A study on estimating the knee joint angle during walking using the motion sensors (Focusing on the effect of centrifugal acceleration and tangential acceleration)

Saito, Ayuko; Miyawaki, Kazuto; Kizawa, Satoru; Kobayashi, Yoshikazu

doi:10.1299/transjsme.17-00488

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…( 14) but for the shank. The translational acceleration of the shank is expressed as the sum of the centrifugal and tangential accelerations of the thigh [23].…”

Section: Nonlinear State Equationmentioning

confidence: 99%

“…Because the magnetic field cannot be measured correctly using a magnetometer in a variable magnetic field, several sensor fusion methods that can correct the magnetometer output under a variable magnetic field have been proposed [19,20]. Moreover, sensor fusion approaches that consider accelerations other than the acceleration due to gravity have been proposed [21][22][23]. These sensor fusion methods contribute to the increased accuracy of the pose estimation.…”

Section: Introductionmentioning

confidence: 99%

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Knee joint angle estimation by sequential correction of gyroscope bias

Saito

Tanzawa

Kizawa

2021

Preprint

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Compact and lightweight nine-axis motion sensors have come to be used for motion analysis in a variety of fields such as medical care, welfare, and sports. Nine-axis motion sensors include a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and can estimate joint angles using the gyroscope outputs. However, the bias of the gyroscope is often unstable depending on the measurement environment and the accuracy of the gyroscope itself, causing error to accumulate in the angle obtained by integrating the gyroscope output. Although several sensor fusions have been proposed for pose estimation, such as using an accelerometer and a magnetometer, sequentially estimating and correcting the bias of the gyroscope are desirable for more accurate pose estimation. In addition, considering accelerations other than the acceleration due to gravity is important for a sensor fusion method that utilizes the accelerometer to correct the gyroscope output. Therefore, in this study, an extended Kalman filter algorithm was developed to sequentially correct both the gyroscope bias and the centrifugal and tangential acceleration of an accelerometer. The gait measurement results indicate that the proposed method successfully suppresses drift in the estimated knee joint angle over the entire measurement time of knee angle measurement during gait. The knee joint angles estimated using the proposed method were generally consistent with results obtained from an optical 3D motion analysis system. The proposed method is expected to be useful for estimating motion in medical care and welfare applications.

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“…( 14) but for the shank. The translational acceleration of the shank is expressed as the sum of the centrifugal and tangential accelerations of the thigh [23].…”

Section: Nonlinear State Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knee joint angle estimation by sequential correction of gyroscope bias

Saito

Tanzawa

Kizawa

2021

Preprint

Self Cite

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“…An extended Kalman lter-based algorithm [18] was used to estimate the attitude of the IMUs attached to both legs, and based on the shank IMU attitude relative to the thigh IMU attitude, the knee exion angle was calculated for each leg. The heel strike (HS) timing was determined using the anteroposterior acceleration data collected via the trunk IMU, which was smoothed with a fth-order low-pass Butterworth lter (cutoff frequen-cy = 2 Hz) [19].…”

Section: Data Processingmentioning

confidence: 99%

Quantitative Evaluation Related to Disease Progression in Knee Osteoarthritis Patients During Gait

Tsurumiya

Hayasaka

Komatsu

et al. 2021

ABE

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Knee osteoarthritis (KOA) is a common joint disease of the lower limbs. Its progression reduces the patients quality of life. Varus thrust (VT), one of the abnormal gait patterns of KOA patients, is considered as an effective index for assessing KOA. Hence, several studies have assessed VT using various measurement methods. Since VT is the momentary lateral knee motion that increases knee varus angle and moment, optical motion capture system is widely used. However, optical motion capture system has some disadvantages in clinical usage, such as high cost, requirement of technical skills, and time-consuming attachment process. Recently, inertial measurement units (IMU) have emerged as a measurement system instead of optical motion capture systems. IMU-based method is regarded as more suitable than optical motion capture systems for VT assessment because of its simplicity. This study aimed to assess the gait of KOA patients using IMUs and to quantitatively evaluate VT based on disease progression. For this purpose, we recruited 7 healthy participants and 15 KOA patients. Subsequently, their knees were classi ed into 3 progression groups: 14 healthy (grade 0) knees, 9 grade 3 knees, and 14 grade 4 knees. As a gait test, all the participants wore IMUs positioned at the trunk, both thighs, and both shanks, and traversed a 10-m walkway. VT was considered as the rst peak value of the mediolateral acceleration and the varus-valgus angular velocity data, which were collected via the IMUs at both shanks. Thereafter, these acquired data were compared and evaluated among the three progression groups. The results indicate that both peak values were signi cantly greater in the KOA patients than in the healthy subjects. Moreover, there was a signi cant positive correlation between the two peak values. Thus, this study is expected to contribute toward early detection of KOA.

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“…The nine-axis motion sensors include a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Using information obtained from the motion sensors, several sensor fusion algorithms have been proposed for pose estimation: as one example, a sensor fusion algorithm that can correct gyroscope drift using information obtained from the other two sensors has been used for human pose estimation during daily activities and exercise [11][12][13]. Furthermore, a sensor fusion algorithm able to correct the magnetometer output using information obtained from a gyroscope has been used for pose estimation in a variable magnetic field [14,15].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 were running in place for about 100 s. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

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A study on estimating the knee joint angle during walking using the motion sensors (Focusing on the effect of centrifugal acceleration and tangential acceleration)

Cited by 6 publications

References 9 publications

Knee joint angle estimation by sequential correction of gyroscope bias

Knee joint angle estimation by sequential correction of gyroscope bias

Quantitative Evaluation Related to Disease Progression in Knee Osteoarthritis Patients During Gait

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

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