This paper proposes a robust sensor fusion algorithm to accurately track the spatial location and motion of a human under various dynamic activities, such as walking, running, and jumping. The position accuracy of the indoor wireless positioning systems frequently suffers from non-line-of-sight and multipath effects, resulting in heavy-tailed outliers and signal outages. We address this problem by integrating the estimates from an ultra-wideband (UWB) system and inertial measurement units, but also taking advantage of the estimated velocity and height obtained from an aiding lower body biomechanical model. The proposed method is a cascaded Kalman filter-based algorithm where the orientation filter is cascaded with the robust position/velocity filter. The outliers are detected for individual measurements using the normalized innovation squared, where the measurement noise covariance is softly scaled to reduce its weight. The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The proposed algorithm was validated against an optical motion tracking system for both slow (walking) and dynamic (running and jumping) activities performed in laboratory experiments. The results show that the proposed algorithm can maintain high accuracy for tracking the location of a subject in the presence of the outliers and UWB signal outages with a combined 3-D positioning error of less than 13 cm.

Walking speed is widely used to study human health status. Wearable inertial measurement units (IMU) are promising tools for the ambulatory measurement of walking speed. Among wearable inertial sensors, the ones worn on the wrist, such as a watch or band, have relatively higher potential to be easily incorporated into daily lifestyle. Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. A novel kinematic variable is proposed, which finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable (called pca-acc) is obtained by applying sensor fusion on IMU data to find the orientation followed by the use of principal component analysis. An experimental evaluation was performed on 15 healthy young subjects during free walking trials. The experimental results show that the use of the proposed pca-acc variable can significantly improve the walking speed estimation accuracy when compared to the use of raw acceleration information (p<0.01). When Gaussian process regression is used, the resulting walking speed estimation accuracy and precision is about 5.9% and 4.7%, respectively.

SUMMARY: In this paper, the dynamics and biomimetic control of an artificial finger joint actuated by two opposing one-way shape memory alloy (SMA) muscle wires that are configured in a double spring-biased agonist–antagonist fashion is presented. This actuation system, which was described in Part I, forms the basis for biomimetic tendon-driven flexion/extension and abduction/adduction of the artificial finger. The work presented in this paper centres on thermomechanical modelling of the SMA wire, including both major and minor hysteresis loops in the phase transformation model, and co-operative control strategy of the agonist–antagonist muscle pair using a pulse-width-modulated proportional-integral-derivation (PWM–PID) controller. Parametric analysis and identification are carried out based on both simulation and experimental results. The performance advantage of the proposed co-operative control is shown using the metacarpophalangeal joint of the artificial finger.

In this study, the design approach for wireless power systems in the application of charging wristband wearable devices is studied. As the power transfer efficiency relates to the system parameters, the design approach includes an analytical model for available wireless power in planar circular coils with lateral and angular misalignments, which is typically the case in these applications. The model is then utilised in an optimisation algorithm to design the wireless power system such that maximum available power is transferred through the system. The practicality of the proposed wireless power transfer (WPT) system design is analysed in terms of positioning the power transmitter coil on a bed, while performing the power transfer during a sleeping period of the wearer. Experimental results validate the analytical model of mutual inductance variations and demonstrate the performance of the WPT system for this application.

This paper presents a theoretical analysis based on classic mechanical principles of balance of forces in bipedal walking. Theories on the state of balance have been proposed in the area of humanoid robotics and although the laws of classical mechanics are equivalent to both humans and humanoid robots, the resulting motion obtained with these theories is unnatural when compared to normal human gait. Humanoid robots are commonly controlled using the zero moment point (ZMP) with the condition that the ZMP cannot exit the foot-support area. This condition is derived from a physical model in which the biped must always walk under dynamically balanced conditions, making the centre of pressure (CoP) and the ZMP always coincident. On the contrary, humans follow a different strategy characterized by a ‘controlled fall’ at the end of the swing phase. In this paper, we present a thorough theoretical analysis of the state of balance and show that the ZMP can exit the support area, and its location is representative of the imbalance state characterized by the separation between the ZMP and the CoP. Since humans exhibit this behavior, we also present proof-of-concept results of a single subject walking on an instrumented treadmill at different speeds (from slow 0.7 m/s to fast 2.0 m/s walking with increments of 0.1 m/s) with the motion recorded using an optical motion tracking system. In order to evaluate the experimental results of this model, the coefficient of determination (R²) is used to correlate the measured ground reaction forces and the resultant of inertial and gravitational forces (anteroposterior R²= 0.93, mediolateral R²= 0.89, and vertical R²= 0.86) indicating that there is a high correlation between the measurements. The results suggest that the subject exhibits a complete dynamically balanced gait during slow speeds while experiencing a controlled fall (end of swing phase) with faster speeds. This is quantified with the root-mean-square deviation (RMSD) between the CoP and the ZMP, a relationship that grows exponentially, suggesting that the ZMP exits the support area earlier with faster walking speeds (relative to the stride duration). We conclude that the ZMP is a significant concept that can be exploited for the analysis of bipedal balance, but we also challenge the control strategy adopted in humanoid robotics that forces the ZMP to be contained within the support area causing the robot to follow unnatural patterns.