PMID- 32050576
OWN - NLM
STAT- MEDLINE
DCOM- 20201110
LR  - 20240328
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 20
IP  - 3
DP  - 2020 Feb 10
TI  - Monitoring Movements of Ataxia Patient by Using UWB Technology.
LID - 10.3390/s20030931 [doi]
LID - 931
AB  - 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%.
FAU - Zilani, Tanjila Akter
AU  - Zilani TA
AD  - School of Electronic Engineering, Xidian University, Xi'an 710071, China.
FAU - Al-Turjman, Fadi
AU  - Al-Turjman F
AUID- ORCID: 0000-0001-6375-4123
AD  - Artificial Intelligence Engineering Department, Near East University, 99138 
      Nicosia, Mersin 10, Turkey.
AD  - Research Centre for AI and IoT, Near East University, 99138 Nicosia, Mersin 10, 
      Turkey.
FAU - Khan, Muhammad Bilal
AU  - Khan MB
AD  - School of Electronic Engineering, Xidian University, Xi'an 710071, China.
FAU - Zhao, Nan
AU  - Zhao N
AD  - School of Electronic Engineering, Xidian University, Xi'an 710071, China.
FAU - Yang, Xiaodong
AU  - Yang X
AD  - School of Electronic Engineering, Xidian University, Xi'an 710071, China.
LA  - eng
GR  - 61301175/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20200210
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Adult
MH  - Ataxia/*physiopathology
MH  - Humans
MH  - Monitoring, Physiologic/*methods
MH  - *Movement
MH  - Support Vector Machine
MH  - Walking
PMC - PMC7039007
OTO - NOTNLM
OT  - Ataxia
OT  - IoMT
OT  - SVM
OT  - UWB
COIS- The authors declare no conflict of interest.
EDAT- 2020/02/14 06:00
MHDA- 2020/11/11 06:00
PMCR- 2020/02/01
CRDT- 2020/02/14 06:00
PHST- 2020/01/05 00:00 [received]
PHST- 2020/02/07 00:00 [revised]
PHST- 2020/02/07 00:00 [accepted]
PHST- 2020/02/14 06:00 [entrez]
PHST- 2020/02/14 06:00 [pubmed]
PHST- 2020/11/11 06:00 [medline]
PHST- 2020/02/01 00:00 [pmc-release]
AID - s20030931 [pii]
AID - sensors-20-00931 [pii]
AID - 10.3390/s20030931 [doi]
PST - epublish
SO  - Sensors (Basel). 2020 Feb 10;20(3):931. doi: 10.3390/s20030931.