Motion Capture Experiments Research Articles

Fuzzy-pid control design of biped robot based on motion capture technology

Abstract To improve the precision of the biped robot’s walking control system, a fuzzy PID controller with a variable theory domain was added to the design of the biped robot’s lower limb control system to optimize the tracking process of joint angular displacement and improve the motion effect. Firstly, the motion capture experiment is carried out to obtain the angular displacement data of the joint. The theoretical angle was calculated according to the biped robot’s lower limb diagram model and kinematics equation, and the expected trajectory was obtained by simple gait planning and using MATLAB to verify the validity of the angle parameters. Then the fuzzy PID controller of the lower limb joint was established, and the variable theory domain fuzzy controller was established by introducing the expansion factor. Finally, the Simulink test bench is built to simulate the two controllers, and the expected data and simulation data are compared. The results show that the error range of the variable theory domain fuzzy controller is only ±2°, and the variable theory domain adaptive fuzzy controller can accurately track the desired trajectory, reduce the joint angular displacement error of the biped robot, ensure the precision and stability of the biped robot motion, and basically meet the requirements of practical production.

Joint synergy and muscle activity in the motion of the ankle-foot complex.

The movement of the ankle-foot complex joints is coupled as a result of various physiological and physical constraints. This study introduces a novel approach to the analysis of joint synergies and their physiological basis by focusing on joint rotational directions and the types of muscle contractions. We developed a biomimetic model of the ankle-foot complex with seven degrees of freedom, considering the skeletal configuration and physiological axis directions. Motion capture experiments were conducted with eight participants performing dorsiflexion and plantarflexion in open-chain states, as well as various walking tasks in closed-chain states, across different ground inclinations (±10, ±5, 0deg) and walking speeds (3 and 4 km h-1). Hierarchical cluster analysis identified joint synergy clusters and motion primitives, revealing that in open-chain movements, plantarflexion of the ankle, tarsometatarsal and metatarsophalangeal joints exhibited synergy with the inversion of the remaining joints in the complex; meanwhile, dorsiflexion was aligned with eversion. During closed-chain movements, the synergies grouping was exchanged in the subtalar, talonavicular and metatarsophalangeal joints. Further analysis showed that in open-chain movements, synergy patterns influenced by multi-joint muscles crossing oblique joint axes contribute to foot motion. In closed-chain movements, these changes in synergistic patterns enhance the propulsion of the center of mass towards the contralateral leg and improve foot arch compliance, facilitating human motion. Our work enhances the understanding of the physiological mechanisms underlying synergistic motion within the ankle-foot complex.

Calibrationless monocular vision musculoskeletal simulation during gait

With computer vision technology and prediction of ground reaction forces (GRF), a previous study performed markerless motion capture and musculoskeletal simulation with two smartphones (OpenCap). A recent approach can reconstruct 3D human motion from a single video without calibration and it may further simplify the motion capture process. However it has not been combined with musculoskeletal simulation and the validity is unclear. Therefore, the purpose of this study was to determine the validity of the musculoskeletal simulation using a monocular vision approach. An open-source dataset that contains motion capture and video data during gait from 10 healthy participants was used. Human motion reconstruction with the skinned human (SMPL) model was performed on each video. Virtual marker data was generated by extracting the position data from the SMPL skin vertices. Inverse kinematics, GRF prediction (only for monocular vision approach), inverse dynamics and static optimization were performed using a musculoskeletal model for experimental motion capture data and the generated virtual markers from videos. Mean absolute errors (MAE) between motion capture based and monocular vision based simulation outcomes were calculated. The MAE were 8.4° for joint angles, 5.0 % bodyweight for GRF, 1.1 % bodyweight*height for joint moments and 0.11 for estimated muscle activations from 16 muscles. The entire MAE was larger but some were comparable to OpenCap. Using the monocular vision approach, motion capture and musculoskeletal simulation can be done with no preparations and is beneficial for clinicians to quantify the daily gait assessment.

Design of a spherical parallel mechanism with controllable center of rotation using a spherical reconfiguration linkage

It is well known that Remote Center of the motion (RCM) mechanisms are highly suitable for many medical applications. While it is sometimes preferable that the mechanism can control their Center of Rotation (CoR), most RCM mechanisms do not have this possibility without being mounted on large and heavy translational platforms. By using a new concept of Spherical Reconfigurable Linkage (SRL), the present study offers an alternative solution. These new linkages are designed to control their radius and they are connected to form an RCM mechanism with controllable CoR. The newly mounted mechanism now has two linear Degrees of Freedom (DoF) for the displacement of its CoR in addition to its two angular DoF. The linear kinematic model is derived along with the angular kinematic one while taking into account the reconfiguration of the SRL. The velocity model is calculated and all singular configurations are identified. The mechanism performances in terms of workspace and kinematic performances are is also assessed. Finally, motion capture experiments on a prototype validated the models.

