Inertial Motion Capture Research Articles

Use of Field-based Motion Capture to Augment Observational data in ergonomic Assessment of Aircraft Maintenance

A dramatically increasing trend in the population of aircraft painters with permanent physical limitations was identified among participants in the return-to-work program at a large aircraft maintenance facility. The increase came primarily from painters who had used a full-depth mechanical sanding depainting process. A detailed ergonomic assessment of the process was begun to elicit details of the range and types of risks faced by painters in that shop, and determine what types of mitigation might be utilized to minimize the prevalence of injury. Shoulder injuries represent the biggest area of ergonomic risk for painters. Cervical problems, hand/wrist problems, and lumbar problems make up the majority of the remainder of injuries. The extent, duration, and complexity of the risks involved in the depaint process required more data than the typical observational and psychophysical assessment tools could provide. Inertial motion capture and tool force instrumentation was implemented in combination with the typical ergonomic methods to provide more details as to the biomechanical sources of risk and how this varies throughout the depainting process. The results indicate surprisingly high normal forces on the palm sander, with rapid muscle fatigue and frequent postural substitution evident during overhead sanding. The long duration of this overhead activity, in combination with the evidence of fatigue, implicates the development of risky scapulo-humeral kinematics and end-of-range glenohumeral motion in the etiology of shoulder and cervical injuries. A mix of management and engineering strategies for mitigation resulted from the analysis.

A procedure for a mechanical evaluation of an undefined osteo-protective material

Falls represent a major care and cost problem to health and social services world-widely, since 50% of falls result in an injury. In this work, is proposed a methodology to evaluate protective pads materials and geometry performance, in order to reduce impact results in a fall event. Since the material properties and the pad geometry are the key factors to make the protection possible when a fall event occurs, our approach relies on the use of mechanical tests to evaluate the properties of the material and in the study of the pad response during a fall. For this, were used compression, tensile and instrumented falling weight tests, that allow a fully characterization of the materials that can be employed in the protective pads. Likewise, to gather precise information on falls events, in order to study the pad response during a fall, a set of laboratory fall trials were created using a camera-less inertial motion capture (mocap) system. This allow the acquisition of dynamic information of falls, namely acceleration and velocity that can be used to create a finite element analysis (FEA) model, where different segments from the human body can be evaluated when the protective pad is associated to it. Through the proposed methodology, different materials and pad geometries can be studied towards maximizing the performance of protection pads for falls. The mocap system allows the acquisition of fall data, and also the creation of a human body geometrical model, representative of the fall. From the mechanical trials, was showed that the spacer fabric embedded with silicone has the higher ability to reduce the peak force in case of impact when compared with all the other specimens. The compression and the tensile tests allow the mechanical definition of the material, and with this the material definition on the FEA model.

The Analysis of Multi-Rotor Attitude Simulator Based on Matlab

There exist overfrequency issues of airframe vibration, instable fight conditions, inefficient airframe survivability and undeveloped cockpit’s eject-ability insides multi-rotor aircraft flights. This paper focuses on four-rotor aircraft researches via gathering inherent rotor attitudes with the inertial motion capture device. Based-on the resulting data analyses, the relations between flight attitudes and rotor performances are clearly figured out. This research also advances multi-rotor aircraft works, such as: drawing frameworks, evaluating simulations, designing control systems, and optimizing safety and stability properties.

Markerless motion capture using appearance and inertial data.

Current monitoring techniques for biomechanical analysis typically capture a snapshot of the state of the subject due to challenges associated with long-term monitoring. Continuous long-term capture of biomechanics can be used to assess performance in the workplace and rehabilitation at home. Noninvasive motion capture using small low-power wearable sensors and camera systems have been explored, however, drift and occlusions have limited their ability to reliably capture motion over long durations. In this paper, we propose to combine 3D pose estimation from inertial motion capture with 2D pose estimation from vision to obtain more robust posture tracking. To handle the changing appearance of the human body due to pose variations and illumination changes, our implementation is based upon Least Soft-Threshold Squares Tracking. Constraints on the variation of the appearance model and estimated pose from an inertial motion capture system are used to correct 2D and 3D estimates simultaneously. We evaluate the performance of our method with three state-of-the-art trackers, Incremental Visual Tracking, Multiple Instance Learning, and Least Soft-Threshold Squares Tracking. In our experiments, we track the movement of the upper limbs. While the results indicate an improvement in tracking accuracy at some joint locations, they also show that the result can be further improved. Conclusions and further work required to improve our results are discussed.

