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

• Published in: Journal of healthcare engineering Vol. 2017; no. 2017; pp. 1 - 11
• Main Authors: Wu, Zhengzhi, Wang, Yulong, Shang, Wanfeng, Liu, Qihong, Wang, Chunbao, Li, Meng, Duan, Lihong, Liu, Quanquan, Li, Hua, Sun, Tongyang, Li, Wei-guang
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2017; Hindawi; John Wiley & Sons, Inc
• Subjects: Biomechanical Phenomena; Gait; Health aspects; Hemiplegia - rehabilitation; Humans; Inertial navigation systems; Injuries; Leg; Lower Extremity - physiology; Methods; Motion capture; Range of Motion, Articular; Rehabilitation; Stroke Rehabilitation; Technology application; Wireless Technology - instrumentation
• ISSN: 2040-2295; 2040-2309
• DOI: 10.1155/2017/1949170

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.