Objective clinical gait analysis using inertial sensors and six minute walking test

Ameli, Sina; Naghdy, Fazel; Stirling, David; Naghdy, Golshah; Aghmesheh, Morteza

doi:10.1016/j.patcog.2016.08.002

Cited by 18 publications

(13 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compact and lightweight nine-axis motion sensors have been developed through advances in microelectromechanical systems technology; they have come to be used for motion analysis in widely various fields [1][2][3][4][5][6][7][8]. The nine-axis motion sensors are applicable both indoors and outdoors because of their portability.…”

Section: Introductionmentioning

confidence: 99%

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Saito

Kizawa

Kobayashi

et al. 2020

Preprint

View full text Add to dashboard Cite

This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants ran on a treadmill for about 90 seconds. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

show abstract

Section: Introductionmentioning

confidence: 99%

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Saito

Kizawa

Kobayashi

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Related range sensor and home-video based systems, which cost about £700, such as [9], [10] and [11], that build on the work of [12], with Pro-Trainer motion analysis software (Sports Motion, Inc., Cardiff, CA), offer gait analysis outside the gait laboratory, e.g., in local clinics and at homes. Similar to other range sensor and home-video based gait analysis systems [2,[13][14][15][16][17][18][19][20][21] and Inertial Measurement Unit (IMU) based gait analysis systems [22][23][24][25][26], the gait parameters obtained after data processing can be sent to physiatrists for clinical consultation, indicating the potential for tele-rehabilitation [27][28][29][30][31]. It is shown in [32] that a 2D video tracker software provides similar accuracy to VICON 3D system for knee angle measurement but not for measurement of the ankle angle over time.…”

Section: Introductionmentioning

confidence: 99%

“…Our system addresses some of the drawbacks of related range sensor and home-video based systems [2,9,11,[13][14][15][16][17][18][19]41] and IMU systems [22][23][24][25][26] namely: (1) Unlike [9], there are no colour restrictions on the background or the participant's clothing; (2) In contrast to Soda et al [9], which is validated on only one healthy volunteer with one walking trial with no gold standard benchmark, we validate our proposed system's knee angle against the gold standard VICON MX Giganet 6xT40 and 6xT160 (VICON Motion Systems Ltd., Oxford, UK, approximately £250,000) optical motion analysis system (the same gold standard as used by [11]). (3) Unlike systems of [11] and Pro-Trainer and Siliconcoach (Siliconcoach Ltd., Dunedin, New Zealand) as used by [42] and [43] that require significant manual effort, our system autonomously tracks the markers attached to the joints and calculates the knee angle; the only operational effort required is for marker-template selection for tracking initialization which is done via a user-friendly graphical user interface (GUI).…”

Section: Introductionmentioning

confidence: 99%

“…(5) 3D Kinect range sensor-based systems [13-18, 20, 21] cannot reliably capture relatively fast body motion, since Kinect operates at only 30 frames per second (fps), whereas our system operates at 210 fps. (6) Like other range sensor and home-video based systems, our system is non-intrusive to the participants, which is in contrast to state-of-the-art IMU gait analysis systems [22][23][24][25][26]. However, with only a 2D camera in our gait analysis system, its drawback lies in the following two aspects: (1) Estimation of the human joint locations using our system is less accurate compared to 3D Kinect-based range sensor systems, and (2) The gait parameters derived from the 2D images in our system are less reliable than those derived from the inertial data in IMU systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

Yang

Ugbolue

McNicol

et al. 2019

IET Science, Measurement & Technology

View full text Add to dashboard Cite

While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, we focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibration, autonomous detection of frames-ofinterest, Kalman-filter+Structural-Similarity-based marker tracking, and autonomous knee angle calculation. The proposed system is tested using 15 participants, including 10 stroke patients and 5 healthy volunteers. The evaluation of autonomous frames-ofinterest detection shows only 0.2% difference between the frame number of the detected frame compared to the frame number of the manually labelled ground truth frame, and thus can replace manual labelling. The system is validated against a gold standard optical motion analysis system, using knee angle accuracy as metric of assessment. The accuracy investigation between the RGBand the grayscale-video marker tracking schemes shows that the grayscale system suffers from negligible accuracy loss with a significant processing speed advantage. Experimental results demonstrate that the proposed system can automatically estimate the knee angle, with R-squared value larger than 0.95 and Bland-Altman plot results smaller than 3.0127 degrees mean error. r row centre coordinate of the marker t duration of one frame a candidate block b marker-template ∈ set membership,

show abstract

“…Wearable sensors concerned with quantification of movement have been the focus of research efforts to further enhance clinical assessment of motor dysfunction. The aim of these efforts is to shift clinical assessment of motor dysfunction from the current subjective methods applied in some rating scales to quantifiable and accurate measures and to provide long-term quantified measures that monitor the patient's condition and overall motor progression [1] , [2] , [3] , [4] . In the past couple of decades, there have been significant advances in the miniaturization, proliferation, accessibility and sophistication of sensor technology.…”

mentioning

confidence: 99%

Wearable sensors for the monitoring of movement disorders

Jalloul

2018

Biomedical Journal

View full text Add to dashboard Cite

This paper offers a review of the implementation of current wearable sensing technologies in monitoring the movement and activity of patients suffering from movement disorders. Recent literature has focused on incorporating simple and reliable wearable technologies for the continuous and objective monitoring of patient movement during normal daily activities. However, the use of such wearable sensing technologies has yet to find its way to clinical practice. In the following, the basic elements of such monitoring systems and their applications are introduced, and a discussion regarding current clinical applications is presented.

show abstract

Objective clinical gait analysis using inertial sensors and six minute walking test

Cited by 18 publications

References 55 publications

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Automation enhancement and accuracy investigation of a portable single‐camera gait analysis system

Wearable sensors for the monitoring of movement disorders

Contact Info

Product

Resources

About