Comparison Between Inertial Sensors and Motion Capture System to Quantify Flexion‐Extension Motion in the Back of a Horse

IntroductionThe development of miniaturized inertial sensor technology (IMU) opens new possibilities to assess equine back motion in real‐life situations. The consistency of IMU for measuring dorsoventral displacement of the back has been assessed but orientation has not. The aim of this study was to assess accuracy and consistency of miniaturized inertial sensors for quantification of flexion‐extension of the equine back, compared to motion capture (mocap) as a gold standard.MethodsOne sound horse was trotted (3.5 m/s) and cantered (9 m/s) on a treadmill with synchronized mocap and IMU data collection. The amplitudes of two flexion‐extension angles were calculated between 3 IMUs (T12‐T16‐L2) and with a triad of markers linked to each IMU during 15 consecutive strides for two recordings at each gait. Consistency of sensor measurements was assessed using Lin correlation coefficient.ResultsThoracic and thoracolumbar angles showed respectively a Lin correlation coefficient of 0.96 (excellent) and 0.87 (good). The difference (mean ± SD) between IMU and mocap were, respectively for the thoracic and thoracolumbar angles, 0.57 ± 0.44 and 0.65 ± 0.47 degrees during trotting, and 0.83 ± 0.67 and 0.63 ± 0.44 degrees during canter.ConclusionsAngle values calculated with IMU data showed acceptable accuracy consistency for quantification of flexion‐extension movement in a horse's back. These results are encouraging for future development of the method in real‐life situations where mocap systems are unsuited for the quantification of back movements.Ethical Animal ResearchNone declared. Sources of funding: The authors thank the Institut Français du Cheval et de l′Equitation, the Association Nationale de la Recherche et de la Technologie and the Conseil Régional d′Aquitaine for financial support, the Pôle Hippolia for logistical support, and CIRALE for loaning the treadmill and the horse. Competing interests: none.

An evaluation of classification algorithms for manual material handling tasks based on data obtained using wearable technologies

With recent progress in wearable measurement systems, physical exposures can be feasibly assessed at high precision in the workplace. Such systems, however, generally lack contextual information for a given job (e.g. task type, duration). To extract such information, we explored three classification algorithms to classify manual material handling (MMH) tasks during a simulated job in a laboratory, using several combinations of outputs from commercially available inertial motion capture and in-shoe pressure measurement systems. A total of 10 participants completed three replications of four cycles of a simulated job. Precision and recall values of ≥ ∼90% and 80%, respectively, and errors in estimated task duration of < ∼14%, could be achieved across the MMH task examined. Classification performance, however, varied between classification algorithms, input data sets and task types. Overall, combining wearable technology with task classification could be an effective approach for field-based exposure assessment, though field-testing is needed to demonstrate the applicability of this method.Practitioner Summary: Combining wearable technologies with task classification was explored to extract exposure context, specifically task type and duration. Results supported that task classification can facilitate the use of wearable technologies in field-based exposure assessment, specifically by aiding in task identification from within the rather large data sets obtained from these technologies.

A preliminary investigation of the use of inertial sensing technology for the measurement of hip rotation asymmetry in horse riders

This study investigated the use of inertial sensing technology as an indicator of asymmetry in horse riders, evidenced by discrepancies in the angle of external rotation of the hip joint. Twelve horse and rider combinations were assessed with the rider wearing the XsensTM MVN inertial motion capture suit. Asymmetry (left vs right) was revealed in mean hip external rotation of all riders, with values ranging from 1° to 27°, and 83% showed greater external rotation of the right hip. This study represents novel use of inertial sensing equipment in its application to the measurement of rider motion patterns. The technique is non-invasive, is capable of recording rider hip rotation asymmetry whilst performing a range of movements unhindered and was found to be efficient and practical, with potential to further advance the analysis of horse and rider interactions.

An optimization-based approach to human body motion capture using inertial sensors

In inertial human motion capture, a multitude of body segments are equipped with inertial measurement units, consisting of 3D accelerometers, 3D gyroscopes and 3D magnetometers. Relative position and orientation estimates can be obtained using the inertial data together with a biomechanical model. In this work we present an optimization-based solution to magnetometer-free inertial motion capture. It allows for natural inclusion of biomechanical constraints, for handling of nonlinearities and for using all data in obtaining an estimate. As a proof-of-concept we apply our algorithm to a lower body configuration, illustrating that the estimates are drift-free and match the joint angles from an optical reference system.

2A1-R03 光学式/慣性式モーションキャプチャを併用したハンド/アームロボットの遠隔操作(人間機械協調)

Teleoperation of the robot arm is achieved by capturing the motion of the operator's arm with optical motion capture. However, tracking markers disappear on the way motion capture, optical motion capture can't get operator's posture continuously. So we propose teleoperation system for robot arm by using optical and inertial motion capture. By using this system, we can teleoperate the robot arm continuously.

