Evidence map›Paper›PMID 32050576›Full record

ArticleSensors (Basel, Switzerland)2020

Monitoring Movements of Ataxia Patient by Using UWB Technology.

Tanjila Akter Zilani, Fadi Al-Turjman, Muhammad Bilal Khan, Nan Zhao, Xiaodong Yang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. The use of digital tools in rare neurological diseases towards a new care model: a narrative review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024
    Review
  3. Article
  4. Article
  5. 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

5 authors.

Tanjila Akter ZilaniSchool of Electronic Engineering, Xidian University, Xi'an 710071, China.
Fadi Al-TurjmanArtificial Intelligence Engineering Department, Near East University, 99138 Nicosia, Mersin 10, Turkey.ORCID 0000-0001-6375-4123
Muhammad Bilal KhanSchool of Electronic Engineering, Xidian University, Xi'an 710071, China.
Nan ZhaoSchool of Electronic Engineering, Xidian University, Xi'an 710071, China.
Xiaodong YangSchool of Electronic Engineering, Xidian University, Xi'an 710071, China.

Funding

National Natural Science Foundation of China 61301175
6 · The paper itself

Abstract

Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.

Indexed as

MovementAdultAtaxiaHumansMonitoring, PhysiologicSupport Vector MachineWalkingAtaxiaIoMTSVMUWB

Identifiers

PMID32050576
PMCPMC7039007

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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.