumansPosturePressure UlcerWheelchairsactivity assessment indexactivity level assessmentactivity recognitionbody posture analysis modelsmart cushion

Identifiers

PMID28972556
PMCPMC5677409
OpenAlexW2762241698

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.