Ambulatory assessment of human body kinematics en kinetics

Schepers, H. Martin

doi:10.3990/1.9789036528443

Cited by 15 publications

(23 citation statements)

References 87 publications

(168 reference statements)

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“…The magnetic field of a coil is generally described as a function of the perpendicular distance pz towards the coil and the radial distance r = p 2 x + p 2 y towards the center of the coil [12,13]. However, in tracking we are interested in absolute position rather than just the distance to a source.…”

Section: Some Additional Words About the Magnetic Field Modelmentioning

confidence: 99%

MEMS-based inertial navigation based on a magnetic field map

Kok

Wahlström

Schön

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper presents an approach for 6D pose estimation where MEMS inertial measurements are complemented with magnetometer measurements assuming that a model (map) of the magnetic field is known. The resulting estimation problem is solved using a RaoBlackwellized particle filter. In our experimental study the magnetic field is generated by a magnetic coil giving rise to a magnetic field that we can model using analytical expressions. The experimental results show that accurate position estimates can be obtained in the vicinity of the coil, where the magnetic field is strong.

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Section: Some Additional Words About the Magnetic Field Modelmentioning

confidence: 99%

MEMS-based inertial navigation based on a magnetic field map

Kok

Wahlström

Schön

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Moreover, this approach requires many sensor modules on different body segments. Another method for estimating relative positions of body segments, using wearable sensor systems, is with the use of on-body position measurement systems [58,62,66,79]. This is described for example by Roetenberg et al [59], where the relative positions and orientations of inertial/magnetic sensors on the human body were investigated using a 3D magnetic source, positioned on the back of the body, and 3D magnetic sensors placed at different body segments.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Disadvantages of these lab bound systems are line of sight problems and the fact that only a limited number of steps can be measured inside a lab. Furthermore, movement is restricted because the steps need to be on the force plate [66]. …”

Section: Traditional Motion Capture Systemsmentioning

confidence: 99%

“…However, the most popular are inertial sensors [46,58,66]. The principle of inertial sensing is based on measuring forces acting on moving masses [74].…”

Section: Movement Sensorsmentioning

confidence: 99%

“…Moments, center of pressure and center of mass can be estimated from the ground reaction forces measured using these sensors [66,69].…”

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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Preliminary Analysis of Low-Cost Motion Capture Techniques to Support Virtual Ergonomics

Colombo

Regazzoni

Rizzi

et al. 2013

Lecture Notes in Mechanical Engineering

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This paper concerns the development of a computer-aided platform to analyse workers' postures and movements and ergonomically validate the design of device a man or woman may deal with. In particular, we refer to pick and place operations of food items on the display unit shelves. The paper describes three low-cost solutions that integrate two optical motion capture techniques (one based on web-cam and another on MS Kinect sensor) and two human modelling systems (Jack and LifeMod) with the main goal of determining the suitability of operators' working conditions and, eventually, providing a feedback to the design step. The solutions have been tested considering a vertical display unit as case study. Preliminary results of the experimentation as well as main benefits and limits are presented. The results have been considered promising; however, we have planned to perform an acquisition campaign in the real environment, the supermarket.

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Ambulatory assessment of human body kinematics en kinetics

Cited by 15 publications

References 87 publications

MEMS-based inertial navigation based on a magnetic field map

MEMS-based inertial navigation based on a magnetic field map

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

Preliminary Analysis of Low-Cost Motion Capture Techniques to Support Virtual Ergonomics

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