Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles. Existing approaches for I2S assignment usually rely on hand crafted features and shallow classification approaches (e.g., support vector machines), with no agreement regarding the most suitable features for the assignment task. Moreover, estimating the complete orientation alignment of an IMU relative to the segment it is attached to using a machine learning approach has not been shown in literature so far. This is likely due to the high amount of training data that have to be recorded to suitably represent possible IMU alignment variations. In this work, we propose online approaches for solving the assignment and alignment tasks for an arbitrary amount of IMUs with respect to a biomechanical lower body model using a deep learning architecture and windows of 128 gyroscope and accelerometer data samples. For this, we combine convolutional neural networks (CNNs) for local filter learning with long-short-term memory (LSTM) recurrent networks as well as generalized recurrent units (GRUs) for learning time dynamic features. The assignment task is casted as a classification problem, while the alignment task is casted as a regression problem. In this framework, we demonstrate the feasibility of augmenting a limited amount of real IMU training data with simulated alignment variations and IMU data for improving the recognition/estimation accuracies. With the proposed approaches and final models we achieved 98.57% average accuracy over all segments for the I2S assignment task (100% when excluding left/right switches) and an average median angle error over all segments and axes of $2.91$ ° for the I2S alignment task.

Background: Deep and slow abdominal breathing is an important skill for the management of stress and pain. However, despite multiple proofs on the effectiveness of biofeedback, most breathing apps remain limited to pacing specific breathing patterns, without sensor feedback on the actual breathing behavior. Objective: To fill this gap, an app named Breathing-Mentor was developed. This app combines effective visualization of the instruction with biofeedback on deep abdominal breathing, based on the mobile phone’s accelerometers. The aim of this pilot study was to investigate users’ feedback and breathing behavior during initial contact with the app. Methods: To reveal the possible effects of biofeedback, two versions of the mobile app were developed. Both contained the same visual instruction, but only the full version included additional biofeedback. In total, 40 untrained participants were randomly assigned to one of the two versions of the app. They had to follow the app’s instructions as closely as possible for 5 min. Results: The group with additional biofeedback showed an increased signal-to-noise ratio for instructed breathing frequency (0.1 Hz) compared with those using visual instruction without biofeedback (F1,37=4.18; P<.048). During this initial contact with the full version, self-reported relaxation effectivity was, however, lower than the group using visual instruction without biofeedback (t37=−2.36; P=.02), probably owing to increased cognitive workload to follow the instruction. Conclusions: This study supports the feasibility and usefulness of incorporating biofeedback in the Breathing-Mentor app to train abdominal breathing. Immediate effects on relaxation levels should, however, not be expected for untrained users.

Secondary flow phenomena have a considerable part in the efficiency loss in turbomachinery. A potential method to reduce these secondary flow losses is tangential end wall contouring inside the blade passages. The present paper is the second of two papers which investigate the impact of tangential end wall contouring on 2D and 3D airfoils compared to a baseline configuration. The first paper summarizes the experimental investigation on a 2-stage air driven turbine test rig located at the Institute of Power Plant Technology, Steam and Gas Turbines RWTH Aachen University. To enhance the impact of the tangential end wall contours (TEWC) on the near wall flow, the rotor cavities are sealed by means of combined brush- and labyrinth seals. The stator cavities are sealed by labyrinth seals, only. This paper investigates the flow phenomena using CFD with the commercial software package ANSYS 15.0©. The brush seals are modeled by using the porous body approach and are calibrated using pressure drop measurements across the first rotor cavity. The experimental data will be presented and is used to validate the numerical model. For this, circumferential plots for the measurement planes are shown. In addition a detailed description of the changes in vortex formations as well as blade loading will be given for the various configurations. Finally a discussion of the impact on the turbine’s efficiency is given. It has been found, that for steady CFD simulations the use of stage interfaces suppresses the positive effects of the tangential end wall contour onto the downstream blade row.

This paper proposes a framework for the integration of handwriting recognition into natural user interfaces. As more and more pen-enabled touch displays are available, we make use of the distinction between touch actions and pen actions. Furthermore, we apply a recently introduced mode detection approach to distinguish between handwritten strokes and graphics drawn with the pen. These ideas are implemented in the Touch & Write SDK which can be used for various applications. In order to evaluate the effectiveness of our approach, we have conducted experiments for an annotation scenario. We asked several users to mark and label several objects in videos. We have measured the labeling time when using our novel user interaction system and compared it to the time needed when using common labeling tools. Furthermore, we compare our handwritten input paradigm to other existing systems. It turns out that the annotation is performed much faster when using our method and the user experience is also much better.

BACKGROUND Deep and slow abdominal breathing is an important skill for the management of stress and pain. However, despite multiple proofs on the effectiveness of biofeedback, most breathing apps remain limited to pacing specific breathing patterns, without sensor feedback on the actual breathing behavior. OBJECTIVE To fill this gap, an app named Breathing-Mentor was developed. This app combines effective visualization of the instruction with biofeedback on deep abdominal breathing, based on the mobile phone’s accelerometers. The aim of this pilot study was to investigate users’ feedback and breathing behavior during initial contact with the app. METHODS To reveal the possible effects of biofeedback, two versions of the mobile app were developed. Both contained the same visual instruction, but only the full version included additional biofeedback. In total, 40 untrained participants were randomly assigned to one of the two versions of the app. They had to follow the app’s instructions as closely as possible for 5 min. RESULTS The group with additional biofeedback showed an increased signal-to-noise ratio for instructed breathing frequency (0.1 Hz) compared with those using visual instruction without biofeedback (F1,37=4.18; P<.048). During this initial contact with the full version, self-reported relaxation effectivity was, however, lower than the group using visual instruction without biofeedback (t37=−2.36; P=.02), probably owing to increased cognitive workload to follow the instruction. CONCLUSIONS This study supports the feasibility and usefulness of incorporating biofeedback in the Breathing-Mentor app to train abdominal breathing. Immediate effects on relaxation levels should, however, not be expected for untrained users.