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Computer Methods in Biomechanics and Biomedical Engineering Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/gcmb20
Visualising gait symmetry/asymmetry from acceleration data a
Mitsuru Yoneyama a
Mitsubishi Chemical Group Science and Technology Research Centre, Inc., 1000 Kamoshida-cho, Aoba-ku, Yokohama, 227-8502, Japan Published online: 25 Nov 2013.
To cite this article: Mitsuru Yoneyama (2013): Visualising gait symmetry/asymmetry from acceleration data, Computer Methods in Biomechanics and Biomedical Engineering, DOI: 10.1080/10255842.2013.856892 To link to this article: http://dx.doi.org/10.1080/10255842.2013.856892
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Computer Methods in Biomechanics and Biomedical Engineering, 2013 http://dx.doi.org/10.1080/10255842.2013.856892
Visualising gait symmetry/asymmetry from acceleration data Mitsuru Yoneyama* Mitsubishi Chemical Group Science and Technology Research Centre, Inc., 1000 Kamoshida-cho, Aoba-ku, Yokohama 227-8502, Japan
Downloaded by [Utah State University Libraries] at 07:57 27 September 2014
(Received 12 September 2013; accepted 15 October 2013) Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation. Keywords: asymmetric gait; accelerometry; 3D visualisation; biphasicity
1.
Introduction
Gait symmetry/asymmetry is an important aspect of human walking. It is widely assumed that a healthy gait is symmetrical and the presence of an asymmetric pattern is a sign of gait abnormality. Although this assumption has not been thoroughly tested (Sadeghi et al. 2000; HsiaoWecksler et al. 2010), various unique measures of gait symmetry, such as symmetry index (Robinson et al. 1987) and phase coordination index (Plotnik et al. 2007), have been developed which may be applicable to the clinical assessment of pathological or injured gait. Standard instruments for analysing gait include multicamera motion capture systems and electronic walkways. These methods provide accurate and reliable information, but are expensive, time-consuming and require special laboratory equipment. Recent developments in microelectromechanical systems technology have enabled the application of wearable inertial sensors, such as accelerometers and gyroscopes, as gait monitoring tools in the clinical setting. The major advantages of inertial sensors are their low cost, long operational lifetime and small size, allowing for unobtrusive monitoring of a person’s walking pattern without interfering with his/her natural motion dynamics. Based on these sensor technologies, researchers have constructed effective methodologies for quantifying gait asymmetry (Sant’anna and Wickstro¨m 2010; Gouwanda and Senanayake 2011). Despite the favourable results attained by inertial sensor-based analysis so far, technical issues remain to be solved to gain wider clinical application. First, reported systems mostly utilise multiple sensors placed on different body parts, e.g. on both legs. In clinical practice, there is a growing demand for rapid methods that are easier to use for objective gait
*Email: [email protected] q 2013 Taylor & Francis
assessment. To meet such demands, a single-accelerometry approach may be promising. Second, it would be of great clinical use if the degree of gait asymmetry could be visualised during rehabilitation. Quantitative measures are essential in capturing the nature of human walking, but the use of algebraic and statistical indexes only makes it difficult for both clinicians and patients to interpret the effect of intervention and rehabilitation. Therefore, 3D visualisation appealing to the eye should be incorporated in the presentation of examination results. This paper presents a novel algorithm to derive informative metrics for visualising gait symmetry/asymmetry in 3D space from motion signals measured by a single trunk-mounted accelerometer. We develop a self-adaptive procedure to estimate motion trajectory (relative velocity or displacement) during walking from the acceleration signal. Then, the 3D autocorrelation spectrum and 3D biphasicity score of the trajectory are computed as a way of 3D visualisation. The whole algorithm works automatically, requiring neither manual tuning nor subject-specific parameters. In addition, the performance of the proposed algorithm was tested for gait data collected from 245 healthy adult subjects.
2.
Methods
In this study, the gait cycle is defined as the time interval from the contact of one foot with the ground to the next contact of the same foot. Henceforth, the notations TG and TMAX are used to refer to the gait cycle and the maximum time scale of interest, respectively. For each gait signal, TG is roughly estimated beforehand, e.g. by using the unbiased auto-
2
M. Yoneyama
correlation procedure (Moe-Nilssenand and Helbostad 2004). Then, TMAX is set to a value
Computer Methods in Biomechanics and Biomedical Engineering Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/gcmb20
Visualising gait symmetry/asymmetry from acceleration data a
Mitsuru Yoneyama a
Mitsubishi Chemical Group Science and Technology Research Centre, Inc., 1000 Kamoshida-cho, Aoba-ku, Yokohama, 227-8502, Japan Published online: 25 Nov 2013.
