We investigate the anisotropic magnetoresistance (AMR) of ferromagnetic CoNi microhelices fabricated by electrodeposition and laser printing. We find that the geometry of the three-dimensional winding determines a characteristic angular and field-dependence of the AMR due to the competition between helical shape anisotropy and external magnetic field. Moreover, we show that there is an additional contribution to the AMR that scales proportionally to the applied current and depends on the helix chirality. We attribute this contribution to the self magnetic field induced by the current, which modifies the orientation of the magnetization relative to the current flow along the helix. Our results underline the interest of three-dimensional curved geometries to tune the AMR and realize tubular magnetoresistive devices.The anisotropic magnetoresistance (AMR) has been intensively studied in the last decades to provide insight into the charge and spin transport properties of magnetic materials 1,2 as well as to realize magnetic field sensors 3,4 and recording devices. 5,6 As most of AMR sensors are thin films, this effect is well-known and characterized for planar two-dimensional systems. Recently, advancement in micro-and nanofabrication techniques has opened new pathways for the investigation of the magnetic properties of more complex three-dimensional structures, 7-9 in which curvilinear or chiral geometries potentially allow for new manifestations of the AMR.Previous studies have addressed magnetoresistive effects in curved structures such as ferromagnetic nanotubes 10,11 and rolled-up membranes. [12][13][14] In these systems, the AMR is mainly determined by the shapedependent behavior of the magnetization in an external field. In order to further exploit the interplay between structure and magnetotransport, it is appealing to engineer microscale devices with a geometry that allows for additional AMR effects due, e.g., to chiral domain walls 15 or self-induced current dependent magnetic fields and scattering by nonmagnetic chiral defects. 16,17 The latter effects are expected to induce nonlinear contributions to the AMR that result in a directional dependence of the resistance on the current flowing in the ferromagnetic structure, similar to the unidirectional spin Hall magnetoresistance reported in ferromagnetic/nonmagnetic bilayers. [18][19][20][21] In nonmagnetic conductors, such chiral nonlinear effects have been observed in the ordinary magnetoresistance of bismuth helices, 16 carbon nanotubes, 22 and enantiopure molecular crystals. 23 In this work, we study the AMR in CoNi microhelices of left and right chirality. We find that the helical geometry imprints a characteristic angular dependence on the AMR due to the competition between shape-anisotropy, which favors the direction tangential to the helical path, and external field. This competition results in a different angular dependence of the AMR of the helices compared to planar thin films 1,2 and tubular structures. 10-14 Moreover, we find that the helical ge...

<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

Up to 17% of all motorcycle accidents occur when the rider is maneuvering through a curve and the main cause of curve accidents can be attributed to inappropriate speed and wrong intra-lane position of the motorcycle. Existing curve warning systems lack crucial state estimation components and do not scale well. We propose a new type of road curvature warning system for motorcycles, combining the latest advances in computer vision, optimal control and mapping technologies to alleviate these shortcomings. Our contributes are fourfold: 1) we predict the motorcycle's intra-lane position using a convolutional neural network (CNN), 2) we predict the motorcycle roll angle using a CNN, 3) we use an upgraded controller model that incorporates road incline for a more realistic model and prediction, 4) we design a scale-able system by utilizing HERE Technologies map database to obtain the accurate road geometry of the future path. In addition, we present two datasets that are used for training and evaluating of our system respectively, both datasets will be made publicly available. We test our system on a diverse set of real world scenarios and present a detailed case-study. We show that our system is able to predict more accurate and safer curve trajectories, and consequently warn and improve the safety for motorcyclists.

Detecting gait phases unobtrusively and reliablyin real-time for long-term unsupervised walking isimportant for clinical gait rehabilitation and early diagnosisof neurological diseases. Due to hardware limitations inwearable devices (e.g., memory and computation power),reliable real-time gait phase detection remains a challengefor unsupervised mobility assessment. In this work, a hybridalgorithm combining a reduced support vector machine(RSVM) and a finite state machine (FSM) is developedto address this. K-means clustering is used to reduce thenumber of support vectors (SVs) by constructing a smallerdataset that contains the most informative data points.For each gait phase prediction, an FSM is designed tovalidate the prediction and correct misclassifications. AfterSV reduction, the model size is reduced by 88%, and thecomputation time is reduced by a factor of 36, with onlya minor degradation in prediction performance of 4.12%,2.34%, and 4.85% for sensitivity, specificity, and accuracy,respectively. The real-time classification performance of thealgorithm is evaluated by twenty healthy subjects walkingalong a predefined route with unsupervised free-living gait.The proposed algorithm demonstrated promising real-timeperformance, with an accuracy of 91.51%, a sensitivity of91.70%, and a specificity of 95.77% across all test subjects.The algorithm also demonstrated its robustness with respectto different values of walking speed, cadence, andstride length.