The adaptive Kalman filter based on fuzzy logic for inertial motion capture system

In the inertial motion capture system, the model complexity and the large amount of computation make the completion of the orientation estimation algorithm rely solely on PC. Because the data processing speed is slow, it is difficult to realize high-speed motion tracking in the embedded system. In order to further expand the application of the motion tracking technology, this paper introduces a two-step Kalman filter, which is suitable for the embedded system. The filter is composed of two sub filters, and is adaptively adjusted based on the variance matching of fuzzy logic. IMU orientation is calculated based on the filtered acceleration vector and the estimated yaw. This approach simplifies the mathematical model, reduces the matrix operations and improves the speed of computation.

Feasibility of Using Virtual and Body Worn Inertial Sensors to Detect Whole-body Decelerations during Stopping

The aim of this study was to explore the use of inertial sensors for determining the rate of deceleration of an athlete during controlled breaking during over-ground running. This study will investigate the application of inertial sensors in identifying kinematic parameters to examine the effects of shock attenuation through the body. This study consisted of participants performing a number of sub-maximal runs and stopping within given distances (6m and 3m) to control the rate of deceleration. Kinematic data was recorded using a combination of motion capture and custom build inertial sensors attached to the participants’ distal fibulas and worn in a commercially available sports vest. From the data, tibia accelerations increased by 32% while peak upper torso accelerations decreased by 0.3% resulting in an overall increase in shock attenuation of 21%. This increase in attenuation was predominantly due to the ankle to knee attenuating 39% more shock, while the knee to sacrum and sacrum to upper torso both decreased by 6% and 2% respectively. As both the inertial sensor acceleration and motion capture derived acceleration displayed similar trends in shock absorption, it is difficult to detect variations in the rate of stopping using only peak impact parameters from an inertial sensor unit located on the upper torso. This is due to the varying shock attenuation capabilities of the lower joints in particular the knee which absorbed the majority of the shock under both stopping conditions.

Automatic identification of inertial sensor placement on human body segments during walking

BackgroundCurrent inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically.MethodsWalking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis).Results and conclusionsAfter testing the algorithm with 10-fold cross-validation using 31 walking trials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for a lower body plus trunk configuration (8 inertial sensors) was trained and tested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree was also tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligament reconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of the method.

Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks

With a long-term goal of improving quantification of physical exposures in the workplace, this study examined the ability of a commercially available inertial motion capture (IMC) system in quantifying exposures during five different simulated manual material handling tasks. Fourteen participants repeated all these tasks in three 20 min sequential time blocks. Performance of the IMC system was compared against an optical motion capture (OMC) system (‘gold standard’) in terms of joint angles, angular velocities and moments at selected body parts. Though several significant changes in performance over time were found, the magnitudes of these were relatively small and may have limited practical relevance. The IMC system yielded peak kinematic values that differed by up to 28% from the OMC system. The IMC system, in some cases, incorrectly reflected the actual extremity positions of a participant, and which can cause relatively large errors in joint moment estimation. Given the potential limitations, practical recommendations are offered and discussed. Practitioner Summary: Use of an inertial motion capture system can advance the quantification of physical exposures in situ. Results indicate a good potential capacity for capturing physical exposure data in the field for an extended period, while highlighting potential limitations. Future system application can help provide better understandings of dose-exposure relationships.

Human velocity and dynamic behavior tracking method for inertial capture system

This paper introduces a method to track the real-time velocity and capture the dynamic behavior of a person using inertial motion capture system. The velocity of a person while moving is curtail, but existing capture systems do not discuss on this. In this work, a method for monitoring the velocity of a key reference root point on the subject is first proposed. Based on the contact detection, the velocity calculated from lower limb kinematics and the integration of accelerations are fused through Kalman Filters to achieve accurate and drift free estimation results. Based on this velocity estimation and the limb kinematics, the spatial location of the subject can then be updated in real-time. Experiments are conducted to show the capability of the proposed method. The capability and accuracy of the proposed method in both the velocity tracking and localization are verified using the commercial optical motion capture system, Motion Analysis. The velocity and spatial behavior of the person during motions like walking, jumping and running are accurately captured with this self-contained system. This system does not depend on external devices, has little constraint to the human movements and can accurately monitor the moving speed of the person, thus it can show advantage in everyday practice and sport training in large area or home environments.

A novel wearable measurement system for ambulatory assessment of joint loading in the occupational setting

It is know that biomechanical overexposure of the joints is an important cause of occupational injuries. This paper presents a novel wearable measurement system for automated assessment of joint loading in the occupational setting. The wearable measurement system consists of a full body inertial sensor motion capture system which can be worn under the clothes and shoes instrumented with 3D force sensors (ForceShoes). Promising results have been found for the performance of the inertial sensor system and the ForceShoe, separately. Validation experiments are in preparation in which the performance of the combined measurements system will be tested in the laboratory by comparing the assessed joint loading to the joint loading assessed by a conventional state-of-the-art lab-based method.

