Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented-Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)-and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16 /spl plusmn/ 1.8 mm in horizontal and 39 /spl plusmn/ 3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage

Tracking technical refinement in elite performers: The good, the better, and the ugly

This study extends coaching research examining the practical implementation of technical refinement in elite-level golfers. In doing so, we provide an initial check of precepts pertaining to the Five-A Model and, examine the dynamics between coaching, psychomotor, biomechanical and psychological inputs to the process. Three case studies of golfers attempting refinements to their already well-established techniques are reported. Kinematic data were supplemented with intra-individual movement variability and self-perceptions of mental effort as measures of tracking behaviour and motor control. Results showed different levels of success in refining technique and subsequent ability to return to executing under largely subconscious control. In one case, the technique was refined as intended but without consistent reduction of conscious attention, in another, both were successfully apparent, whereas in the third case neither was achieved. Implications of these studies are discussed with reference to the process’ interdisciplinary nature and importance of the initial and final stages

Nonparametric Hammerstein model based model predictive control for heart rate regulation.

This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints

Classification of sporting activities using smartphone accelerometers

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach

Calculating critical power and the finite work capacity from a single all-out cycling test

Critical power (CP) is an important training threshold and represents the highest power output that elicits steady-state physiological responses. Research suggests that CP and the finite work capacity (Wʹ), can be estimated from a single 3-min bout of all-out cycling. Five experimental studies were undertaken to explore the reliability and validity of CP tests, and to propose a novel all-out testing protocol. Study one investigated the reliability and validity of the 3-min cycling test when performed against a fixed resistance and in isokinetic mode. Results suggested that the 3-min cycling test provided a reliable and valid estimate of CP in isokinetic mode, but significantly overestimated CP when performed against a fixed resistance. Study two investigated the effect of cadence on CP and Wʹ during the 3-min cycling test when performed against a fixed resistance, with results suggesting that a better estimation of CP is observed at higher cadences (e.g. preferred cadence +10 rev·min-1). Studies three and four focused on measuring power output using cycle-mounted power meters to support the novel all-out testing protocol used in study five. The PowerTap P1 pedals demonstrated greater reliability and validity than the Garmin Vector 2 pedals across all power outputs, with reliability maintained after prolonged use. Consequently, the PowerTap P1 pedals were used in study five, which investigated the reliability and validity of a novel all-out cycling test to estimate CP and Wʹ. Results suggested that CP could be estimated from the novel all-out cycling test; however, caution should be taken when estimating Wʹ. The results also suggested that cycling at CP calculated from the original protocol, 3-min cycling test protocol, and novel all-out test protocol resulted in exhaustion occurring within 20 min, and a metabolic steady-state was not observed. The overall findings of this thesis question the underpinning physiology of CP, and whether CP represents the boundary between the heavy and severe exercise intensity domains

Identification and control for heart rate regulation during treadmill exercise

This paper proposes a novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system. For the identification of Hammerstein systems, the pseudorandom binary sequence input is employed to decouple the identification of dynamic linear part from input nonlinearity. The powerful ε-insensitivity support vector regression method is adopted to obtain sparse representations of the inverse of static nonlinearity in order to obtain an approximate linear model of the Hammerstein system. An H ∞ controller is designed for the approximated linear model to achieve robust tracking performance. This new approach is successfully applied to the design of a computer-controlled treadmill system for the regulation of heart rate during treadmill exercise. Minimizing deviations of heart rate from a preset profile is achieved by controlling the speed of the treadmill. Both conventional proportional-integral-derivative (PID) control and the proposed approaches have been employed for the controller design. The proposed algorithm achieves much better heart rate tracking performance. © 2007 IEEE

VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera

We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleton. This makes our approach the first monocular RGB method usable in real-time applications such as 3D character control---thus far, the only monocular methods for such applications employed specialized RGB-D cameras. Our method's accuracy is quantitatively on par with the best offline 3D monocular RGB pose estimation methods. Our results are qualitatively comparable to, and sometimes better than, results from monocular RGB-D approaches, such as the Kinect. However, we show that our approach is more broadly applicable than RGB-D solutions, i.e. it works for outdoor scenes, community videos, and low quality commodity RGB cameras.Comment: Accepted to SIGGRAPH 201