Wearable Motion Capture System Research Articles

Analysis of Gait Kinematics in Smart Walker-Assisted Locomotion in Immersive Virtual Reality Scenario.

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.

A Rapid Review on the Effectiveness and Use of Wearable Biofeedback Motion Capture Systems in Ergonomics to Mitigate Adverse Postures and Movements of the Upper Body.

Work-related diseases and disorders remain a significant global health concern, necessitating multifaceted measures for mitigation. One potential measure is work technique training utilizing augmented feedback through wearable motion capture systems. However, there exists a research gap regarding its current effectiveness in both real work environments and controlled settings, as well as its ability to reduce postural exposure and retention effects over short, medium, and long durations. A rapid review was conducted, utilizing two databases and three previous literature reviews to identify relevant studies published within the last twenty years, including recent literature up to the end of 2023. Sixteen studies met the inclusion criteria, of which 14 were of high or moderate quality. These studies were summarized descriptively, and the strength of evidence was assessed. Among the included studies, six were rated as high quality, while eight were considered moderate quality. Notably, the reporting of participation rates, blinding of assessors, and a-priori power calculations were infrequently performed. Four studies were conducted in real work environments, while ten were conducted in controlled settings. Vibration feedback was the most common feedback type utilized (n = 9), followed by auditory (n = 7) and visual feedback (n = 1). All studies employed corrective feedback initiated by the system. In controlled environments, evidence regarding the effectiveness of augmented feedback from wearable motion capture systems to reduce postural exposure ranged from strong evidence to no evidence, depending on the time elapsed after feedback administration. Conversely, for studies conducted in real work environments, the evidence ranged from very limited evidence to no evidence. Future reach needs are identified and discussed.

Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs.

Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system's adaptability shows promise for applications in medical rehabilitation and sports.

American Sign Language Recognition and Translation Using Perception Neuron Wearable Inertial Motion Capture System.

Sign language is designed as a natural communication method to convey messages among the deaf community. In the study of sign language recognition through wearable sensors, the data sources are limited, and the data acquisition process is complex. This research aims to collect an American sign language dataset with a wearable inertial motion capture system and realize the recognition and end-to-end translation of sign language sentences with deep learning models. In this work, a dataset consisting of 300 commonly used sentences is gathered from 3 volunteers. In the design of the recognition network, the model mainly consists of three layers: convolutional neural network, bi-directional long short-term memory, and connectionist temporal classification. The model achieves accuracy rates of 99.07% in word-level evaluation and 97.34% in sentence-level evaluation. In the design of the translation network, the encoder-decoder structured model is mainly based on long short-term memory with global attention. The word error rate of end-to-end translation is 16.63%. The proposed method has the potential to recognize more sign language sentences with reliable inertial data from the device.

Real-time tracking of the effect of jumping rope exercise using a wearable device

Abstract With the development of science and technology, wearable devices, as an emerging field, have been gradually integrated into our daily lives and are widely used in the tracking of movement effects. In this paper, the data fusion algorithm combining complementary filtering and extended Kalman filtering and the human posture solving algorithm based on the D-H method is selected to solve the designed human jumping rope motion joint model, which realizes the construction of a wearable jumping rope motion capture system. Furthermore, the effect and commercial value of the wearable device designed in this paper for real-time tracking of jumping rope movement are tested by a single node posture test and a comparison experiment with posture solving. The experimental results show that the static test error and dynamic test accuracy of the sensor are 1.4° and 4°, respectively, which indicate that the sensor can accurately recognize the trajectory of jumping rope movements. The average values of RMSE for pitch angle, roll angle, and yaw angle were 0.37, 0.69, and 1.40, respectively. This indicates that the wearable device and the pose-solving algorithm used in this paper can meet the standard for commercial applications. This study provides a new approach to studying sports, which has rarely been done in the field of smart sports.

Reliability Enhancement Method of Attitude Estimation for Wearable Motion-Capture Systems in Human Lower Limb Rehabilitation

Reliability Enhancement Method of Attitude Estimation for Wearable Motion-Capture Systems in Human Lower Limb Rehabilitation

Automatic video analysis and classification of sleep-related hypermotor seizures and disorders of arousal.

Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy with seizures occurring mostly during sleep. SHE seizures present different motor characteristics ranging from dystonic posturing to hyperkinetic motor patterns, sometimes associated with affective symptoms and complex behaviors. Disorders of arousal (DOA) are sleep disorders with paroxysmal episodes that may present analogies with SHE seizures. Accurate interpretation of the different SHE patterns and their differentiation from DOA manifestations can be difficult and expensive, and can require highly skilled personnel not always available. Furthermore, it is operator dependent. Common techniques for human motion analysis, such as wearable sensors (e.g., accelerometers) and motion capture systems, have been considered to overcome these problems. Unfortunately, these systems are cumbersome and they require trained personnel for marker and sensor positioning, limiting their use in the epilepsy domain. To overcome these problems, recently significant effort has been spent in studying automatic methods based on video analysis for the characterization of human motion. Systems based on computer vision and deep learning have been exploited in many fields, but epilepsy has received limited attention. In this paper, we present a pipeline composed of a set of three-dimensional convolutional neural networks that, starting from video recordings, reached an overall accuracy of 80% in the classification of different SHE semiology patterns and DOA. The preliminary results obtained in this study highlight that our deep learning pipeline could be used by physicians as a tool to support them in the differential diagnosis of the different patterns of SHE and DOA, and encourage further investigation.

Evaluation of a Vibrotactile Biofeedback System Targeting Stance Time Symmetry Ratio of Individuals With Lower-Limb Amputation: A Pilot Study

Individuals with lower-limb amputation (LLA) often exhibit atypical gait patterns and asymmetries. These patterns can be corrected using biofeedback (BFB). Real-time BFB strategies have demonstrated to be effective to various degrees in BFB systems. However, no studies have evaluated the use of corrective vibrotactile BFB strategies to improve temporal gait symmetry of LLA. The aim of this study was to evaluate a wearable vibrotactile BFB system to improve stance time symmetry ratio (STSR) of LLA, and compare two corrective BFB strategies that activate either one or two vibrating motors at two different frequency and amplitude levels, based on a pre-set STSR target. Gait patterns of five unilateral LLA were assessed with and without BFB. Spatiotemporal and kinematic gait parameters were measured and assessed using a wearable motion capture system. Usability and workload were assessed using the System Usability Scale and NASA Task Load Index questionnaires, respectively. Results showed that participants significantly (p<0.001) improved STSR with BFB; however, this coincided with a reduction in gait speed and cadence compared to walking without feedback. Knee and hip flexion angles improved and changes in other parameters were variable. Immediate post-test retention effects were observed, suggesting that gait changes due to BFB were preserved for at least a short-time after feedback was withdrawn. System usability was found to be acceptable while using BFB. The outcomes of this study provide new insights into the development and implementation of clinically practical and viable BFB systems. Future work should focus on assessing the long-term use and retention effects of BFB outside controlled-laboratory conditions.

A simplified model for whole-body angular momentum calculation.

The capability to monitor gait stability during everyday life could provide key information to guide clinical intervention to patients with lower limb disabilities. Whole body angular momentum (Lbody) is a convenient stability indicator for wearable motion capture systems. However, Lbody is costly to estimate, because it requires monitoring all major body segment using expensive sensor elements. In this study, we developed a simplified rigid body model by merging connected body segments to reduce the number of body segments, which need to be monitored. We demonstrated that the Lbody could be estimated by a seven-segment model accurately for both people with and without lower extremity amputation.

Synchronization of wearable motion capture and EMG measurement systems.

Synchronization of motion capture systems with other modalities in out-of-the-lab settings is not trivial. Various synchronization methods exist, such as using servers or transistor-transistor-logic pulses. However, not all measurement set-ups allow for such synchronization methods. Therefore, we have developed and validated an acceleration based post-measurement method to synchronize an IMU based motion capture system and an EMG measurement device. On top of the thigh IMU an additional accelerometer was placed which was connected to the analog input of the EMG device. By applying cross-correlation continuously, the similarities in the measured acceleration by the two measurement systems can be used for synchronization. We performed a validation measurement on seven able-bodied subjects and tested various correlation window sizes in hour long measurements in an out of the lab setting. It can be concluded that the developed method works for different activities when a suitable window length is chosen for cross-correlation. If no other options are available for synchronization, this correlation based method using an additional accelerometer is a viable option.

Design of a Professional Sports Competition Adjudication System Based on Data Analysis and Action Recognition Algorithm