Variable Pivot Gait Based a Novel Dynamics Correction Method for Human Lower Limbs Model.

The rationality of gait analysis directly affects the dynamics of human lower limbs in the sagittal plane, and recent studies on gait stage redivision lack the stage when both feet are not in complete contact with the ground. This paper proposes a novel variable pivot gait, which includes the stage when the heel of one foot and the toe of the other are in contact with the ground and a dynamics correction method based on this gait. First, the relative motion data between the foot and the ground are measured by motion capture experiments, and then a variable pivot gait is proposed in terms of the pivot transformation between the foot and the ground. Second, the dynamics modeling is conducted based on the principle of mechanisms of human lower limbs in each stage of the variable pivot gait. Third, a dynamics correction method is proposed to correct the foot dynamics when the foot is not in complete contact with the ground. The experiment and simulation show that the variable pivot gait is consistent with the actual motion of the foot relative to the ground. The effectiveness of the dynamics correction method is proved by comparing dynamics results (hip, knee, and ankle moments) with previous studies. The variable pivot gait and the dynamics correction method can be applied to the human lower limbs and lower-limb robots, providing a new avenue.

Validation of a musculoskeletal model for simulating muscle mechanics and energetics during diverse human hopping tasks.

Computational musculoskeletal modelling has emerged as an alternative, less-constrained technique to indirect calorimetry for estimating energy expenditure. However, predictions from modelling tools depend on many assumptions around muscle architecture and function and motor control. Therefore, these tools need to continue to be validated if we are to eventually develop subject-specific simulations that can accurately and reliably model rates of energy consumption for any given task. In this study, we used OpenSim software and experimental motion capture data to simulate muscle activations, muscle fascicle dynamics and whole-body metabolic power across mechanically and energetically disparate hopping tasks, and then evaluated these outputs at a group- and individual-level against experimental electromyography, ultrasound and indirect calorimetry data. Comparing simulated and experimental outcomes, we found weak to strong correlations for peak muscle activations, moderate to strong correlations for absolute fascicle shortening and mean shortening velocity, and strong correlations for gross metabolic power. These correlations tended to be stronger on a group-level rather than individual-level. We encourage the community to use our publicly available dataset from SimTK.org to experiment with different musculoskeletal models, muscle models, metabolic cost models, optimal control policies, modelling tools and algorithms, data filtering etc. with subject-specific simulations being a focal goal.

Biomechanical effects of typical lower limb movements of Chen-style Tai Chi on knee joint.

The load and stress distribution on cartilage and meniscus of the knee joint in typical lower limb movements of Chen-style Tai Chi (TC) and deep squat (DS) were analyzed using finite element (FE) analysis. The loadings for this analysis consisted of muscle forces and ground reaction force (GRF), which were calculated through the inverse dynamic approach based on kinematics and force plate measurements obtained from motion capture experiments. Thirteen experienced practitioners performed four typical TC movements, namely, single whip (SW), brush knee and twist step (BKTS), stretch down (SD), and part the wild horse's mane (PWHM), which exhibit lower posture and greater lower limb force compared to other TC styles. The results indicated that TC required greater lower limb muscle strength than DS, resulting in greater knee joint forces. The stress on the medial cartilage in SW and BKTS fell within a range conductive to maintaining the balance between anabolism and catabolism of cartilage matrix. This was due to the fact that SW and BKTS reduce the medial to total tibiofemoral contact force ratios through knee abduction, which may effectively alleviate mild medial knee osteoarthritis (KOA). However, the greater medial contact force ratios observed in SD and PWHM resulted in great contact stresses that may aggravate the pain of patients with KOA. To mitigate these effects, practitioners should consider elevating their postures appropriately to reduce knee flexion angles, especially during the single-leg support phase. This adjustment can decrease the required muscle strength, load and stress on the knee joint.

