Evidence map›Paper›PMID 34641012›Full record

ArticleSensors (Basel, Switzerland)2021

A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction.

Abdul Hannan, Muhammad Zohaib Shafiq, Faisal Hussain, Ivan Miguel Pires

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 7% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Real-Time Tracking of Human Neck Postures and Movements.Healthcare (Basel, Switzerland) · 2021
    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

4 authors at 4 institutions in 3 countries.

Abdul HannanKnowledge Unit of System and Technology, University of Management and Technology, Sialkot 51310, Pakistan.ORCID 0000-0001-9426-9470
Muhammad Zohaib ShafiqDepartment of Computer Science and Engineering, Università di Bologna, 40126 Bologna, Italy.ORCID 0000-0003-3526-6733
Faisal HussainAl-Khwarizmi Institute of Computer Science (KICS), University of Engineering & Technology (UET), Lahore 54890, Pakistan.ORCID 0000-0001-9812-7488
Ivan Miguel PiresInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal.ORCID 0000-0002-3394-6762
University of Bologna · ITUniversity of Engineering and Technology Lahore · PKUniversity of Management and Technology · PKUniversity of Trás-os-Montes and Alto Douro · PT

Funding

Fundação para a Ciência e a Tecnologia UIDB/50008/2020
6 · The paper itself

Abstract

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today's time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature.

Indexed as

COVID-19PandemicsExerciseHumansPostureSARS-CoV-2cloud computinghuman activity recognition (HAR)Internet of Things (IoT)machine learning (ML)posture correctionsmart fitness suitesmart gymsmart health

Identifiers

PMID34641012
PMCPMC8512175
OpenAlexW3204238305

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.