Signal Quality and Compactness of a Dual-Accelerometer System for Gyro-Free Human Motion Analysis

There is a growing interest in measuring human activities via worn inertial sensors, and situating two accelerometers on a body segment allows accessing rotational kinematic information, at a significantly lower energy cost when compared with gyroscopes. However, the placement of sensors has not been widely considered in the literature. In practice, dual-accelerometer systems should be built as compact as possible to ensure long-term wearability. In this paper, the impact of sensor placement and nature of human activity on signal quality is quantified by individual and differential signal-to-noise ratios (SNRs). To do so, noise-free signals are described by a 2-D kinematic model of a body segment as a function of kinematic variables and sensor location on the segment. Measurements are modelled as kinematic signals disturbed by zero mean additive Gaussian noise. Depending on the accuracy needed, one can choose a minimal SNR to achieve, with such dual-accelerometer arrangement. We estimate SNR and minimal sensor separations for three data sets, two from the public domain and one collected for this paper. The data sets give arm motion profiles for reaching, inertial data collected during locomotion on a treadmill and during activities of daily life. With a dual-accelerometer arrangement, we show that it is possible to achieve a good differential SNR for the analysis of various human activities if the separation between the two sensors and their placement is well chosen

Quasi-Real Time Estimation of Angular Kinematics Using Single-Axis Accelerometers

In human movement modeling, the problem of multi-link kinematics estimation by means of inertial measurement units has been investigated by several authors through efficient sensor fusion algorithms. In this perspective a single inertial measurement unit per link is required. This set-up is not cost-effective compared with a solution in which a single-axis accelerometer per link is used. In this paper, a novel fast technique is presented for the estimation of the sway angle in a multi-link chain by using a single-axis accelerometer per segment and by setting the boundary conditions through an ad hoc algorithm. The technique, based on the windowing of the accelerometer output, was firstly tested on a mechanical arm equipped with a single-axis accelerometer and a reference encoder. The technique is then tested on a subject performing a squat task for the knee flexion-extension angle evaluation by using two single-axis accelerometers placed on the thigh and shank segments, respectively. A stereo-photogrammetric system was used for validation. RMSEs (mean ± std) are 0.40 ± 0.02° (mean peak-to-peak range of 147.2 ± 4.9°) for the mechanical inverted pendulum and 1.01 ± 0.11° (mean peak-to-peak range of 59.29 ± 2.02°) for the knee flexion-extension angle. Results obtained in terms of RMSE were successfully compared with an Extended Kalman Filter applied to an inertial measurement unit. These results suggest the usability of the proposed algorithm in several fields, from automatic control to biomechanics, and open new opportunities to increase the accuracy of the existing tools for orientation evaluation

Quasi-real time estimation of angular kinematics using single-axis accelerometers

In human movement modeling, the problem of multi-link kinematics estimation by means of inertial measurement units has been investigated by several authors through efficient sensor fusion algorithms. In this perspective a single inertial measurement unit per link is required. This set-up is not cost-effective compared with a solution in which a single-axis accelerometer per link is used. In this paper, a novel fast technique is presented for the estimation of the sway angle in a multi-link chain by using a single-axis accelerometer per segment and by setting the boundary conditions through an ad hoc algorithm. The technique, based on the windowing of the accelerometer output, was firstly tested on a mechanical arm equipped with a single-axis accelerometer and a reference encoder. The technique is then tested on a subject performing a squat task for the knee flexion-extension angle evaluation by using two single-axis accelerometers placed on the thigh and shank segments, respectively. A stereo-photogrammetric system was used for validation. RMSEs (mean \ub1 std) are 0.40 \ub1 0.02\ub0 (mean peak-to-peak range of 147.2 \ub1 4.9\ub0) for the mechanical inverted pendulum and 1.01 \ub1 0.11\ub0 (mean peak-to-peak range of 59.29 \ub1 2.02\ub0) for the knee flexion-extension angle. Results obtained in terms of RMSE were successfully compared with an Extended Kalman Filter applied to an inertial measurement unit. These results suggest the usability of the proposed algorithm in several fields, from automatic control to biomechanics, and open new opportunities to increase the accuracy of the existing tools for orientation evaluation

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