A Multi-Sensorial Hybrid Control for Robotic Manipulation in Human-Robot Workspaces

Autonomous manipulation in semi-structured environments where human operators can interact is an increasingly common task in robotic applications. This paper describes an intelligent multi-sensorial approach that solves this issue by providing a multi-robotic platform with a high degree of autonomy and the capability to perform complex tasks. The proposed sensorial system is composed of a hybrid visual servo control to efficiently guide the robot towards the object to be manipulated, an inertial motion capture system and an indoor localization system to avoid possible collisions between human operators and robots working in the same workspace, and a tactile sensor algorithm to correctly manipulate the object. The proposed controller employs the whole multi-sensorial system and combines the measurements of each one of the used sensors during two different phases considered in the robot task: a first phase where the robot approaches the object to be grasped, and a second phase of manipulation of the object. In both phases, the unexpected presence of humans is taken into account. This paper also presents the successful results obtained in several experimental setups which verify the validity of the proposed approach.

Determining the Feasibility of Measuring Outdoor Road Cycling Kinematics Using Inertial Motion Capture Technology

Unlike traditional optical systems, inertial motion capture systems (IMSs) can measure human kinematics outdoors as well as in a laboratory. However, these systems are sensitive to magnetic interference. This study evaluated an IMS for use in sports performance analysis, using road cycling as a case study. The objective was to establish the feasibility of obtaining accurate outdoor kinematic data on competition-level road bicycles. Ten male cyclists were recorded on their own bicycle on a stretch of road wearing the IMS. Results revealed unacceptable magnetic interference to the IMS near the pedal and handlebar interfaces. Therefore, accurate full-body cycling kinematics is not currently feasible on most competition-level road bicycles. However, lower limb flexion measurements are possible using the IMS's kinematic coupling algorithm which obtained RMS errors of less than 3.5° for all joints in a benchmark test with an optical system regarded as gold-standard.

Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts

Clinical gait analysis has proven to reduce uncertainties in selecting the appropriate quantity and type of treatment for patients with neuromuscular disorders. However, gait analysis as a clinical tool is under-utilised due to the limitations and cost of acquiring and managing data. To overcome these obstacles, inertial motion capture (IMC) recently emerged to counter the limitations attributed to other methods. This paper investigates the use of IMC for training and testing a back-propagation artificial neural network (ANN) for the purpose of distinguishing between hemiparetic stroke and able-bodied ambulation. Routine gait analysis was performed on 30 able-bodied control subjects and 28 hemiparetic stroke patients using an IMC system. An ANN was optimised to classify the two groups, achieving a repeatable network accuracy of 99.4%. It is concluded that an IMC system and appropriate computer methods may be useful for the planning and monitoring of gait rehabilitation therapy of stroke victims.

Human motion capture sensors and analysis in robotics

PurposeThe purpose of this paper is to provide a review of various motion capture technologies and discuss the methods for handling the captured data in applications related to robotics.Design/methodology/approachThe approach taken in the paper is to compare the features and limitations of motion trackers in common use. After introducing the technology, a summary is given of robotic‐related work undertaken with the sensors and the strengths of different approaches in handling the data are discussed. Each comparison is presented in a table. Results from the author's experimentation with an inertial motion capture system are discussed based on clustering and segmentation techniques.FindingsThe trend in methodology is towards stochastic machine learning techniques such as hidden Markov model or Gaussian mixture model, their extensions in hierarchical forms and non‐linear dimension reduction. The resulting empirical models tend to handle uncertainty well and are suitable for incrementally updating models. The challenges in human‐robot interaction today include expanding upon generalising motions to understand motion planning and decisions and build ultimately context aware systems.Originality/valueReviews including descriptions of motion trackers and recent methodologies used in analyzing the data they capture are not very common. Some exist, as has been pointed out in the paper, but this review concentrates more on applications in the robotics field. There is value in regularly surveying the research areas considered in this paper due to the rapid progress in sensors and especially data modeling.

Design and validation of surface-marker clusters for the quantification of joint rotations in general movements in early infancy

Lack of complexity in general movements in early infancy is an important marker of potential motor disorders of neurological origin, such as cerebral palsy. Quantitative approaches to characterising this complexity are hampered by experimental difficulties in recording from infants in their first few months of life. The aim of this study was to design and validate bespoke surface-marker clusters to facilitate data acquisition and enable full quantification of joint rotations. The clusters were validated by recording the controlled movements of a soft-body dummy doll simultaneously with an optical (Qualisys) and inertial (XSens) motion capture system. The angles estimated from the optical system were compared with those measured by the inertial system. We demonstrate that the surface-marker based approach compares well with the use of an inertial system to obtain “direct” readings of the rotations whilst alleviating the issues associated with the use of an optical motion capture system. We briefly report use of this technique in 1–5 month old infants. By enabling full quantification of joint rotation, use of the custom made markers could pave the way for early diagnosis of movement disorders.