An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data.

In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.

An Efficient 3D Human Pose Retrieval and Reconstruction from 2D Image-Based Landmarks.

We propose an efficient and novel architecture for 3D articulated human pose retrieval and reconstruction from 2D landmarks extracted from a 2D synthetic image, an annotated 2D image, an in-the-wild real RGB image or even a hand-drawn sketch. Given 2D joint positions in a single image, we devise a data-driven framework to infer the corresponding 3D human pose. To this end, we first normalize 3D human poses from Motion Capture (MoCap) dataset by eliminating translation, orientation, and the skeleton size discrepancies from the poses and then build a knowledge-base by projecting a subset of joints of the normalized 3D poses onto 2D image-planes by fully exploiting a variety of virtual cameras. With this approach, we not only transform 3D pose space to the normalized 2D pose space but also resolve the 2D-3D cross-domain retrieval task efficiently. The proposed architecture searches for poses from a MoCap dataset that are near to a given 2D query pose in a definite feature space made up of specific joint sets. These retrieved poses are then used to construct a weak perspective camera and a final 3D posture under the camera model that minimizes the reconstruction error. To estimate unknown camera parameters, we introduce a nonlinear, two-fold method. We exploit the retrieved similar poses and the viewing directions at which the MoCap dataset was sampled to minimize the projection error. Finally, we evaluate our approach thoroughly on a large number of heterogeneous 2D examples generated synthetically, 2D images with ground-truth, a variety of real in-the-wild internet images, and a proof of concept using 2D hand-drawn sketches of human poses. We conduct a pool of experiments to perform a quantitative study on PARSE dataset. We also show that the proposed system yields competitive, convincing results in comparison to other state-of-the-art methods.

Action observation improves sit-to-walk in patients with Parkinson's disease and freezing of gait. Biomechanical analysis of performance.

INTRODUCTION: Freezing of gait (FoG) is one of the most disabling gait disorders in Parkinson's disease (PD), reflecting motor and cognitive impairments, mainly related to dopamine deficiency. Recent studies investigating kinematic and kinetic factors affecting gait in these patients showed a postural instability characterized by disturbed weight-shifting, inappropriate anticipatory postural adjustment, worse reactive postural control, and a difficulty executing complex motor tasks (i.e. sit-to-walk). These symptoms are difficult to alleviate and not very responsive to Levodopa. For this reason, additional therapeutic actions based on specific therapeutic protocols may help patients with their daily lives. We conducted a randomized control trial aimed to test if two clinical protocols for PD patients with FoG were effective to improve postural control. METHODS: Rehabilitation protocols, conceived to improve gait, were based on learning motor exercises with the Action Observation plus Sonification (AOS) technique, or by the use of external sensory cues. We collected biomechanical data (Center of Mass COM, Center of Pressure COP, and moving timings), using the sit-to-walk task as a measure of motor and gait performance. RESULTS: Kinetic and kinematic data showed that when treatment effects consolidate, patients treated with AOS protocol are more efficient in merging subsequent motor tasks (sit-to-stand and gait initiation), and diminished the total moving time and the area of the COP positions. CONCLUSION: We demonstrated for the first time that PD patients with FoG treated with an AOS protocol aimed at relearning appropriate gait patterns increased balance control and re-acquired more efficient postural control.

Analyzing Spinal Shape Changes During Posture Training Using a Wearable Device.

Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user's posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.

Action Observation Plus Sonification. A Novel Therapeutic Protocol for Parkinson's Patient with Freezing of Gait.

Freezing of gait (FoG) is a disabling symptom associated with falls, with little or no responsiveness to pharmacological treatment. Current protocols used for rehabilitation are based on the use of external sensory cues. However, cued strategies might generate an important dependence on the environment. Teaching motor strategies without cues [i.e., action observation (AO) plus Sonification] could represent an alternative/innovative approach to rehabilitation that matters most on appropriate allocation of attention and lightening cognitive load. We aimed to test the effects of a novel experimental protocol to treat patients with Parkinson's disease (PD) and FoG, using functional, and clinical scales. The experimental protocol was based on AO plus Sonification. 12 patients were treated with 8 motor gestures. They watched eight videos showing an actor performing the same eight gestures, and then tried to repeat each gesture. Each video was composed by images and sounds of the gestures. By means of the Sonification technique, the sounds of gestures were obtained by transforming kinematic data (velocity) recorded during gesture execution, into pitch variations. The same 8 motor gestures were also used in a second group of 10 patients; which were treated with a standard protocol based on a common sensory stimulation method. All patients were tested with functional and clinical scales before, after, at 1 month, and 3 months after the treatment. Data showed that the experimental protocol have positive effects on functional and clinical tests. In comparison with the baseline evaluations, significant performance improvements were seen in the NFOG questionnaire, and the UPDRS (parts II and III). Importantly, all these improvements were consistently observed at the end, 1 month, and 3 months after treatment. No improvement effects were found in the group of patients treated with the standard protocol. These data suggest that a multisensory approach based on AO plus Sonification, with the two stimuli semantically related, could help PD patients with FoG to relearn gait movements, to reduce freezing episodes, and that these effects could be prolonged over time.

Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models.

This paper introduces a new method for data analysis of animal muscle activation during locomotion. It is based on fitting Gaussian mixture models (GMMs) to surface EMG data (sEMG). This approach enables researchers/users to isolate parts of the overall muscle activation within locomotion EMG data. Furthermore, it provides new opportunities for analysis and exploration of sEMG data by using the resulting Gaussian modes as atomic building blocks for a hierarchical clustering. In our experiments, composite peak models representing the general activation pattern per sensor location (one sensor on the long back muscle, three sensors on the gluteus muscle on each body side) were identified per individual for all 14 horses during walk and trot in the present study. Hereby we show the applicability of the method to identify composite peak models, which describe activation of different muscles throughout cycles of locomotion.

One Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial Sensor.

A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings at a single body location from a single step. We recorded accelerations and angular velocities of 26 subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back, right wrist and left ankle) when performing standardized gait tasks. The collected data were segmented into individual walking steps. We trained random forest classifiers in order to estimate soft biometrics (gender, age and height). We applied two different validation methods to the process, 10-fold cross-validation and subject-wise cross-validation. For all three classification tasks, we achieve high accuracy values for all four sensor locations. From these results, we can conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for a robust estimation of the gender, height and age of a person.

MotionExplorer: exploratory search in human motion capture data based on hierarchical aggregation.

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

The holistic integration of virtual screening in drug discovery.

During the past decade, virtual screening (VS) has come of age. In this review, we document the evolution and maturation of VS from a rather exotic, stand-alone method toward a versatile hit and lead identification technology. VS campaigns have become fully integrated into drug discovery campaigns, evenly matched and complementary to high-throughput screening (HTS) methods. Here, we propose a novel classification of VS applications to help to monitor the advances in VS and to support future improvement of computational hit and lead identification methods. Several relevant VS studies from recent publications, in both academic and industrial settings, were selected to demonstrate the progress in this area. Furthermore, we identify challenges that lie ahead for the development of integrated VS campaigns.

Exploring the chemical space of multitarget ligands using aligned self-organizing maps.

Design of multitarget drugs and polypharmacological compounds has become popular during the past decade. However, the main approach to design such compounds is to link two selective ligands via a flexible linker. Although such chimeric ligands often have reasonable potency in vitro, the in vivo efficacy is low due to high molecular weight, low ligand efficiency, and poor pharmacokinetic profile. We developed an unprecedented in silico approach for fragment-based design of multitarget ligands. It relies on superposition of the chemical spaces related to the affinity on single targets represented by self-organizing maps. We used this approach for screening of molecular fragments, which bind to the enzymes 5-lipoxygenase (5-LO) and soluble epoxide hydrolase (sEH). Using STD-NMR and activity-based assays, we were able to identify fragments binding to both targets. Furthermore, we were able to expand one of the fragments to a potent dual inhibitor bearing a reasonable molecular weight (MW = 446) and high affinity to both targets (IC50 of 0.03 µM toward 5-LO and 0.17 µM toward sEH).

Do Germans keep patients too long in hospital? A prospective randomized trial.

Cost-effectiveness reduces hospital stay for all patients with thyroid surgery but lacks information on medical comparability and patients' fulfilled expectations. The aim of this study was to assess if a hospital stay of 2 days after thyroid surgery had a negative influence on the medical quality or on health-related quality of life. In a controlled prospective randomized trial with 238 patients, a postoperative hospital stay of 2 days was compared to one longer than 2 days. The postoperative medical investigation included serum calcium levels, laryngeal nerve function, and suction drainage volume. Health-related quality of life was assessed on the day of admission before the operation and again 14 days after discharge. Fourteen days after discharge patients were also asked about their subjective health. Despite the study design, it was necessary, for ethical reasons, to let the patients decide when to leave the hospital. In the 2-day study group, 56.6% of the patients preferred hospitalization for more than 2 days (most choosing 3 days). Medical reasons were hyperthyroidism (p < 0.02) and postoperative hypocalcemia (p < 0.03). In the control group 28% left the hospital after 2 days. Only 35% of the patients left the hospital at the second postoperative day, but 60% of these patient supported this shorter hospitalization. Health-related quality of life and self-rated health was significantly higher in patients leaving the hospital on the second postoperative day. A 2-day hospital stay after thyroid surgery is possible and does not show medical or health-related quality of life disadvantages in patients with an uncomplicated postoperative course who consider themselves healthy.