D3PM: a comprehensive database for protein motions ranging from residue to domain.

BACKGROUND: Knowledge of protein motions is significant to understand its functions. While currently available databases for protein motions are mostly focused on overall domain motions, little attention is paid on local residue motions. Albeit with relatively small scale, the local residue motions, especially those residues in binding pockets, may play crucial roles in protein functioning and ligands binding. RESULTS: A comprehensive protein motion database, namely D3PM, was constructed in this study to facilitate the analysis of protein motions. The protein motions in the D3PM range from overall structural changes of macromolecule to local flip motions of binding pocket residues. Currently, the D3PM has collected 7679 proteins with overall motions and 3513 proteins with pocket residue motions. The motion patterns are classified into 4 types of overall structural changes and 5 types of pocket residue motions. Impressively, we found that less than 15% of protein pairs have obvious overall conformational adaptations induced by ligand binding, while more than 50% of protein pairs have significant structural changes in ligand binding sites, indicating that ligand-induced conformational changes are drastic and mainly confined around ligand binding sites. Based on the residue preference in binding pocket, we classified amino acids into "pocketphilic" and "pocketphobic" residues, which should be helpful for pocket prediction and drug design. CONCLUSION: D3PM is a comprehensive database about protein motions ranging from residue to domain, which should be useful for exploring diverse protein motions and for understanding protein function and drug design. The D3PM is available on www.d3pharma.com/D3PM/index.php .

Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms.

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.

The sequelae of mastectomy and quadrantectomy with respect to the reaching movement in breast cancer survivors: evidence for an integrated rehabilitation protocol during oncological care.

BACKGROUND: Breast cancer (BC) is the most common cancer among women in developed countries. Several types of surgical interventions are commonly used in BC, such as mastectomy and quadrantectomy, followed by radiation or not. Today, BC rehabilitation can help survivors obtain and maintain the highest physical, social, psychological, and vocational functioning possible, within the limits that are created by cancer and its treatments. OBJECTIVE: To verify, before and after a specific rehabilitation protocol treatment, the recovery of the fluidity of the reaching movement. METHODS: Patients after BC surgery were enrolled and assigned by cluster randomization into 2 groups through a block randomization list: mastectomy (Mas) and quadrantectomy (Quad). Evaluation scales (DASH and VAS) were administered, and biomechanical evaluations were performed for each group before treatment (T0 = baseline), at the end of the rehabilitative treatment (T1 = 12 sessions, 2/week), and after 3 months of follow-up (T2). RESULTS: Fifty-nine (Mas group = 29; Quad group = 30) after BC surgery were enrolled. VAS scores improved along the evaluation times at T0-T1 and T0-T2 (p < 0.001), without a statistically significant difference between groups. With regard to the normalized jerk, there was no significant interaction between time group (F = 2.029; p = 0.136). There was a significant decrease between T0-T1 and T1-T2 (F = 60.189; p < 0.001). Velocity improved significantly between T0-T1 and T1-T2 (F = 10.322; p < 0.001). There was a significant interaction for the elbow angle at the end of movement between time and group at T2 (F = 5.022; p = 0.029). CONCLUSION: The integrated, low-intensity, rehabilitative intervention is effective, even if it is not performed in the first period after BC surgery, without any difference between mastectomy and quadrantectomy.

Estimation of a Human-Maneuvered Target Incorporating Human Intention.

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.

Visual Leakage Inspection in Chemical Process Plants Using Thermographic Videos and Motion Pattern Detection.

