Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.
Jump locomotion is the basic movement of human. However, no thorough research on the recognition of jump sub-phases has been carried so far. This paper aims to use multi-sensor information fusion and machine learning to recognize the human jump phase, which is crucial to the development of exoskelet...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 4; pp. 883 - 900 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149884711&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149884711 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2021 vid: 59 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149884711 149361424 149884711 NLM33745104 10.1007/s11517-021-02335-9 NLM33745104 149884711 ppf: 883 ppct: 17 formats: fmt: @attributes: type: P tig: atl: Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning. aug: au: Lu, Yanzheng Wang, Hong Hu, Fo Zhou, Bin Xi, Hailong affil: School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China sug: subj: Lower Extremity Locomotion Algorithms Electromyography Clinical Assessment Tools Arthritis Impact Measurement Scales ab: Jump locomotion is the basic movement of human. However, no thorough research on the recognition of jump sub-phases has been carried so far. This paper aims to use multi-sensor information fusion and machine learning to recognize the human jump phase, which is crucial to the development of exoskeleton that assists jumping. The method of information fusion for sensors including sEMG, IMU, and footswitch sensor is studied. The footswitch signals are filtered by median filter. A processing method of synthesizing Euler angles into phase angle is proposed, which is beneficial to data integration. The jump locomotion is creatively segmented into five phases. The onset and offset of active segment are detected by sample entropy of sEMG and standard deviation of acceleration signal. The features are extracted from analysis windows using multi-sensor information fusion, and the dimension of feature matrix is selected. By comparing the performances of state-of-the-art machine learning classifiers, feature subsets of sEMG, IMU, and footswitch signals are selected from time domain features in a series of analysis window parameters. The average recognition accuracy of sEMG and IMU is 91.76% and 97.68%, respectively. When using the combination of sEMG, IMU, and footswitch signals, the average accuracy is 98.70%, which outperforms the combination of sEMG and IMU (97.97%, p < 0.01). Graphical Abstract The sub-phases of human locomotion are recognized based on multi-sensor information fusion and machine learning method. The feature data of the sub-phases is visualized in 3-dimensional space. The predicted states and the true states in a complete jump are compared along the time axis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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