Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network

被引:10
作者
Moon, Kee S. [1 ]
Lee, Sung Q. [2 ]
Ozturk, Yusuf [3 ]
Gaidhani, Apoorva [1 ]
Cox, Jeremiah A. [1 ]
机构
[1] San Diego State Univ, Dept Mech Engn, 5500 Campanile Dr, San Diego, CA 92182 USA
[2] Elect & Telecommun Res Inst, ICT, 218 Gajeong Ro, Daejeon 34129, South Korea
[3] San Diego State Univ, Dept Elect & Comp Engn, 5500 Campanile Dr, San Diego, CA 92182 USA
关键词
gait analysis; wearable sensors; inertial measurement unit; human kinematics; phase difference angle; HUMAN MOVEMENT;
D O I
10.3390/s19225024
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.
引用
收藏
页数:13
相关论文
共 25 条
[1]   An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors [J].
Anwary, Arif Reza ;
Yu, Hongnian ;
Vassallo, Michael .
SENSORS, 2018, 18 (02)
[2]   Summary measures for clinical gait analysis: A literature review [J].
Cimolin, Veronica ;
Galli, Manuela .
GAIT & POSTURE, 2014, 39 (04) :1005-1010
[3]   Detection of Freezing of Gait in Parkinson Disease: Preliminary Results [J].
Coste, Christine Azevedo ;
Sijobert, Benoit ;
Pissard-Gibollet, Roger ;
Pasquier, Maud ;
Espiau, Bernard ;
Geny, Christian .
SENSORS, 2014, 14 (04) :6819-6827
[4]   Gait Differs Between Unilateral and Bilateral Knee Osteoarthritis [J].
Creaby, Mark W. ;
Bennell, Kim L. ;
Hunt, Michael A. .
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION, 2012, 93 (05) :822-827
[5]  
Das D., 2015, P INT C COMP COMM AU, DOI [10.1109/ccaa.2015.7148386, DOI 10.1109/CCAA.2015.7148386]
[6]   Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion [J].
Filippeschi, Alessandro ;
Schmitz, Norbert ;
Miezal, Markus ;
Bleser, Gabriele ;
Ruffaldi, Emanuele ;
Stricker, Didier .
SENSORS, 2017, 17 (06)
[7]   Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion [J].
Gaidhani, Apoorva ;
Moon, Kee S. ;
Ozturk, Yusuf ;
Lee, Sung Q. ;
Youm, Woosub .
SENSORS, 2017, 17 (12)
[8]   Direct measurement of human movement by accelerometry [J].
Godfrey, A. ;
Conway, R. ;
Meagher, D. ;
OLaighin, G. .
MEDICAL ENGINEERING & PHYSICS, 2008, 30 (10) :1364-1386
[9]   Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring [J].
Karantonis, DM ;
Narayanan, MR ;
Mathie, M ;
Lovell, NH ;
Celler, BG .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2006, 10 (01) :156-167
[10]  
Kim Y., 2004, P AS PAC C BIOM EM S, V1, P113, DOI [10.1299/jsmeapbio.2004.1.113, DOI 10.1299/JSMEAPBIO.2004.1.113]