Abstract
Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).
Highlights
In recent years, the development of sensing technologies and sensor signals processing techniques paved the way for the use of wearable sensors to monitor human status and performance.These developments resulted in the need for managing efficiently such networks, as explained by Fortino et al in [1]
Motivated through the variety of approaches to inertial measurements units (IMUs)-based human motion tracking (IHMT), the goal of this article is introducing the reader to IHMT
Aligned mIMU and optical motion capture (OMC) data are used to calculate the performance measures that we introduced in Section 2.2.4 and compare the algorithms
Summary
The development of sensing technologies and sensor signals processing techniques paved the way for the use of wearable sensors to monitor human status and performance. These developments resulted in the need for managing efficiently such networks, as explained by Fortino et al in [1]. Wearable body sensor networks (BSN) are nowadays used in several applications which include healthcare, ergonomics, sport and entertainment, (see [2] for a review on the argument). A field that has benefited from the research on BSN is motion tracking. Motion tracking has received the attention and the effort of generations of researchers. There are several techniques that allow for motion reconstruction based on different information sources
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