Mechanically Prestressed Pneumatically Driven Bistable Soft Actuators

Abstract Bistable soft robots are gaining momentum for their fast speed. This study presents a novel asymmetric mechanically prestressed, pneumatically driven, bistable laminated soft actuator. Its two orthogonal stable shapes are created by prestretching two orthogonal elastomer matrix composites before bonding them to a thin core layer. Two fluidic layers with fluid channels are bonded on either side of the core layer to actuate and trigger the snap-through process of the actuator. An analytical model is proposed as follows: the actuator net energy is calculated based on polynomials with unknown coefficients, and the stable shapes of the actuator are computed as a result of pneumatic pressure and external loads with the Rayleigh–Ritz method. Bistable actuators are fabricated with different prestrains, and motion capture and tensile loading experiments are conducted for model validation. A gripper is fabricated with two bistable actuators and demonstrated to grasp a variety of objects. Sensitivity studies are performed to identify the actuator response as a function of a variety of design parameters.

A Probabilistic Model of Human Activity Recognition with Loose Clothing.

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by 67% more than rigid-attached sensors when the window size is 0.5s. Simulated and real human motion capture experiments with several participants confirm the model's predictions, demonstrating that this counterintuitive effect is accurately captured.

Motion Planning for a Cable-Driven Lower Limb Rehabilitation Robot with Movable Distal Anchor Points

This article introduces a cable-driven lower limb rehabilitation robot with movable distal anchor points (M-CDLR). The traditional cable-driven parallel robots (CDPRs) control the moving platform by changing the length of cables, M-CDLR can also adjust the position of the distal anchor point when the moving platform moves. The M-CDLR this article proposed has gait and single-leg training modes, which correspond to the plane and space motion of the moving platform, respectively. After introducing the system structure configuration, the generalized kinematics and dynamics of M-CDLR are established. The fully constrained CDPRs can provide more stable rehabilitation training than the under-constrained one but requires more cables. Therefore, a motion planning method for the movable distal anchor point of M-CDLR is proposed to realize the theoretically fully constrained with fewer cables. Then the expected trajectory of the moving platform is obtained from the motion capture experiment, and the motion planning of M-CDLR under two training modes is simulated. The simulation results verify the effectiveness of the proposed motion planning method. This study serves as a basic theoretical study of the structure optimization and control strategy of M-CDLR.

Communicative constraints affect oro-facial gestures and acoustics: Whispered vs normal speech.

The present paper investigates a relationship between the acoustic signal and oro-facial expressions (gestures) when speakers (i) speak normally or whisper, (ii) do or do not see each other, and (iii) produce questions as opposed to statements. To this end, we conducted a motion capture experiment with 17 native speakers of German. The results provide partial support to the hypothesis that the most intensified oro-facial expressions occur when speakers whisper, do not see each other, and produce questions. The results are interpreted in terms of two hypotheses, i.e., the "hand-in-hand" and "trade-off" hypotheses. The relationship between acoustic properties and gestures does not provide straightforward support for one or the other hypothesis. Depending on the condition, speakers used more pronounced gestures and longer duration compensating for the lack of the fundamental frequency (supporting the trade-off hypothesis), but since the gestures were also enhanced when the listener was invisible, we conclude that they are not produced solely for the needs of the listener (supporting the hand-in-hand hypothesis), but rather they seem to help the speaker to achieve an overarching communicative goal.

A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks.

The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation.

Mathematical Analysis and Motion Capture System Utilization Method for Standardization Evaluation of Tracking Objectivity of 6-DOF Arm Structure for Rehabilitation Training Exercise Therapy Robot.

A treatment method for suppressing shoulder pain by reducing the secretion of neurotransmitters in the brain is being studied in compliance with domestic and international standards. A robot is being developed to assist physical therapists in shoulder rehabilitation exercise treatment. The robot used for rehabilitation therapy enables the training of patients to perform rehabilitation exercises repeatedly. However, the biomechanical movement (or motion) of the shoulder joint should be accurately designed to enhance efficiency using a shoulder rehabilitation robot. Furthermore, safely treating patients by accurately evaluating biomechanical movements in compliance with domestic and international standards is a major task. Therefore, an in-depth analysis of shoulder movement is essential for understanding the mechanism of shoulder rehabilitation using robots. This paper proposes a method for analyzing shoulder movements. The rotation angle and range of motion (ROM) of the shoulder joint are measured by attaching a marker to the body and analyzing the inverse kinematics. The first motion is abduction and adduction, and the second is external and internal rotation. The location information of the marker is transmitted to an application software through an infrared camera. For the analysis using an inverse kinematics solution, five males and five females participated in the motion capture experiment. The subjects did not have any disability, and abduction and adduction were repeated 10 times. As a result, ROM of the abduction and adduction were 148° with males and 138.7° in females. Moreover, ROM of the external and internal rotation were 111.2° with males and 106° in females. Because this study enables tracking of the center coordinates of the joint suitably through a motion capture system, inverse kinematics can be accurately calculated. Additionally, a mathematical inverse kinematics equation will utilize follow-up study for designing an upper rehabilitations robot. The proposed method is assessed to be able to contribute to the definition of domestic and international standardization of rehabilitation robots and motion capture for objective evaluation.

