Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements.
adaptive neuro-fuzzy inference system
physiotherapy exercise
rehabilitation recognition
sensor-enabled wristband
ubiquitous healthcare measurement.
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
08 Apr 2019
08 Apr 2019
Historique:
received:
01
02
2019
revised:
02
04
2019
accepted:
02
04
2019
entrez:
11
4
2019
pubmed:
11
4
2019
medline:
3
8
2019
Statut:
epublish
Résumé
The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.
Identifiants
pubmed: 30965675
pii: s19071679
doi: 10.3390/s19071679
pmc: PMC6479922
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Ministry of Science and Technology, Taiwan
ID : MOST 105-2119-M-039-003, 106-2119-M-039-002, 106-2221-E-155-020, 107-2119-M-039-002
Organisme : China Medical University, Taiwan
ID : CMU106-S-29
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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