Abstract
The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial measurement units (IMUs). Such a unit is composed of accelerometer, gyroscope and magnetometer. The final quality of an obtained motion arises from all the individual parts of the described system. The proposed system is a sequence of the following stages: sensor data acquisition, sensor orientation estimation, system calibration, pose estimation and data visualisation. The construction of the system’s architecture with the dataflow programming paradigm makes it easy to add, remove and replace the data processing steps. The modular architecture of the system allows an effortless introduction of a new sensor orientation estimation algorithms. The original contribution of the paper is the design study of the individual components used in the motion capture system. The two key steps of the system design are explored in this paper: the evaluation of sensors and algorithms for the orientation estimation. The three chosen algorithms have been implemented and investigated as part of the experiment. Due to the fact that the selection of the sensor has a significant impact on the final result, the sensor evaluation process is also explained and tested. The experimental results confirmed that the choice of sensor and orientation estimation algorithm affect the quality of the final results.
Highlights
The full-body motion capture technology has applications in various domains, including virtual reality [1], athletic training [2], biomedical engineering [3] and rehabilitation [4,5]
The effect of internal processing is clearly visible in the integration results (Figure 4d), which indicates that similar results can be achieved for other sensors if the processing of the signal is designed with the noise characteristic in mind
The other conclusion is that it seems the modern inertial measurement units (IMUs) with its complex internal signal processing, designed to suppress different kinds of noise, can be hardly characterised by a simple coefficient reading from the plot as it is described in the previous paragraph
Summary
The full-body motion capture technology has applications in various domains, including virtual reality [1], athletic training [2], biomedical engineering [3] and rehabilitation [4,5]. The demand for rehabilitation services and the resulting demand for systems capable of body movement monitoring continue to grow due to the increasing population of ageing people. The user-worn inertial measurement units (IMUs) has been proven to be suitable for unrestrained tracking of body segments’ orientations because they are small, light, affordable and completely self-contained [3,6,7,8,9]. Used IMUs are composed of accelerometers, gyroscopes, and magnetometers, and are characterised by high measurement noise, incorrect scaling and biasing. The bulk of the Sensors 2017, 17, 612; doi:10.3390/s17030612 www.mdpi.com/journal/sensors
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