Methods of Estimating Foot Power and Work in Standing Vertical Jump.

Experimental motion capture studies have commonly considered the foot as a single rigid body even though the foot contains 26 bones and 30 joints. Various methods have been applied to study rigid body deviations of the foot. This study compared 3 methods: distal foot power (DFP), foot power imbalance (FPI), and a 2-segment foot model to study foot power and work in the takeoff phase of standing vertical jumps. Six physically active participants each performed 6 standing vertical jumps from a starting position spanning 2 adjacent force platforms to allow ground reaction forces acting on the foot to be divided at the metatarsophalangeal (MTP) joints. Shortly after movement initiation, DFP showed a power absorption phase followed by a power generation phase. FPI followed a similar pattern with smaller power absorption and a larger power generation compared to DFP. MTP joints primarily generated power in the 2-segment model. The net foot work was -4.0 (1.0)J using DFP, 1.8 (1.1)J using FPI, and 5.1 (0.5)J with MTP. The results suggest that MTP joints are only 1 source of foot power and that differences between DFP and FPI should be further explored in jumping and other movements.

The role of the anterior temporal cortex in action: evidence from fMRI multivariate searchlight analysis during real object grasping

Intelligent manipulation of handheld tools marks a major discontinuity between humans and our closest ancestors. Here we identified neural representations about how tools are typically manipulated within left anterior temporal cortex, by shifting a searchlight classifier through whole-brain real action fMRI data when participants grasped 3D-printed tools in ways considered typical for use (i.e., by their handle). These neural representations were automatically evocated as task performance did not require semantic processing. In fact, findings from a behavioural motion-capture experiment confirmed that actions with tools (relative to non-tool) incurred additional processing costs, as would be suspected if semantic areas are being automatically engaged. These results substantiate theories of semantic cognition that claim the anterior temporal cortex combines sensorimotor and semantic content for advanced behaviours like tool manipulation.

A Hybrid Mechanism-Based Robot for End-Traction Lower Limb Rehabilitation: Design, Analysis and Experimental Evaluation

Conventional lower-limb rehabilitation robots cannot provide in-time rehabilitation training for stroke patients in the acute stage due to their large size and mass as well as their complex wearing process. Aiming to solve the problems, first, a novel hybrid end-traction lower-limb rehabilitation robot (HE-LRR) was designed as the lower-limb rehabilitation requirement of patients in the acute stage, in this paper. The usage of (2-UPS + U)&(R + RPS)&(2-RR) hybrid mechanism and a mirror motion actuator had the advantages of compact structure, large working space and short wearing time to the HE-LRR. Then, the mobility of the HE-LRR was calculated and the motion property was analyzed based on screw theory. Meanwhile, the trajectory planning of the HE-LRR was carried out based on MOTOmed® motion training. Finally, the motion capture and surface electromyography (sEMG) signal acquisition experiments in the MOTOmed motion training were performed. The foot trajectory experimental effect and the lower-limb muscle groups activation rules were studied ulteriorly. The experimental results showed that the HE-LRR achieved good kinematic accuracy and lower limb muscle groups training effect, illustrating that the HE-LRR possessed good application prospects for the lower-limb rehabilitation of patients in the acute stage. This research could also provide a theoretical basis for improving the standardization and compliance of lower-limb robot rehabilitation training.

