Human movement analysis with inertial sensors

Abstract

Present-day systems for human movement analysis are not portable, have a limited capture volume and require a trained technician to analyze the data. To extend the use and benefits to non-laboratory settings the acquisition should be robust, reliable and easy to perform. Ideally, data collection and analysis would be automated to the point where no trained technicians are required. Over the last decade several inertial sensor approaches have been put forward that address most of the aforementioned limitations. Advancements in micro-electro-mechanical sensors (MEMS) and orientation estimation algorithms are boosting the use of inertial sensors in motion capture applications. These sensors currently are the most promising opportunity for non-restricted human motion analysis. In this chapter we will describe the types of sensors used, followed by an overview of their use in the biomechanics community (Sect. 16.1); provide the necessary background of basic mathematics for those that want to refresh the basics of kinematics (case studies and appendix). The limitations of traditional systems can be dealt with due to the redundant information available to obtain orientation estimates. There are several different methods to derive orientation from sensor information; we will highlight the main groups of algorithms and the various ways in which they use the available data (Sect. 16.3). The chapter furthermore contains two hands-on examples to derive orientation (case study 1) and extract joint angles (case study 2, Sect. 16.4).).