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      • Record: found
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      Gait Analysis Using Wearable Sensors

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          Abstract

          Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.

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          Most cited references235

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          An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.

          D Lloyd, Thor Besier (2003)
          This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.
          Article 0 comments Cited 210 times – based on 0 reviews     Review now
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            Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing.

            R Sabatini (2006)
            In this paper, a quaternion based extended Kalman filter (EKF) is developed for determining the orientation of a rigid body from the outputs of a sensor which is configured as the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The suggested applications are for studies in the field of human movement. In the proposed EKF, the quaternion associated with the body rotation is included in the state vector together with the bias of the aiding system sensors. Moreover, in addition to the in-line procedure of sensor bias compensation, the measurement noise covariance matrix is adapted, to guard against the effects which body motion and temporary magnetic disturbance may have on the reliability of measurements of gravity and earth's magnetic field, respectively. By computer simulations and experimental validation with human hand orientation motion signals, improvements in the accuracy of orientation estimates are demonstrated for the proposed EKF, as compared with filter implementations where either the in-line calibration procedure, the adaptive mechanism for weighting the measurements of the aiding system sensors, or both are not implemented.
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              Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring.

              Françios J G Vingerhoets, Arash Salarian, Catherine Dehollain … (2004)
              An ambulatory gait analysis method using body-attached gyroscopes to estimate spatio-temporal parameters of gait has been proposed and validated against a reference system for normal and pathologic gait. Later, ten Parkinson's disease (PD) patients with subthalamic nucleus deep brain stimulation (STN-DBS) implantation participated in gait measurements using our device. They walked one to three times on a 20-m walkway. Patients did the test twice: once STN-DBS was ON and once 180 min after turning it OFF. A group of ten age-matched normal subjects were also measured as controls. For each gait cycle, spatio-temporal parameters such as stride length (SL), stride velocity (SV), stance (ST), double support (DS), and gait cycle time (GC) were calculated. We found that PD patients had significantly different gait parameters comparing to controls. They had 52% less SV, 60% less SL, and 40% longer GC. Also they had significantly longer ST and DS (11% and 59% more, respectively) than controls. STN-DBS significantly improved gait parameters. During the stim ON period, PD patients had 31% faster SV, 26% longer SL, 6% shorter ST, and 26% shorter DS. GC, however, was not significantly different. Some of the gait parameters had high correlation with Unified Parkinson's Disease Rating Scale (UPDRS) subscores including SL with a significant correlation (r = -0.90) with UPDRS gait subscore. We concluded that our method provides a simple yet effective way of ambulatory gait analysis in PD patients with results confirming those obtained from much more complex and expensive methods used in gait labs.
              Article 0 comments Cited 146 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Title: Sensors (Basel, Switzerland)
                Publisher: Molecular Diversity Preservation International (MDPI)
                ISSN (Electronic): 1424-8220
                Publication date Collection: 2012
                Publication date (Electronic): 16 February 2012
                Volume: 12
                Issue: 2
                Pages: 2255-2283
                Affiliations
                [1 ] School of Mechanical Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China; E-Mails: taoweijun01@ 123456163.com (W.T.); fenght@ 123456mail.njust.edu.cn (H.F.)
                [2 ] Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, 185 MIyanokuchi, Tosayamada-Cho, Kami-City, Kochi 782-8502, Japan
                [3 ] Nakano Lab, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan; E-Mail: topzhengrencheng@ 123456hotmail.com
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: liu.tao@ 123456kochi-tech.ac.jp ; Tel.: +81-887-57-2177; Fax: +81-887-57-2170.
                Article
                Publisher ID: sensors-12-02255
                DOI: 10.3390/s120202255
                PMC ID: 3304165
                PubMed ID: 22438763
                SO-VID: 712f1017-ba34-4d4d-9e5c-054e8a36dac9
                Copyright © © 2012 by the authors; licensee MDPI, Basel, Switzerland
                License:

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                Date received : 18 January 2012
                Date revision received : 10 February 2012
                Date accepted : 13 February 2012
                Categories
                Subject: Review

                ScienceOpen disciplines: Biomedical engineering
                Keywords: electromyography,wearable sensors,clinical application,human kinematics,joint kinetics,gait analysis
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: electromyography, wearable sensors, clinical application, human kinematics, joint kinetics, gait analysis

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