To cite this article: Mitsuru Yoneyama (2013): Visualising gait symmetry/asymmetry from acceleration data, Computer Methods in Biomechanics and Biomedical Engineering, DOI: 10.1080/10255842.2013.856892 To link to this article: http://dx.doi.org/10.1080/10255842.2013.856892
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Computer Methods in Biomechanics and Biomedical Engineering, 2013 http://dx.doi.org/10.1080/10255842.2013.856892
Visualising gait symmetry/asymmetry from acceleration data Mitsuru Yoneyama* Mitsubishi Chemical Group Science and Technology Research Centre, Inc., 1000 Kamoshida-cho, Aoba-ku, Yokohama 227-8502, Japan
Downloaded by [Utah State University Libraries] at 07:57 27 September 2014
(Received 12 September 2013; accepted 15 October 2013) Accelerometry-based quantification of gait symmetry/asymmetry is a promising approach for objectively evaluating gait dysfunctions. An important step in the application of this method in clinical settings is to develop reliable gait asymmetry measures and tools for visualising them to create easy-to-understand presentations for both clinicians and patients. This paper describes a new self-adaptive algorithm for estimating motion trajectory from acceleration data and visualising the degree of its asymmetry in 3D space. Two new parameters are introduced to capture asymmetric walking patterns based on the assessment of 3D autocorrelation and biphasicity of the motion trajectory. The performance of our algorithm is confirmed by analysing gait data collected from 245 healthy subjects. The proposed method may be clinically useful in tracking the process of recovering from pathology or injury after rehabilitation. Keywords: asymmetric gait; accelerometry; 3D visualisation; biphasicity
1.
Introduction
Gait symmetry/asymmetry is an important aspect of human walking. It is widely assumed that a healthy gait is symmetrical and the presence of an asymmetric pattern is a sign of gait abnormality. Although this assumption has not been thoroughly tested (Sadeghi et al. 2000; HsiaoWecksler et al. 2010), various unique measures of gait symmetry, such as symmetry index (Robinson et al. 1987) and phase coordination index (Plotnik et al. 2007), have been developed which may be applicable to the clinical assessment of pathological or injured gait. Standard instruments for analysing gait include multicamera motion capture systems and electronic walkways. These methods provide accurate and reliable information, but are expensive, time-consuming and require special laboratory equipment. Recent developments in microelectromechanical systems technology have enabled the application of wearable inertial sensors, such as accelerometers and gyroscopes, as gait monitoring tools in the clinical setting. The major advantages of inertial sensors are their low cost, long operational lifetime and small size, allowing for unobtrusive monitoring of a person’s walking pattern without interfering with his/her natural motion dynamics. Based on these sensor technologies, researchers have constructed effective methodologies for quantifying gait asymmetry (Sant’anna and Wickstro¨m 2010; Gouwanda and Senanayake 2011). Despite the favourable results attained by inertial sensor-based analysis so far, technical issues remain to be solved to gain wider clinical application. First, reported systems mostly utilise multiple sensors placed on different body parts, e.g. on both legs. In clinical practice, there is a growing demand for rapid methods that are easier to use for objective gait
*Email: [email protected] q 2013 Taylor & Francis
assessment. To meet such demands, a single-accelerometry approach may be promising. Second, it would be of great clinical use if the degree of gait asymmetry could be visualised during rehabilitation. Quantitative measures are essential in capturing the nature of human walking, but the use of algebraic and statistical indexes only makes it difficult for both clinicians and patients to interpret the effect of intervention and rehabilitation. Therefore, 3D visualisation appealing to the eye should be incorporated in the presentation of examination results. This paper presents a novel algorithm to derive informative metrics for visualising gait symmetry/asymmetry in 3D space from motion signals measured by a single trunk-mounted accelerometer. We develop a self-adaptive procedure to estimate motion trajectory (relative velocity or displacement) during walking from the acceleration signal. Then, the 3D autocorrelation spectrum and 3D biphasicity score of the trajectory are computed as a way of 3D visualisation. The whole algorithm works automatically, requiring neither manual tuning nor subject-specific parameters. In addition, the performance of the proposed algorithm was tested for gait data collected from 245 healthy adult subjects.
2.
Methods
In this study, the gait cycle is defined as the time interval from the contact of one foot with the ground to the next contact of the same foot. Henceforth, the notations TG and TMAX are used to refer to the gait cycle and the maximum time scale of interest, respectively. For each gait signal, TG is roughly estimated beforehand, e.g. by using the unbiased auto-
2
M. Yoneyama
correlation procedure (Moe-Nilssenand and Helbostad 2004). Then, TMAX is set to a value
