Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2020 Feb 27

PMID 32098239

Citations 13

Authors

Sen Qiu

Huihui Wang

Jie Li

Hongyu Zhao

Zhelong Wang

Jiaxin Wang

Qiong Wang

Dirk Plettemeier

Michael Barhold

Tony Bauer

Bo Ru

Affiliations

Soon will be listed here.

Abstract

Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.

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References

Hayati H, Mahdavi F, Eager D . Analysis of Agile Canine Gait Characteristics Using Accelerometry. Sensors (Basel). 2019; 19(20). PMC: 6832749. DOI: 10.3390/s19204379. View

Hegde N, Zhang T, Uswatte G, Taub E, Barman J, McKay S . The Pediatric SmartShoe: Wearable Sensor System for Ambulatory Monitoring of Physical Activity and Gait. IEEE Trans Neural Syst Rehabil Eng. 2018; 26(2):477-486. DOI: 10.1109/TNSRE.2017.2786269. View

Cresswell K, Shin Y, Chen S . Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models. Sensors (Basel). 2017; 17(3). PMC: 5375752. DOI: 10.3390/s17030466. View

Schicketmueller A, Rose G, Hofmann M . Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training. Sensors (Basel). 2019; 19(21). PMC: 6864796. DOI: 10.3390/s19214804. View

Farris R, Quintero H, Murray S, Ha K, Hartigan C, Goldfarb M . A preliminary assessment of legged mobility provided by a lower limb exoskeleton for persons with paraplegia. IEEE Trans Neural Syst Rehabil Eng. 2013; 22(3):482-90. PMC: 4476394. DOI: 10.1109/TNSRE.2013.2268320. View

Wang Q, Markopoulos P, Yu B, Chen W, Timmermans A . Interactive wearable systems for upper body rehabilitation: a systematic review. J Neuroeng Rehabil. 2017; 14(1):20. PMC: 5346195. DOI: 10.1186/s12984-017-0229-y. View

Yu L, Zheng J, Wang Y, Song Z, Zhan E . Adaptive method for real-time gait phase detection based on ground contact forces. Gait Posture. 2014; 41(1):269-75. DOI: 10.1016/j.gaitpost.2014.10.019. View

Sensinger J, Intawachirarat N, Gard S . Contribution of prosthetic knee and ankle mechanisms to swing-phase foot clearance. IEEE Trans Neural Syst Rehabil Eng. 2012; 21(1):74-80. DOI: 10.1109/TNSRE.2012.2224885. View

Bao S, Meng X, Xiao W, Zhang Z . Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking. Sensors (Basel). 2017; 17(2). PMC: 5336044. DOI: 10.3390/s17020340. View

10.

Skog I, Handel P, Nilsson J, Rantakokko J . Zero-velocity detection --- an algorithm evaluation. IEEE Trans Biomed Eng. 2010; 57(11). DOI: 10.1109/TBME.2010.2060723. View

11.

Qiu S, Liu L, Zhao H, Wang Z, Jiang Y . MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion. Micromachines (Basel). 2018; 9(9). PMC: 6187565. DOI: 10.3390/mi9090442. View

12.

Barth J, Oberndorfer C, Pasluosta C, Schulein S, Gassner H, Reinfelder S . Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data. Sensors (Basel). 2015; 15(3):6419-40. PMC: 4435165. DOI: 10.3390/s150306419. View

13.

Brennan A, Zhang J, Deluzio K, Li Q . Quantification of inertial sensor-based 3D joint angle measurement accuracy using an instrumented gimbal. Gait Posture. 2011; 34(3):320-3. DOI: 10.1016/j.gaitpost.2011.05.018. View

14.

Chiang C, Chen K, Liu K, Hsu S, Chan C . Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty. Sensors (Basel). 2017; 17(2). PMC: 5336055. DOI: 10.3390/s17020418. View

15.

Zhao H, Cheng W, Yang N, Qiu S, Wang Z, Wang J . Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion. Sensors (Basel). 2019; 19(20). PMC: 6832213. DOI: 10.3390/s19204554. View

16.

Tao W, Liu T, Zheng R, Feng H . Gait analysis using wearable sensors. Sensors (Basel). 2012; 12(2):2255-83. PMC: 3304165. DOI: 10.3390/s120202255. View

17.

van Schooten K, Pijnappels M, Lord S, van Dieen J . Quality of Daily-Life Gait: Novel Outcome for Trials that Focus on Balance, Mobility, and Falls. Sensors (Basel). 2019; 19(20). PMC: 6832726. DOI: 10.3390/s19204388. View

18.

OBrien M, Hidalgo-Araya M, Mummidisetty C, Vallery H, Ghaffari R, Rogers J . Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults. Sensors (Basel). 2019; 19(20). PMC: 6832985. DOI: 10.3390/s19204537. View