Literature DB >> 29072400

Inertial Sensor-Based Motion Analysis of Lower Limbs for Rehabilitation Treatments

Tongyang Sun1, Hua Li2, Quanquan Liu1,3, Lihong Duan3, Meng Li3, Chunbao Wang1,3,4, Qihong Liu1, Weiguang Li1, Wanfeng Shang3, Zhengzhi Wu3, Yulong Wang2.   

Abstract

The hemiplegic rehabilitation state diagnosing performed by therapists can be biased due to their subjective experience, which may deteriorate the rehabilitation effect. In order to improve this situation, a quantitative evaluation is proposed. Though many motion analysis systems are available, they are too complicated for practical application by therapists. In this paper, a method for detecting the motion of human lower limbs including all degrees of freedom (DOFs) via the inertial sensors is proposed, which permits analyzing the patient's motion ability. This method is applicable to arbitrary walking directions and tracks of persons under study, and its results are unbiased, as compared to therapist qualitative estimations. Using the simplified mathematical model of a human body, the rotation angles for each lower limb joint are calculated from the input signals acquired by the inertial sensors. Finally, the rotation angle versus joint displacement curves are constructed, and the estimated values of joint motion angle and motion ability are obtained. The experimental verification of the proposed motion detection and analysis method was performed, which proved that it can efficiently detect the differences between motion behaviors of disabled and healthy persons and provide a reliable quantitative evaluation of the rehabilitation state.
© 2017 Tongyang Sun et al.

Entities:  

Year:  2017        PMID: 29072400     DOI: 10.1155/2017/1949170

Source DB:  PubMed          Journal:  J Healthc Eng        ISSN: 2040-2295            Impact factor:   2.682


  2 in total

1.  Ankle Angle Prediction Using a Footwear Pressure Sensor and a Machine Learning Technique.

Authors:  Zachary Choffin; Nathan Jeong; Michael Callihan; Savannah Olmstead; Edward Sazonov; Sarah Thakral; Camilee Getchell; Vito Lombardi
Journal:  Sensors (Basel)       Date:  2021-05-30       Impact factor: 3.847

2.  Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation.

Authors:  Bor-Shing Lin; I-Jung Lee; Shu-Yu Yang; Yi-Chiang Lo; Junghsi Lee; Jean-Lon Chen
Journal:  Sensors (Basel)       Date:  2018-05-13       Impact factor: 3.847

  2 in total

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