Navigation Using Inertial Sensors

This tutorial provides an introduction to navigation using inertial sensors, explaining the underlying principles. Topics covered include accelerometer and gyro technology and their characteristics, strapdown inertial navigation, attitude determination, integration and alignment, zero updates, motion constraints, pedestrian dead reckoning using step detection, and fault detection

A functional electrical stimulation (fes) control system for upper limb rehabilitation

Functional electrical stimulation (FES) is the controlled use of electrical pulses to produce contraction of muscles in such a way as to support functional movement. FES is now widely used to aid walking in stroke patients and research into using FES to support other tasks is growing. However, in the more complex applications, it is very challenging to achieve satisfactory levels of FES control.The overall aim of the author’s PhD thesis is to develop improved techniques for real-time Finite State Machine (FSM) control of upper limb FES, using multiple accelerometers for tracking upper limb movement and triggering state transitions. Specific achievements include: 1) Development of new methods for using accelerometers to capture body segment angle during performance of an upper limb task and use of that data to trigger state transitions (angle triggering); 2) Development of new methods to improve the robustness of angle triggering; 3) Development of a flexible finite state-machine controller for control of upper limb FES in real time; 4) In collaboration with a clinical PhD student, implementation of a graphical user interface (GUI) that allows clinical users (e.g. physiotherapists) to set up FSM controllers for FES-assisted upper limb functional tasks.Three alternative methods that use 3-axis accelerometer data to track body segment angle with respect to gravity have been reported. The first uncalibrated method calculates the change in angle during a rotation using the gravity vectors before and after the rotation. The second uncalibrated method calculates the angle between the accelerometer x-axis and the gravity vector. The third calibrated method uses a calibration rotation to define the measurement plane and the positive rotation direction. This method then calculates the component of rotation that is in the same plane as the calibration rotation. All three methods use an algorithm that switches between using sine and cosine, depending on the measured angle, which overcomes the poor sensitivity problem seen in previous methods.xviiiA number of methods can be included in the transition triggering algorithm to improve robustness and hence the usability of the system. The aim of such methods is to reduce the number of incorrect transition timings caused by signal noise, jerky arm movements and other negative effects, which lead to poor control of FES during reaching tasks. Those methods are: 1) Using the change in angle since entering a state rather than absolute angle; 2) Ignoring readings where the acceleration vector is significant in comparison to the gravity vector (i.e. the magnitude of the measured vector is significantly different from 9.81); and 3) Requiring a given number of consecutive or non-consecutive valid readings before triggering a transition. These have been implemented with the second uncalibrated angle tracking method and incorporated into a flexible FSM controller.The flexible FSM controller and the associated setup software are also presented in this thesis, for control of electrical stimulation to support upper limb functional task practice. In order to achieve varied functional task practice across a range of patients, the user should be able to set up a variety of different state machines, corresponding to different functional tasks, tailored to the individual patient. The goal of the work is to design a FSM controller and produce an interface that clinicians (even potentially patients) can use to design and set up their own task and patient-specific FSMs.The software has been implemented in the Matlab-Simulink environment, using the Hasomed RehaStim stimulator and Xsens MTx inertial sensors. The full system has been tested with stroke patients practicing a range of tasks in the laboratory environment, demonstrating the potential for further exploitation of the work