Liquid leakage from pipelines is a critical issue in large-scale chemical process plants since it can affect the normal operation of the plant and pose unsafe and hazardous situations. Therefore, leakage detection in the early stages can prevent serious damage. Developing a vision-based inspection system by means of IR imaging can be a promising approach for accurate leakage detection. IR cameras can capture the effect of leaking drops if they have higher (or lower) temperature than their surroundings. Since the leaking drops can be observed in an IR video as a repetitive phenomenon with specific patterns, motion pattern detection methods can be utilized for leakage detection. In this paper, an approach based on the Kalman filter is proposed to track the motion of leaking drops and differentiate them from noise. The motion patterns are learned from the training data and applied to the test data to evaluate the accuracy of the method. For this purpose, a laboratory demonstrator plant is assembled to simulate the leakages from pipelines, and to generate training and test videos. The results show that the proposed method can detect the leaking drops by tracking them based on obtained motion patterns. Furthermore, the possibilities and conditions for applying the proposed method in a real industrial chemical plant are discussed at the end.

Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion.

Aiming at the requirement of rapid recognition of the wearer's gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.

A Performance Evaluation Scheme for Multiple Object Tracking with HFSWR.

High-frequency surface wave radar (HFSWR) can detect and continuously track ship objects in real time and beyond the horizon. When ships navigate in a sea area, their motions in a time period form a scenario. The diversity and complexity of the motion scenarios make it difficult to accurately track ships, in which failures such as track fragmentation (TF) are frequently observed. However, it is still unclear how and to what degrees the motions of ships affect the tracking performance, especially which motion patterns can cause tracking failures. This paper addresses this problem and attempts to undertake a first step towards providing an intensive quantitative performance assessment and vulnerability detection scheme for ship-tracking algorithms by proposing an evolutionary and data-mining-based approach. Low-dimensional scenarios in terms of multiple maneuvering ship objects are generated using a grammar-based model. Closed-loop feedback is introduced using evolutionary computation to efficiently collect scenarios that cause more and more tracking performance loss, which provides diversified cases for analysing using data-mining technique to discover indicators of tracking vulnerability. Results on different tracking algorithms show that more cluster and convergence patterns and longer duration of our convoy and cluster patterns in the scenarios can cause severer TF to HFSWR ship tracking.

Age and sex differences in ranges of motion and motion patterns.

This study investigated the effects of age and sex on joint ranges of motion (ROMs) and motion patterns. Forty participants performed 18 motions using eight body segments at self-selected speeds. Older subjects showed smaller ROMs than younger subjects for 11 motions; the greatest difference in ROM was 44.9% for eversion/inversion of the foot. Older subjects also required more time than younger subjects to approach the peak angular velocity for six motions. In contrast, sex significantly affected ROMs but not motion patterns. Male subjects exhibited smaller ROMs than female subjects for four motions; the greatest sex-dependent difference in ROM was 29.7% for ulnar/radial deviation of the hand. The age and sex effects depended on the specific segments used and motions performed, possibly because of differences in anatomical structures and frequencies of use of the joints in habitual physical activities between the groups.

TipDet: A multi-keyframe motion-aware framework for tip detection during ultrasound-guided interventions.

BACKGROUND AND OBJECTIVE: Automatic needle tip detection is important in real-time ultrasound (US) images that are utilized to guide interventional needle puncture procedures in clinical settings. However, due to the spatial indiscernibility problem caused by the severe background interferences and the tip characteristics of small size, being grayscale and indistinctive appearance patterns, tip detection in US images is challenging. METHODS: To achieve precise tip detection in US images against spatial indiscernibility, a novel multi-keyframe motion-aware framework called TipDet is proposed. It can identify tips based on their short-term spatial-temporal pattern and long-term motion pattern. In TipDet, first, an adaptive keyframe model (AKM) is proposed to decide whether a frame is informative to serve as a keyframe for long-term motion pattern learning. Second, candidate tip detection is conducted using a two-stream backbone (TSB) based on their short-term spatial-temporal pattern. Third, to further identify the true one in the candidate tips, a novel method for learning the long-term motion pattern of the tips is proposed based on the proposed optical-flow-aware multi-head cross-attention (OFA-MHCA). RESULTS: On the clinical human puncture dataset, which includes 4195 B-mode images, the experimental results show that the proposed TipDet can achieve precise tip detection against the spatial indiscernibility problem, achieving 78.7 % AP0.1:0.5 and 8.9 % improvement over the base detector at approximately 20 FPS. Moreover, a tip localization error of 1.3±0.6 % is achieved, exceeding the existing method. CONCLUSIONS: The proposed TipDet can facilitate a wider and easier application of US-guided interventional procedures by providing robust and precise needle tip localization. The codes and data are available at https://github.com/ResonWang/TipDet.

