Wearable inertial sensor system towards daily human kinematic gait analysis: benchmarking analysis to MVN BIOMECH

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015 and SFRH/BD/147878/2019, by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Real-time geophysical applications with Android GNSS raw measurements

The number of Android devices enabling access to raw GNSS (Global Navigation Satellite System) measurements is rapidly increasing, thanks to the dedicated Google APIs. In this study, the Xiaomi Mi8, the first GNSS dual-frequency smartphone embedded with the Broadcom BCM47755 GNSS chipset, was employed by leveraging the features of L5/E5a observations in addition to the traditional L1/E1 observations. The aim of this paper is to present two different smartphone applications in Geoscience, both based on the variometric approach and able to work in real time. In particular, tests using both VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) to retrieve the 3D velocity of a stand-alone receiver in real-time, and VARION (Variometric Approach for Real-Time Ionosphere Observations) algorithms, able to reconstruct real-time sTEC (slant total electron content) variations, were carried out. The results demonstrate the contribution that mass-market devices can offer to the geosciences. In detail, the noise level obtained with VADASE in a static scenario-few mm/s for the horizontal components and around 1 cm/s for the vertical component-underlines the possibility, confirmed from kinematic tests, of detecting fast movements such as periodic oscillations caused by earthquakes. VARION results indicate that the noise level can be brought back to that of geodetic receivers, making the Xiaomi Mi8 suitable for real-time ionosphere monitoring

GPS Seismology for a moderate magnitude earthquake: Lessons learned from the analysis of the 31 October 2013 ML6.4 Ruisui (Taiwan) earthquake

The 31 October 2013 ML 6.4 Ruisui earthquake was well recorded by twelve 50-Hz, four 20-Hz and thirteen 1-Hz GPS receivers, and twenty-five strong motion stations located within the epicentral distance of 90 km in eastern Taiwan. Kinematic positioning solutions estimated by four GNSS software (TRACK, RTKLIB, GIPSY, VADASE) are used to derive the seismic waveforms and the co-seismic displacements for this event; strong motion accelerometers are used to verify the capability of high rate GPS to detect seismic waves generated by this earthquake. Results show that the coordinate repeatability of the GPS displacements time series are ~6 mm and ~20 mm standard deviation in the horizontal and vertical components respectively, after applying spatial filtering. The largest co-seismic displacement derived from high-rate GPS is nearly 15 centimeter at 5 km northeast of the epicenter. S waves and surface waves are successfully detected by motions of high-rate GPS and double-integrated accelerometers within the 15 km epicentral distance. For the first time twelve 50-Hz and four 20 Hz GPS observations for seismological study were used and analyzed in Taiwan; a clear benefit was evidenced with regard to the seismic waves features detection, with respect to the 1-Hz GPS data, so that ultra-high rate (> 1-Hz) observations can compensate the sparse coverage of seismic data, provided proper monuments for the GPS permanent stations are realized. Spectra analysis between co-located GPS and strong motion data further suggests that the optimal sampling rate for high-rate GPS Seismology study is 5 Hz. The 2013 Ruisui Taiwan earthquake recorded by the high-rate GPS permanent stations network in Taiwan demonstrates the benefits of GPS Seismology for a moderate size earthquake at a local scale

Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics

Deep Reinforcement Learning for Tensegrity Robot Locomotion

Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and other supplementary materials are available from http://rll.berkeley.edu/drl_tensegrityComment: International Conference on Robotics and Automation (ICRA), 2017. Project website link is http://rll.berkeley.edu/drl_tensegrit

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project