This paper uses data analysis and action recognition algorithms to conduct in-depth research and analysis of professional sports competition judging and designs a professional sports competition-assisted judging system for use in actual judging. In this paper, a wearable motion capture system based on an inertial sensing unit is developed and designed for kayaking technical motion monitoring to achieve the acquisition, analysis, and quantitative evaluation of kayaker motion data. To limit the gyroscope and pose estimation error, a gradient descent method is used for multisensor data fusion to achieve athlete pose update, and a quaternion-driven human skeletal vector model is proposed to reconstruct the kayaker’s paddling technical movements. By calculating the angular sequences of the left shoulder, right shoulder, left elbow, and right elbow joints of the athlete’s upper limbs and comparing them with the optical motion capture system, the results show that the motion capture system developed in this paper is comparable to the optical motion capture system in terms of measurement accuracy. It ultimately affects the result of pose estimation. Therefore, high-resolution networks and low-resolution networks can continuously maintain high-resolution features of the image by allowing each representation layer to repeatedly accept the representation information of other networks. A step matrix model is constructed to encode the multiscale global temporal information of action sequences, and action classification is achieved by calculating the response of the step matrix of test samples to the step matrix of each category of actions. The algorithm achieves 78.96%, 91.84%, and 91.18% accuracy of action classification on the Northwestern-UCLA database, MSRC-12 database, and CAD-60 database, respectively. The designed visual motion tracking system was applied to record the motion data of the experimental subjects in the fine motion assessment task and construct the motion assessment database. The experimental results show that the average error between the prediction results of the proposed action assessment method and the manual scoring is 1.83, and the automated assessment of fine movements is effectively realized.

Simultaneous validation of wearable motion capture system for lower body applications: over single plane range of motion (ROM) and gait activities.

Extracting data from {Zhu, 2019 #5} daily life activities is important in biomechanical applications to define exact boundary conditions for the intended use-based applications. Although optoelectronic camera-marker based systems are used as gold standard tools for medical applications, due to line-of-sight problem, there is a need for wearable, affordable motion capture (MOCAP) systems. We investigate the potential use of a wearable inertial measurement unit (IMU) based-wearable MOCAP system for biomechanical applications. The in vitro proof of concept is provided for the full lower body consisting of hip, knee, and ankle joints via controlled single-plane anatomical range of motion (ROM) simulations using an electrical motor, while collecting data simultaneously via opto-electronic markers and IMU sensors. On 15 healthy volunteers the flexion-extension, abduction-adduction, internal-external rotation (ROM) values of hip and, the flexion- extension ROM values of the knee and ankle joints are calculated for both systems. The Bland-Altman graphs showed promising agreement both for in vitro and in vivo experiments. The maximum Root Mean Square Errors (RMSE) between the systems in vitro was 3.4° for hip and 5.9° for knee flexion motion in vivo, respectively. The gait data of the volunteers were assessed between the heel strike and toe off events to investigate the limits of agreement, calculating the population averages and standard deviation for both systems over the gait cycle. The maximum difference was for the ankle joint <6°. The results show that proposed system could be an option as an affordable-democratic solution.

Performance Index for in Home Assessment of Motion Abilities in Ataxia Telangiectasia: A Pilot Study

Background. It has been shown in the very recent literature that human walking generates rhythmic motor patterns with hidden time harmonic structures that are represented (at the subject’s comfortable speed) by the occurrence of the golden ratio as the the ratio of the durations of specific walking gait subphases. Such harmonic proportions may be affected—partially or even totally destroyed—by several neurological and/or systemic disorders, thus drastically reducing the smooth, graceful, and melodic flow of movements and altering gait self-similarities. Aim. In this paper we aim at, preliminarily, showing the reliability of a technologically assisted methodology—performed with an easy to use wearable motion capture system—for the evaluation of motion abilities in Ataxia-Telangiectasia (AT), a rare infantile onset neurodegenerative disorder, whose typical neurological manifestations include progressive gait unbalance and the disturbance of motor coordination. Methods. Such an experimental methodology relies, for the first time, on the most recent accurate and objective outcome measures of gait recursivity and harmonicity and symmetry and double support subphase consistency, applied to three AT patients with different ranges of AT severity. Results. The quantification of the level of the distortions of harmonic temporal proportions is shown to include the qualitative evaluations of the three AT patients provided by clinicians. Conclusions. Easy to use wearable motion capture systems might be used to evaluate AT motion abilities through recursivity and harmonicity and symmetry (quantitative) outcome measures.

CVIDS: A Collaborative Localization and Dense Mapping Framework for Multi-Agent Based Visual-Inertial SLAM

