Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

Zhao, He; Wang, Zheyao

doi:10.1109/jsen.2011.2166066

Cited by 160 publications

(85 citation statements)

References 35 publications

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“…However it should be remembered that each sensor has different accuracy described as the noise of measurement covariance matrix and his uniqueness should be included while gauging the real location of arm. The references to Kalman filtration can be seen in bibliography [5][6][7][8]. It includes all of foregoing factors and sets the estimate of the state vector having the smallest covariance error.…”

Section: Kinematic Structurementioning

confidence: 99%

The Measurement of Displacement with the Use of MEMS Sensors: Accelerometer, Gyroscope and Magnetometer

Kobierska¹,

Podsedkowski²,

Poryzała³

et al. 2017

JAMRIS

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Section: Kinematic Structurementioning

confidence: 99%

The Measurement of Displacement with the Use of MEMS Sensors: Accelerometer, Gyroscope and Magnetometer

Kobierska¹,

Podsedkowski²,

Poryzała³

et al. 2017

JAMRIS

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“…Recently, Zhao & Wang (2011) use EKF for the fusion of data from inertial sensors, ultrasonic sensors, and magnetic sensor. A 3D magnetic sensor and a 3D accelerometer are combined to measure the gravity and the earth's magnetic field for the static orientation.…”

Section: Application Examplesmentioning

confidence: 99%

The Application of Magnetic Sensors in Self-Contained Local Positioning

Huang¹,

Zhang²

2012

Magnetic Sensors - Principles and Applications

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“…A decision tree was used for classification of the body segments. When using a full-body configuration (17 different sensor locations), 97.5% of the sensors were correctly classified. Chapter 3 presents a method that identifies the location of a sensor, without making assumptions about the applied sensor configuration or the activity the user is performing.…”

Section: Discussionmentioning

confidence: 99%

“…Other researchers who used ultrasound for 7. Conclusions and discussion position estimation are Huitema et al [30] and Zhao and Wang [97]. Disadvantage of the systems they presented is the need for stationary transmitters or receivers on the floor.…”

Section: Relative Foot Position and Orientation Estimation And Balancmentioning

confidence: 95%

“…Zhao and Wang [97] also compensated the drift of inertial sensors with the assistance of magnetometers and an ultrasound emitter mounted on a moving vehicle and three receivers fixed on the ground. Although it was shown that drift in estimated positions was eliminated, the system is not completely ambulatory since the ultrasound receivers are fixed to the ground, similar to the study of Huitema et al as described above.…”

Section: Introductionmentioning

confidence: 99%

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Click-on-and-Play human motion capture using wearable sensors

Weenk¹

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SummaryHuman motion capture is often used in rehabilitation clinics for diagnostics and monitoring the effects of treatment. Traditionally, camera based systems are used. However, with these systems the measurements are restricted to a lab with expensive cameras. Motion capture outside a lab, using inertial sensors, is becoming increasingly popular to obtain insight in daily-life activity patterns.There are two main disadvantages of inertial sensor systems. Preparing the measurement system is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. Another disadvantage is that inertial sensors cannot measure relative segment positions directly. Especially relative foot positions are very important to be estimated. Together with the center of mass, these positions can be used to assess the balance of a subject. From these two main disadvantages, the goal of this thesis was derived: Contribute to the development of a click-onand-play human motion capture system. This should be a system in which the user attaches (clicks) the sensors to the body segments and can start measuring (play) immediately. Therefore, the following sub-goals were defined. The first goal is to develop an algorithm for the automatic identification of the body segments to which inertial sensors are attached. The second goal is to develop a new sensor system, with a minimal number of sensors, for the estimation of relative foot positions and orientations and the assessment of balance during gait.The first goal is addressed in chapters 2 and 3. Chapter 2 presents a method for the automatic identification of body segments on which inertial sensors are positioned. This identification is performed on the basis of a walking trial, assuming the use of a known sensor configuration. Using this method it is possible to distinguish left and right segments. Cross correlations of signals from different measurement units were used and the features were ranked. A decision tree was used for classification of the body segments. When using a full-body configuration (17 different sensor locations), 97.5% of the sensors were correctly classified. Chapter 3 presents a method that identifies the location of a sensor, without making assumptions about the applied sensor configuration or the activity the user is performing. For a full-body configuration 83.3% of the sensor locations were correctly classified. Subsequently, for each sensor location a model was developed for activity classification, resulting in a maximum vii accuracy of 91.7%.The second goal is addressed in the chapters 4, 5 and 6. In chapter 4, ultrasound time of flight is used to estimate the distance between the feet. This system was validated using an optical reference and showed an average error in distance estimation of 7.0 mm. In chapter 5, 3D relative foot positions are estimated by fusing ultrasound and inertial sensor data measured on the shoes in an extended Kalman filter.Step lengths and step widths were ca...

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Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

Cited by 160 publications

References 35 publications

The Measurement of Displacement with the Use of MEMS Sensors: Accelerometer, Gyroscope and Magnetometer

The Measurement of Displacement with the Use of MEMS Sensors: Accelerometer, Gyroscope and Magnetometer

The Application of Magnetic Sensors in Self-Contained Local Positioning

Click-on-and-Play human motion capture using wearable sensors

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