3d human motion retrieval based on human hierarchical index structure.

With the development and wide application of motion capture technology, the captured motion data sets are becoming larger and larger. For this reason, an efficient retrieval method for the motion database is very important. The retrieval method needs an appropriate indexing scheme and an effective similarity measure that can organize the existing motion data well. In this paper, we represent a human motion hierarchical index structure and adopt a nonlinear method to segment motion sequences. Based on this, we extract motion patterns and then we employ a fast similarity measure algorithm for motion pattern similarity computation to efficiently retrieve motion sequences. The experiment results show that the approach proposed in our paper is effective and efficient.

The Variability of Lumbar Sequential Motion Patterns: Observational Study.

BACKGROUND: Physiological motion of the lumbar spine is a topic of interest for musculoskeletal health care professionals since abnormal motion is believed to be related to lumbar complaints. Many researchers have described ranges of motion for the lumbar spine, but only few have mentioned specific motion patterns of each individual segment during flexion and extension, mostly comprising the sequence of segmental initiation in sagittal rotation. However, an adequate definition of physiological motion is still lacking. For the lower cervical spine, a consistent pattern of segmental contributions in a flexion-extension movement in young healthy individuals was described, resulting in a definition of physiological motion of the cervical spine. OBJECTIVE: This study aimed to define the lumbar spines' physiological motion pattern by determining the sequence of segmental contribution in sagittal rotation of each vertebra during maximum flexion and extension in healthy male participants. METHODS: Cinematographic recordings were performed twice in 11 healthy male participants, aged 18-25 years, without a history of spine problems, with a 2-week interval (time point T1 and T2). Image recognition software was used to identify specific patterns in the sequence of segmental contributions per individual by plotting segmental rotation of each individual segment against the cumulative rotation of segments L1 to S1. Intraindividual variability was determined by testing T1 against T2. Intraclass correlation coefficients were tested by reevaluation of 30 intervertebral sequences by a second researcher. RESULTS: No consistent pattern was found when studying the graphs of the cinematographic recordings during flexion. A much more consistent pattern was found during extension, especially in the last phase. It consisted of a peak in rotation in L3L4, followed by a peak in L2L3, and finally, in L1L2. This pattern was present in 71% (15/21) of all recordings; 64% (7/11) of the participants had a consistent pattern at both time points. Sequence of segmental contribution was less consistent in the lumbar spine than the cervical spine, possibly caused by differences in facet orientation, intervertebral discs, overprojection of the pelvis, and muscle recruitment. CONCLUSIONS: In 64% (7/11) of the recordings, a consistent motion pattern was found in the upper lumbar spine during the last phase of extension in asymptomatic young male participants. Physiological motion of the lumbar spine is a broad concept, influenced by multiple factors, which cannot be captured in a firm definition yet. TRIAL REGISTRATION: ClinicalTrials.gov NCT03737227; https://clinicaltrials.gov/ct2/show/NCT03737227. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/14741.

Respiratory and thoracoabdominal motion pattern at rest and after sub-maximum effort in children with asthma.

Asthma involves an increase in airway resistance even in periods between attacks, which generates changes in thoracoabdominal kinematics. The aim of the present study was to detect these adaptations at rest and after physical effort. Evaluations were performed using optoelectronic plethysmography at rest and immediately after physical effort of moderate intensity. Thirty-two children and adolescents participated in the present study (16 asthma- AG and 16 health controls-CG). After exercise, the AG exhibited a less variability of respiratory variables. The kinematic behavior of thoracoabdominal motion was the inverse of that found in healthy controls. These findings suggest mechanical and physiological adaptations to minimize the possible turbulence of the airflow and reduce the impact of airway resistance during physical exertion. Moreover, these changes are found even at rest and in patients whose asthma is clinically controlled.

Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion.

An exoskeleton is a kind of intelligent wearable device with bioelectronics and biomechanics. To realize its effective assistance to the human body, an exoskeleton needs to recognize the real time movement pattern of the human body in order to make corresponding movements at the right time. However, it is of great difficulty for an exoskeleton to fully identify human motion patterns, which are mainly manifested as incomplete acquisition of lower limb motion information, poor feature extraction ability, and complicated steps. Aiming at the above consideration, the motion mechanisms of human lower limbs have been analyzed in this paper, and a set of wearable bioelectronics devices are introduced based on an electromyography (EMG) sensor and inertial measurement unit (IMU), which help to obtain biological and kinematic information of the lower limb. Then, the Dual Stream convolutional neural network (CNN)-ReliefF was presented to extract features from the fusion sensors' data, which were input into four different classifiers to obtain the recognition accuracy of human motion patterns. Compared with a single sensor (EMG or IMU) and single stream CNN or manual designed feature extraction methods, the feature extraction based on Dual Stream CNN-ReliefF shows better performance in terms of visualization performance and recognition accuracy. This method was used to extract features from EMG and IMU data of six subjects and input these features into four different classifiers. The motion pattern recognition accuracy of each subject under the four classifiers is above 97%, with the highest average recognition accuracy reaching 99.12%. It can be concluded that the wearable bioelectronics device and Dual Stream CNN-ReliefF feature extraction method proposed in this paper enhanced an exoskeleton's ability to capture human movement patterns, thus providing optimal assistance to the human body at the appropriate time. Therefore, it can provide a novel approach for improving the human-machine interaction of exoskeletons.

Changes in task-specific fear of movement and impaired trunk motor control by pain neuroscience education and exercise: A preliminary single-case study of a worker with low back pain.

We report a case (a worker with low back pain) who was provided patient education and therapeutic exercise, and we performed a detailed kinematic analysis of his work-related activity over time. The subjects were one 28-year-old male worker with low back pain. In addition, to clearly identify impaired trunk movement during work-related activity in the low back pain subject, 20 age-matched healthy males (control group) were also included as a comparison subject. He received pain neurophysiology education and exercise instruction. We analyzed the subject's trunk movement pattern during a lifting task examined by a three-dimensional-motion capture system. In addition, task-specific fear that occurred during the task was assessed by the numerical rating scale. The assessment was performed at the baseline phase (4 data points), the intervention phase (8 data points), and the follow-up phase (8 data points), and finally at 3 and 8 months after the follow-up phase. No intervention was performed in the control group; they underwent only one kinematic evaluation at baseline. As a result, compared to the control group, the low back pain subject had slower trunk movement velocity (peak trunk flexion velocity = 50.21 deg/s, extension velocity = -47.61 deg/s), and his upper-lower trunk segments indicated an in-phase motion pattern (mean absolute relative phase = 15.59 deg) at baseline. The interventions reduced his pain intensity, fear of movement, and low back pain-related disability; in addition, his trunk velocity was increased (peak trunk flexion velocity = 82.89 deg/s, extension velocity = -77.17 deg/s). However, the in-phase motion pattern of his trunk motor control remained unchanged (mean absolute relative phase = 16.00 deg). At 8 months after the end of the follow-up, the subject's in-phase motion pattern remained (mean absolute relative phase = 13.34 deg) and his pain intensity had increased. This report suggests that if impaired trunk motor control remains unchanged after intervention, as in the course of the low back pain subject, it may eventually be related to a recurrence of low back pain symptoms.

Hierarchical anatomical structure-aware based thoracic CT images registration.

