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- Xu, Linghui, et al.
(author)
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Machine-learning-based children’s pathological gait classification with low-cost gait-recognition system
- 2021
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In: Biomedical engineering online. - : Springer Nature. - 1475-925X. ; 20:1
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Journal article (peer-reviewed)abstract
- Background: Pathological gaits of children m:y lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.
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| 3. |
- Yang, Jiansong, et al.
(author)
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The effects of dose staggering on metabolic drug-drug interactions
- 2003
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In: European Journal of Pharmaceutical Sciences. - : Elsevier. - 0928-0987 .- 1879-0720. ; 20:2, s. 223-232
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Journal article (peer-reviewed)abstract
- PURPOSETo investigate the effect of dose staggering on metabolic drug-drug interactions (MDDI).METHODSUsing Matlab, anatomical, physiological and biochemical data relating to human pharmacokinetics were integrated to create a representative virtual healthy subject relevant to in vivo studies. The effects of dose staggering on AUC and C(max) were investigated under various scenarios with respect to pharmacokinetic characteristics of the inhibitor and substrate drugs (e.g. hepatic extraction ratio). Specific cases were also simulated where MDDI had been studied experimentally for combinations of drugs (budesonide and ketoconazole; triazolam and itraconazole).RESULTSThe decrease in the magnitude of the inhibitory effect of the 'perpetrator' drug (inhibitor) on the 'victim' drug (substrate) as a result of 'dose staggering' was greater when the 'perpetrator' was given after the 'victim'. There was reasonable agreement between the predicted extent of the interactions and the observed in vivo data (mean prediction errors of 25 and -14% for AUC and C(max) values, respectively (n=7)). The impact of dose staggering was minimal during continuous dosage of inhibitors with long elimination half-lives (e.g. itraconazole, >20 h).CONCLUSIONSClinical trial simulations using physiological information may provide useful guidelines for optimal dose staggering when poly-pharmacy is inevitable.
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