Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
| Autor: | L. Miro-Amarante, Francisco Luna-Perejon, José L. Sevillano-Ramos, Marilo Hernandez-Velazquez, Manuel Domínguez-Morales |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
lcsh:Technology law.invention lcsh:Chemistry Bluetooth 03 medical and health sciences embedded system 0302 clinical medicine Gait (human) law General Materials Science Computer vision lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes Foot (prosody) Artificial neural network lcsh:T business.industry Process Chemistry and Technology Deep learning Work (physics) General Engineering 030229 sport sciences neural networks Pressure sensor lcsh:QC1-999 Computer Science Applications Microcontroller biomechanical study ComputingMethodologies_PATTERNRECOGNITION lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 e-Health Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business footstep lcsh:Physics 030217 neurology & neurosurgery |
| Zdroj: | Applied Sciences Volume 9 Issue 19 Applied Sciences, Vol 9, Iss 19, p 3970 (2019) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app9193970 |
| Popis: | Abnormal foot postures during gait are common sources of pain and pathologies of the lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure measurement system is developed to identify areas with higher or lower pressure load. This system is composed of an embedded system placed in the insole and a user application. The instrumented insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth module. The user application receives and shows the insole pressure information in real-time and, finally, provides information about the foot posture. In order to identify the different pressure states and obtain the final information of the study with greater accuracy, a Deep Learning neural network system has been integrated into the user application. The neural network can be trained using a stored dataset in order to obtain the classification results in real-time. Results prove that this system provides an accuracy over 90% using a training dataset of 3000+ steps from 6 different users. |
| Databáze: | OpenAIRE |
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