Kinematics analysis and gait planning for a hemiplegic exoskeleton robot

Background: As the world's aging population increases, the number of hemiplegic patients is increasing year by year. At present, in many countries with low medical level, there are not enough rehabilitation specialists. Due to the different condition of patients, the current rehabilitation training system cannot be applied to all patients. so that patients with hemiplegia cannot get effective rehabilitation training. Methods: Through a motion capture experiment, the mechanical design of the hip joint, knee joint and ankle joint was rationally optimized based on the movement data. Through the kinematic analysis of each joint of the hemiplegic exoskeleton robot, the kinematic relationship of each joint mechanism was obtained, and the kinematics analysis of the exoskeleton robot was performed using the Denavit-Hartenberg (D-H) method. The kinematics simulation of the robot was carried out in automatic dynamic analysis of mechanical systems (ADAMS), and the theoretical calculation results were compared with the simulation results to verify the correctness of the kinematics relationship. According to the exoskeleton kinematics model, a mirror teaching method of gait planning was proposed, allowing the affected leg to imitate the movement of the healthy leg with the help of an exoskeleton robot. Conclusions: A new hemiplegic exoskeleton robot designed by Shenzhen Institute of Advanced Technology (SIAT-H) is proposed, which is lightweight, modular and anthropomorphic. The kinematics of the robot have been analyzed, and a mirror training gait is proposed to enable the patient to form a natural walking posture. Finally, the wearable walking experiment further proves the feasibility of the structure and gait planning of the hemiplegic exoskeleton robot.

Development of the active spatial rolling contact pair to generate the specified trajectory

Linkage mechanisms with 1 DOF composed of only lower pairs cannot generate the specified motion completely because of severe kinematic limitation of lower pairs. In order to relax the kinematic limitation and to generate the specified spatial trajectory completely, the authors have proposed the spatial rolling contact pair (SRCP), where two links in contact at a line roll relatively with generating the specified relative spatial trajectory completely, as a novel kinematic pair used for a spatial-path generator with 1 DOF. However, since it is a passive kinematic pair, it must be used in a closed-loop mechanism. Thus, the spatial-path generator with the SRCP cannot be simplified enough. In this paper, the ”active” spatial rolling contact pair (ASRCP), which is driven by several active elastic elements such as flexible linear actuators, is developed to achieve a simple spatial-path generator. Firstly, a design method to optimally arrange active elastic elements between two links of the SRCP based on a transmission index is proposed. Here, a method to use more than the minimum number of active elastic elements which are required to constitute force-closure state is described to design the ASRCP in order to achieve sufficient motion range. Besides, a control method of the ASRCP to generate the ideal rolling motion is proposed, where actuation forces of active elastic elements to get high stability between the links are calculated with the use of the actuation redundancy. As examples, the ASRCP driven with reeled-wires and the ASRCP driven with fluid-driven artificial muscles are designed and prototyped. Then, they are controlled with the proposed method, and their performances are investigated through motion capture experiments.

Cartesian Control of Sit-to-Stand Motion Using Head Position Feedback.

Sit-to-stand (STS) motion is an indicator of an individual's physical independence and well-being. Determination of various variables that contribute to the execution and control of STS motion is an active area of research. In this study, we evaluate the clinical hypothesis that besides numerous other factors, the central nervous system (CNS) controls STS motion by tracking a prelearned head position trajectory. Motivated by the evidence for a task-oriented encoding of motion by the CNS, we adopt a robotic approach for the synthesis of STS motion and propose this scheme as a solution to this hypothesis. We propose an analytical biomechanical human CNS modeling framework where the head position trajectory defines the high-level task control variable. The motion control is divided into low-level task generation and motor execution phases. We model CNS as STS controller and its Estimator subsystem plans joint trajectories to perform the low-level task. The motor execution is done through the Cartesian controller subsystem that generates torque commands to the joints. We do extensive motion and force capture experiments on human subjects to validate our analytical modeling scheme. We first scale our biomechanical model to match the anthropometry of the subjects. We do dynamic motion reconstruction through the control of simulated custom human CNS models to follow the captured head position trajectories in real time. We perform kinematic and kinetic analyses and comparison of experimental and simulated motions. For head position trajectories, root mean square (RMS) errors are 0.0118 m in horizontal and 0.0315 m in vertical directions. Errors in angle estimates are 0.55 rad, 0.93 rad, 0.59 rad, and 0.0442 rad for ankle, knee, hip, and head orientation, respectively. RMS error of ground reaction force (GRF) is 50.26 N, and the correlation between ground reaction torque and the support moment is 0.72. Low errors in our results validate (1) the reliability of motion/force capture methods and anthropometric technique for customization of human models and (2) high-level task control framework and human CNS modeling as a solution to the hypothesis. Accurate modeling and detailed understanding of human motion can have significant scope in the fields of rehabilitation, humanoid robotics, and virtual characters' motion planning based on high-level task control schemes.

Neuro-fuzzy control of sit-to-stand motion using head position tracking

Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.