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However, the magnetic field varies with time and position, which makes it necessary to calibrate sensors before tracking. To address the poor adaptability of IMU to the environments and improve the accuracy of estimated traces, this paper presents an ENN-based (Elman neural network) method to track human arm motions, which consists of two steps. First, the data derived from IMUs are preprocessed for the rough Euler angles; then, an ENN is trained to estimate motions. We explore the initially estimated position to calibrate the acceleration measurements as the input of the ENN. Real-world experiments of arm motion tracking are carried out with the ground truth from an optical motion tracking system. The experimental results show that the mean tracking errors are around 35\u2009mm, with a strong ability to eliminate the effect of extreme measurement and environment noises, avoiding calibrating the magnetometer. The implementation of the well-trained model to independent motions indicates that the robustness of the proposed method is excellent, and the errors reduce by 37.2% on the <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>x<\/a:mi>\n                     <\/a:math>\n                  <\/jats:inline-formula>-axis and perform similarly on the <jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>z<\/c:mi>\n                     <\/c:math>\n                  <\/jats:inline-formula>-axis compared with 4 traditional methods. This method quite suits those situations where trajectory tracking of the standardized motions is required, such as the medical habilitation.<\/jats:p>","DOI":"10.1155\/2022\/3926417","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T16:50:08Z","timestamp":1652719808000},"page":"1-11","source":"Crossref","is-referenced-by-count":4,"title":["Multi-Inertial Sensor-Based Arm 3D Motion Tracking Using Elman Neural Network"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1221-1561","authenticated-orcid":true,"given":"Haoran","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1734-2704","authenticated-orcid":true,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Xuzhou Comprehensive Testing Centre for Quality and Technology Supervision, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2240-6311","authenticated-orcid":true,"given":"Huiqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4656-2506","authenticated-orcid":true,"given":"Yunde","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9999-4513","authenticated-orcid":true,"given":"Lifeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5169-1110","authenticated-orcid":true,"given":"Yujin","family":"Kuang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2266-6682","authenticated-orcid":true,"given":"Yuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","first-page":"774","article-title":"Towards human motion tracking: an open-source platform based on multi-sensory fusion methods","author":"C. 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