An Ambulatory Gait Monitoring System with Activity Classification and Gait Parameter Calculation Based on a Single Foot Inertial Sensor

Goal: For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able t...

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Published in:	IEEE transactions on biomedical engineering Vol. 65; no. 4; pp. 885 - 893
Main Authors:	Song, Minsu, Kim, Jonghyun
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.04.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Acceleration Accelerometry - instrumentation Adult Algorithms Classification Classification algorithms Female Foot Foot - physiology Gait Gait - physiology gait activity classification Gait analysis Gait recognition Human Activities - classification Humans inertial measurement unit Inertial sensing devices Legged locomotion Male Monitoring Monitoring, Ambulatory - instrumentation Monitoring, Ambulatory - methods Parameter estimation ramp walk Signal Processing, Computer-Assisted - instrumentation stair walk System effectiveness Three-dimensional displays Walking Walking - physiology Wearable Electronic Devices Young Adult
ISSN:	0018-9294, 1558-2531, 1558-2531
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Abstract	Goal: For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk. Method: The classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course. Results: The results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059-0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment. Conclusion: The proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity.
AbstractList	For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk.GOALFor healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk.The classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course.METHODThe classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course.The results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059-0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment.RESULTSThe results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059-0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment.The proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity.CONCLUSIONThe proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity. For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk. The classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course. The results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059-0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment. The proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity. Goal: For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk. Method: The classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course. Results: The results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059-0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment. Conclusion: The proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity.
Author	Song, Minsu Kim, Jonghyun
Author_xml	– sequence: 1 givenname: Minsu surname: Song fullname: Song, Minsu email: sms160@dgist.ac.kr organization: Department of Robotics EngineeringDGIST (Daegu Gyeongbuk Institute of Science and Technology) – sequence: 2 givenname: Jonghyun orcidid: 0000-0002-0380-7446 surname: Kim fullname: Kim, Jonghyun email: jhkim@dgist.ac.kr organization: Department of Robotics Engineering, DGIST (Daegu Gyeongbuk Institute of Science and Technology), Daegu, Republic of Korea
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SubjectTerms	Acceleration Accelerometry - instrumentation Adult Algorithms Classification Classification algorithms Female Foot Foot - physiology Gait Gait - physiology gait activity classification Gait analysis Gait recognition Human Activities - classification Humans inertial measurement unit Inertial sensing devices Legged locomotion Male Monitoring Monitoring, Ambulatory - instrumentation Monitoring, Ambulatory - methods Parameter estimation ramp walk Signal Processing, Computer-Assisted - instrumentation stair walk System effectiveness Three-dimensional displays Walking Walking - physiology Wearable Electronic Devices Young Adult
Title	An Ambulatory Gait Monitoring System with Activity Classification and Gait Parameter Calculation Based on a Single Foot Inertial Sensor
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