This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.

No abstract

A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under non-isometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth -active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shankquadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is Preprint submitted to Control Engineering Practice 16 July 2004explicitly designed whereas the model was identified a priori using the developed identification procedure.

Abstract-Aim:The aim of this study was to investigate feedback control strategies for integration of electric motor assist and functional electrical stimulation (FES) for paraplegic cycling, with particular focus on development of a testbed for exercise testing in FES cycling, in which both cycling cadence and workrate are simultaneously well controlled and contemporary physiological measures of exercise performance derived. A second aim was to investigate the possible benefits of the approach for mobile, recreational cycling.Methods: A recumbent tricycle with an auxiliary electric motor is used, which is adapted for paraplegic users, and instrumented for stimulation control. We propose a novel integrated control strategy which simultaneously provides feedback control of leg power output (via automatic adjustment of stimulation intensity) and cycling cadence (via electric motor control). Both loops are designed using system identification and analytical (model-based) feedback design methods. Ventilatory and pulmonary gas exchange response profiles are derived using a portable system for real-time breath-by-breath acquisition.Results: We provide indicative results from one paraplegic subject in which a series of feedback-control tests illustrate accurate control of cycling cadence, leg power control, and external disturbance rejection. We also provide physiological response profiles from a submaximal exercise step test and a maximal incremental exercise test, as facilitated by the control strategy. Conclusion: The integrated control strategy is effective in facilitating exercise testing under conditions of well-controlled cadence and power output. Our control approach significantly extends the achievable workrate range and enhances exercise-test sensitivity for FES cycling, thus allowing a more stringent characterization of physiological response profiles and estimation of key parameters of aerobic function. We further conclude that the control approach can significantly improve the overall performance of mobile recreational cycling.Index Terms-Cardiopulmonary exercise testing, control systems, functional electrical stimulation (FES), lower limb cycling, spinal-cord injury (SCI) rehabilitation.