ION CACIULA, GIORGIAN MARIUS IONITA, HENRI GEORGE COANDA, DINU COLTUC, NICOLETA ANGELESCU, FELIX ALBU, DANIELA HAGIESCU
{"title":"用于长期监测的可穿戴步态装置","authors":"ION CACIULA, GIORGIAN MARIUS IONITA, HENRI GEORGE COANDA, DINU COLTUC, NICOLETA ANGELESCU, FELIX ALBU, DANIELA HAGIESCU","doi":"10.46939/j.sci.arts-23.3-c01","DOIUrl":null,"url":null,"abstract":"This study describes a low-cost and easy to deploy gait monitoring system that uses an ESP32 microcontroller and an ICM-20948 module. The ESP32 microcontroller collects data from the ICM-20948 module and these data are used to train a convolutional neural network (CNN) to classify gait patterns into two categories: normal and pathological. The results show that the system can achieve a high accuracy for binary gait classification, being able to correctly classify 97.05% of the normal gait samples and 84.54% of the pathological gait samples. The power consumption of the devive was measured using a calibrated and dual-acquisition digital multimeter. The estimated operating time was around 12 hours, with a battery capacity of 1800 mAh LiPo type. Therefore, it could be used to track the gait of patients with neurological disorders or to assess the effectiveness of gait rehabilitation treatments.","PeriodicalId":54169,"journal":{"name":"Journal of Science and Arts","volume":"68 1","pages":"0"},"PeriodicalIF":0.3000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WEARABLE GAIT DEVICE FOR LONG-TERM MONITORING\",\"authors\":\"ION CACIULA, GIORGIAN MARIUS IONITA, HENRI GEORGE COANDA, DINU COLTUC, NICOLETA ANGELESCU, FELIX ALBU, DANIELA HAGIESCU\",\"doi\":\"10.46939/j.sci.arts-23.3-c01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study describes a low-cost and easy to deploy gait monitoring system that uses an ESP32 microcontroller and an ICM-20948 module. The ESP32 microcontroller collects data from the ICM-20948 module and these data are used to train a convolutional neural network (CNN) to classify gait patterns into two categories: normal and pathological. The results show that the system can achieve a high accuracy for binary gait classification, being able to correctly classify 97.05% of the normal gait samples and 84.54% of the pathological gait samples. The power consumption of the devive was measured using a calibrated and dual-acquisition digital multimeter. The estimated operating time was around 12 hours, with a battery capacity of 1800 mAh LiPo type. Therefore, it could be used to track the gait of patients with neurological disorders or to assess the effectiveness of gait rehabilitation treatments.\",\"PeriodicalId\":54169,\"journal\":{\"name\":\"Journal of Science and Arts\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2023-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Science and Arts\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46939/j.sci.arts-23.3-c01\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Science and Arts","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46939/j.sci.arts-23.3-c01","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
This study describes a low-cost and easy to deploy gait monitoring system that uses an ESP32 microcontroller and an ICM-20948 module. The ESP32 microcontroller collects data from the ICM-20948 module and these data are used to train a convolutional neural network (CNN) to classify gait patterns into two categories: normal and pathological. The results show that the system can achieve a high accuracy for binary gait classification, being able to correctly classify 97.05% of the normal gait samples and 84.54% of the pathological gait samples. The power consumption of the devive was measured using a calibrated and dual-acquisition digital multimeter. The estimated operating time was around 12 hours, with a battery capacity of 1800 mAh LiPo type. Therefore, it could be used to track the gait of patients with neurological disorders or to assess the effectiveness of gait rehabilitation treatments.