Human gait modelling with step estimation and phase classification utilising a single thigh mounted IMU for vision impaired indoor navigation

This research is focused on human gait modelling for infrastructure free inertial navigation for vision impaired. A pedometer based on a single thigh mounted gyroscope, an efficient algorithm to estimate thigh flexion and extension, gait models for level walking, a model to estimate step length and a technique to detect gait phases based on a single thigh mounted Inertial Measurement Unit (IMU) were developed and confirmed higher accuracies

Alignment parameter calibration for IMU using the Taguchi method for image deblurring

Inertial measurement units (IMUs) utilized in smartphones can be used to detect camera motion during exposure, in order to improve image quality degraded with blur through long hand-held exposure. Based on the captured camera motion, blur in images can be removed when an appropriate deblurring filter is used. However, two research issues have not been addressed: (a) the calibration of alignment parameters for the IMU has not been addressed. When inappropriate alignment parameters are used for the IMU, the camera motion would not be captured accurately and the deblurring effectiveness can be downgraded. (b) Also selection of an appropriate deblurring filter correlated with the image quality has still not been addressed. Without the use of an appropriate deblurring filter, the image quality could not be optimal. This paper proposes a systematic method, namely the Taguchi method, which is a robust and systematic approach for designing reliable and high-precision devices, in order to perform the alignment parameter calibration for the IMU and filter selection. The Taguchi method conducts a small number of systematic experiments based on orthogonal arrays. It studies the impact of the alignment parameters and appropriate deblurring filter, which attempts to perform an effective deblurring. Several widely adopted image quality metrics are used to evaluate the deblurred images generated by the proposed Taguchi method. Experimental results show that the quality of deblurred images achieved by the proposed Taguchi method is better than those obtained by deblurring methods which are not involved with the alignment parameter calibration and filter selection. Also, much less computational effort is required by the Taguchi method when comparing with the commonly used optimization methods for determining alignment parameters and deblurring filter

Human Activity Recognition Using Thigh Angle Derived from Single Thigh Mounted IMU Data

Accurate human activity recognition is a challenging topic of research in many areas. A common approach to activity recognition is to use accelerometers and/or gyroscopes to detect trunk or leg movement. This paper present a novel approach to detect human activities based on thigh angle computed using data from a single thigh mounted Inertial Measurement Unit (IMU). As this work forms a component of a system underdevelopment to assist the vision impaired in indoor navigation, activities common in indoor pedestrian tracking such as sitting, standing and walking were considered in the development of the algorithm. This algorithm uses simple signal processing techniques including peak detection, zero crossing detection and timers to identify the activity based on the thigh angle computed by fusing accelerometer and gyroscope. This allows implantation of the algorithm in a general purpose low end microcontroller. To reduce the number of input parameters to the algorithm, it was assumed that accelerometer y–axis is aligned with the thigh such that gyroscopic x–data represents angular velocity of the forward and backward movement of the thigh. The algorithm has shown above 78% accuracy in detecting standing, above 92%accuracy for walking and no measured errors for sitting, in a test conducted with a limited number of samples with ideal testing conditions. These results indicate that this less computationally intense algorithm gives promising results in activity detection in indoor pedestrian navigation applications

The Application of "off-the-shelf" Components for Building IMUs for Navigation Research

Inertial measurement units (IMU) are commonly used in pedestrian and robotic navigation applications and research. Although many IMUs are commercially available, almost all of them are non-customizable and they process the collected raw data before presenting them to the user. However, this creates a limitation for researchers due to the fact that they have to rely on a set of per-processed data. Further, available resources and features such as SD card slots, wireless connectivity, available in the IMU may not suit one’s research. This paper provides a survey on availability and usage of different off-the-shelf devices to build a custom made IMU. The authors considered open source microcontroller platforms, low cost MEMS sensors and low cost accessories in this survey so that the IMUs will be affordable to many people. A range of sensors, their features, available processor options and different types of wired and wireless communication options available are discussed. Particular emphasis is made on the ability to modify or add functionality to commonly available hardware. Possible technical issues in assembling the IMU and calibrating sensors are also discussed in this paper. Technologies available for constructing a housing and mounting systems for the IMU best suited to the application are also discussed in this paper. As an example, IMUs developed and implemented by the authors with different housing designs specifically created for particular applications are presented. This survey indicated that off-the-shelf components can effectively be used to build custom-made IMUs to suit the particular research interest or application best