Anthony Giachin, J. J. Steckenrider, Gregory M Freisinger
{"title":"利用惯性测量单元估计姿态控制的数据驱动方法","authors":"Anthony Giachin, J. J. Steckenrider, Gregory M Freisinger","doi":"10.1115/imece2021-70518","DOIUrl":null,"url":null,"abstract":"\n In this paper, we propose a probabilistic multi-Gaussian parameter estimation technique which addresses the complex relationship between acceleration and ground force signals used to derive a human’s static center of pressure. The intent of this work is to develop an accurate accelerometer-based method for determining postural control and neuromuscular status which is more portable and cost-effective than force plate-based techniques. Acceleration data was collected using an inertial measurement unit while ground reaction forces were simultaneously measured using a force plate. Various metrics were calculated from both sensors and probabilistic data models were built to characterize the relationships between the two sensors. These models were used to predict force-based postural control metrics corresponding to observed acceleration metrics. Data collected from one participant was used as a training set to which the test data of two individuals were then applied. We conclude that converted acceleration-based metrics on average can accurately predict all the corresponding force-based metrics we studied here. Furthermore, the proposed multi-Gaussian parameter estimation approach outperforms a more basic linear transformation technique for 75% of the metrics studied, as evidenced by an increase in correlation coefficients between true and estimated force plate metrics.","PeriodicalId":314012,"journal":{"name":"Volume 5: Biomedical and Biotechnology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Data-Driven Approach for Estimating Postural Control Using an Inertial Measurement Unit\",\"authors\":\"Anthony Giachin, J. J. Steckenrider, Gregory M Freisinger\",\"doi\":\"10.1115/imece2021-70518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n In this paper, we propose a probabilistic multi-Gaussian parameter estimation technique which addresses the complex relationship between acceleration and ground force signals used to derive a human’s static center of pressure. The intent of this work is to develop an accurate accelerometer-based method for determining postural control and neuromuscular status which is more portable and cost-effective than force plate-based techniques. Acceleration data was collected using an inertial measurement unit while ground reaction forces were simultaneously measured using a force plate. Various metrics were calculated from both sensors and probabilistic data models were built to characterize the relationships between the two sensors. These models were used to predict force-based postural control metrics corresponding to observed acceleration metrics. Data collected from one participant was used as a training set to which the test data of two individuals were then applied. We conclude that converted acceleration-based metrics on average can accurately predict all the corresponding force-based metrics we studied here. Furthermore, the proposed multi-Gaussian parameter estimation approach outperforms a more basic linear transformation technique for 75% of the metrics studied, as evidenced by an increase in correlation coefficients between true and estimated force plate metrics.\",\"PeriodicalId\":314012,\"journal\":{\"name\":\"Volume 5: Biomedical and Biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 5: Biomedical and Biotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/imece2021-70518\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 5: Biomedical and Biotechnology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/imece2021-70518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Data-Driven Approach for Estimating Postural Control Using an Inertial Measurement Unit
In this paper, we propose a probabilistic multi-Gaussian parameter estimation technique which addresses the complex relationship between acceleration and ground force signals used to derive a human’s static center of pressure. The intent of this work is to develop an accurate accelerometer-based method for determining postural control and neuromuscular status which is more portable and cost-effective than force plate-based techniques. Acceleration data was collected using an inertial measurement unit while ground reaction forces were simultaneously measured using a force plate. Various metrics were calculated from both sensors and probabilistic data models were built to characterize the relationships between the two sensors. These models were used to predict force-based postural control metrics corresponding to observed acceleration metrics. Data collected from one participant was used as a training set to which the test data of two individuals were then applied. We conclude that converted acceleration-based metrics on average can accurately predict all the corresponding force-based metrics we studied here. Furthermore, the proposed multi-Gaussian parameter estimation approach outperforms a more basic linear transformation technique for 75% of the metrics studied, as evidenced by an increase in correlation coefficients between true and estimated force plate metrics.