Accurate thoracic CT image registration remains challenging due to complex joint deformations and different motion patterns in multiple organs/tissues during breathing. To combat this, we devise a hierarchical anatomical structure-aware based registration framework. It affords a coordination scheme necessary for constraining a general free-form deformation (FFD) during thoracic CT registration. The key is to integrate the deformations of different anatomical structures in a divide-and-conquer way. Specifically, a deformation ability-aware dissimilarity metric is proposed for complex joint deformations containing large-scale flexible deformation of the lung region, rigid displacement of the bone region, and small-scale flexible deformation of the rest region. Furthermore, a motion pattern-aware regularization is devised to handle different motion patterns, which contain sliding motion along the lung surface, almost no displacement of the spine and smooth deformation of other regions. Moreover, to accommodate large-scale deformation, a novel hierarchical strategy, wherein different anatomical structures are fused on the same control lattice, registers images from coarse to fine via elaborate Gaussian pyramids. Extensive experiments and comprehensive evaluations have been executed on the 4D-CT DIR and 3D DIR COPD datasets. It confirms that this newly proposed method is locally comparable to state-of-the-art registration methods specializing in local deformations, while guaranteeing overall accuracy. Additionally, in contrast to the current popular learning-based methods that typically require dozens of hours or more pre-training with powerful graphics cards, our method only takes an average of 63 s to register a case with an ordinary graphics card of RTX2080 SUPER, making our method still worth promoting. Our code is available at https://github.com/heluxixue/Structure_Aware_Registration/tree/master.

Robotic Shoulder Rehabilitation With the Hybrid Assistive Limb in a Patient With Delayed Recovery After Postoperative C5 Palsy: A Case Report.

C5 palsy is a serious complication that may occur after cervical spine surgery; however, standard procedures for shoulder rehabilitation for patients with postoperative C5 palsy have not yet been established. We used a wearable robot suit Hybrid Assistive Limb (HAL) in a patient with delayed recovery after postoperative C5 palsy and conducted shoulder abduction training with the HAL. A 62-year-old man presented with weakness in his left deltoid muscle 2 days after cervical spine surgery. He experienced great difficulty in elevating his left arm and was diagnosed with postoperative C5 palsy. Seven months after surgery, shoulder abduction training with a HAL was initiated. In total, 23 sessions of shoulder HAL rehabilitation were conducted until 26 months after surgery. His shoulder abduction angle and power improved at every HAL session, and he was able to fully elevate his arm without any compensatory movement after the 23rd session, suggesting that the HAL is a useful tool for shoulder rehabilitation in patients with postoperative C5 palsy. We employed shoulder HAL training for a patient with delayed recovery from postoperative C5 palsy and achieved complete restoration of shoulder function. We believe that the HAL-based training corrected the erroneous motion pattern of his paralyzed shoulder and promoted errorless motor learning for recovery. Our collective experience suggests that shoulder HAL training could be an effective therapeutic tool for patients with postoperative C5 palsy.

Automatic segmentation and tracking of biological prosthetic heart valves.

Purpose: Prosthetic heart valve designs must be rigorously tested using cardiovascular equipment. The valve orifice area over time constitutes a key quality metric which is typically assessed manually, thus a tedious and error-prone task. From a computer vision viewpoint, a major unsolved issue lies in the orifice being partly occluded by the leaflets' inner side or inaccurately depicted due to its transparency. Here, we address this issue, which allows us to focus on the accurate and automatic computation of valve orifice areas. Approach: We propose a segmentation approach based on the detection of the leaflets' free edges. Using video frames recorded with a high-speed digital camera during in vitro simulations, an initial estimation of the orifice area is first obtained via active contouring and thresholding and then refined to capture the leaflet free edges via a curve transformation mechanism. Results: Experiments on video data from pulsatile flow testing demonstrate the effectiveness of our approach: a root-mean-square error (RMSE) on the temporal extracted orifice areas between 0.8% and 1.2%, an average Jaccard similarity coefficient between 0.933 and 0.956, and an average Hausdorff distance between 7.2 and 11.9 pixels. Conclusions: Our approach significantly outperformed a state-of-the-art algorithm in terms of evaluation metrics related to valve design (RMSE) and computer vision (accuracy of the orifice shape). It can also cope with lower quality videos and is better at processing frames showing an almost closed valve, a crucial quality for assessing valve design malfunctions related to their improper closing.

Computational Model of Motion Sickness Describing the Effects of Learning Exogenous Motion Dynamics.

The existing computational models used to estimate motion sickness are incapable of describing the fact that the predictability of motion patterns affects motion sickness. Therefore, the present study proposes a computational model to describe the effect of the predictability of dynamics or the pattern of motion stimuli on motion sickness. In the proposed model, a submodel - in which a recursive Gaussian process regression is used to represent human features of online learning and future prediction of motion dynamics - is combined with a conventional model of motion sickness based on an observer theory. A simulation experiment was conducted in which the proposed model predicted motion sickness caused by a 900 s horizontal movement. The movement was composed of a 9 m repetitive back-and-forth movement pattern with a pause. Regarding the motion condition, the direction and timing of the motion were varied as follows: (a) Predictable motion (M_P): the direction of the motion and duration of the pause were set to 8 s; (b) Motion with unpredicted direction (M_dU): the pause duration was fixed as in (M_P), but the motion direction was randomly determined; (c) Motion with unpredicted timing (M_tU): the motion direction was fixed as in (M_P), but the pause duration was randomly selected from 4 to 12 s. The results obtained using the proposed model demonstrated that the predicted motion sickness incidence for (M_P) was smaller than those for (M_dU) and (M_tU) and no considerable difference was found between M_dU and M_tU. This tendency agrees with the sickness patterns observed in a previous experimental study in which the human participants were subject to motion conditions similar to those used in our simulations. Moreover, no significant differences were found in the predicted motion sickness incidences at different conditions when the conventional model was used.

Effects of differential probabilities of reinforcement on human timing.

We investigated how differential payoffs affect the temporal discrimination of humans. In a temporal bisection task, participants learned to make one response after a short sample and another after a long sample. When presented with a range of intermediate samples, the proportion of responses fitted well a Gaussian-like distribution function characterized by a location (bias), a scale (sensitivity) parameter, and two asymptote (discrimination) parameters. In Experiment 1, when one response yielded more reinforcers than the other, parameters were unaltered, but overall responses increased for the response producing higher payoffs. In Experiment 2, we used a video game to track motion during the sample and participants learned to approach the "short" response location at sample onset and remain there before departing to the "long" location on long trials. Departure times were shorter when "long" choices produced higher payoffs than "short" and matched well the shifted psychometric functions. However, on some trials, subjects were biased for short, returning to the short side after having departed towards long. Evidence was found for effects of differential payoffs on response bias, but discrimination and sensitivity did not change consistently. These results favor a behavioral account of timing processes.

Simultaneous identification of human body model parameters and gait trajectory from 3D motion capture data.

The analysis of human movements rests on a realistic human body model. Deducing model parameters from anthropomorphic data is challenging since these are inherently imprecise. An approach to improve model accuracy is the parameter adaptation based on motion data. 3D motion capture data are already being used for generating the trajectories of a human body model, so combining motion tracking and parameter identification seems most natural. This paper introduces a holistic approach to simultaneously identify the geometric parameters of a kinematic human lower limb model and the parameters defining a (cyclic) gait trajectory, based on 3D marker positions. The result is a time-continuous description of a physiologically compatible lower extremity movement along with optimal model parameters so to best reproduce the captured motion. The method takes into account restrictions such as the range of motion of human body joints and is robust against missing data due to marker occlusions or failures of the measurement system. Considering multiple gait cycles of a movement trial, we derive the characteristic motion pattern (CMP) of a specific subject walking at a specific speed. Our method further allows for motion analysis and assessment, but also for motion synthesis with arbitrary time span and time resolution and can thus be used for simulations and trajectory planning of rehabilitation and movement assistance systems, such as exoskeletons.