Nowadays, visual SLAM (Simultaneous Localization And Mapping) has become a hot research topic due to its low costs and wide application scopes. Traditional visual SLAM frameworks are usually designed for single-agent systems, completing both the localization and the mapping with sensors equipped on a single robot or a mobile device. However, the mobility and work capacity of the single agent are usually limited. In reality, robots or mobile devices sometimes may be deployed in the form of clusters, such as drone formations, wearable motion capture systems, and so on. As far as we know, existing SLAM systems designed for multi-agents are still sporadic, and most of them have non-negligible limitations in functions. Specifically, on one hand, most of the existing multi-agent SLAM systems can only extract some key features and build sparse maps. On the other hand, schemes that can reconstruct the environment densely cannot get rid of the dependence on depth sensors, such as RGBD cameras or LiDARs. Systems that can yield high-density maps just with monocular camera suites are temporarily lacking. As an attempt to fill in the research gap to some extent, we design a novel collaborative SLAM system, namely CVIDS (Collaborative Visual-Inertial Dense SLAM), which follows a centralized and loosely coupled framework and can be integrated with any existing Visual-Inertial Odometry (VIO) to accomplish the co-localization and the dense reconstruction. Integrating our proposed robust loop closure detection module and two-stage pose-graph optimization pipeline, the co-localization module of CVIDS can estimate the poses of different agents in a unified coordinate system efficiently from the packed images and local poses sent by the client-ends of different agents. Besides, our motion-based dense mapping module can effectively recover the 3D structures of selected keyframes and then fuse their depth information to the global map for reconstruction. The superior performance of CVIDS is corroborated by both quantitative and qualitative experimental results. To make our results reproducible, the source code has been released at https://cslinzhang.github.io/CVIDS.

Validation of a Wireless Sensor System for the Estimation of Cumulative Lumbar Loads in Occupational Settings

This study aimed to evaluate the accuracy of 3D L5/S1 moment estimates from a wearable inertial motioncapture system during manual lifting tasks. Reference L5/S1 moments were calculated using inversedynamics bottom-up and top-down laboratory models, based on the data from a measurement systemcomprising optical motion capture and force plates. Nine groups of four subjects performed tasks consistingof lifting and lowering 10 lbs. load with three different heights and asymmetry angles. As a measure ofsystem performance, the root means square errors and absolute peak errors between models werecompared. Also, repeated measures analyses of variance were calculated comparing the means and theabsolute peaks of the estimated moments. The results suggest that most of the estimates obtained from thewireless sensor system are in close correspondence when comparing the means, and more variability isobserved when comparing peak values to other models calculating estimates of L5/S1 moments.

Wearable Motion Capture System Evaluation for Biomechanical Studies for Hip Joints.

Human motion capture (MOCAP) systems are vital while determining the loads occurring at the joints. Most of the clinical MOCAP systems are very costly, requiring investment and infrastructure. Therefore, alternative technologies are in demand. In this study, a novel markerless wearable MOCAP system was assessed for its compatibility with a biomechanical modeling software. To collect evidence, experiments were designed in two stages for quantifying the range of motion (ROM) of the hip joint, in vitro and in vivo. Three constrained single-plane motions-abduction/adduction, flexion/extension, and internal/external rotation movements of the active leg-were analyzed. The data were collected from 14 healthy volunteers, using the wearable system and a medical grade optoelectronic MOCAP system simultaneously and compared against. For the in vitro study, the root-mean-square error (RMSE) for the abduction/adduction motion of the hip joint was calculated as 0.11 deg/0.30 deg and 0.11 deg/0.09 deg, respectively, for the wearable and the opto-electronic system. The in vivo Bland-Altman plots showed that the two system data are comparable. The simulation software is found compatible to run the simulations in offline mode. The wearable system could be utilized in the field of biomechanics software for running the kinetic simulations. The results demonstrated that the wearable system could be an alternative in the field of biomechanics based on the evidence collected.

Validation of a wearable system for 3D ambulatory L5/S1 moment assessment during manual lifting using instrumented shoes and an inertial sensor suit

This study aimed to evaluate the accuracy of 3D L5/S1 moment estimates from an ambulatory measurement system consisting of a wearable inertial motion capture system (IMC) and instrumented force shoes (FSs), during manual lifting. Reference L5/S1 moments were calculated using an inverse dynamics bottom-up laboratory model (buLABmodel), based on data from a measurement system comprising optical motion capture (OMC) and force plates (FPs). System performance of (1) a bottom-up ambulatory model (buAMBmodel) using lower-body kinematic IMC and FS data, and (2) a top-down ambulatory model (tdAMBmodel) using upper-body kinematic IMC data and hand forces (HFs) were compared. HFs were estimated using full-body kinematic IMC data and FS forces.Eight males and eight females lifted a 10-kg box from different initial vertical/horizontal positions using either a free or an asymmetric lifting style. As a measure of system performance, root-mean-square (RMS) errors were calculated between the reference (buLABmodel) and ambulatory (tdAMBmodel &buAMBmodel) moments.The results showed two times smaller errors for the tdAMBmodel (averaged RMS errors < 20 Nm or 10% of peak extension moment) than for the buAMBmodel (average RMS errors < 40 Nm or 20% of peak extension moment). In conclusion, for ambulatory L5/S1 moment assessment with an IMC + FS system, using a top-down inverse dynamics approach with estimated hand forces is to be preferred over a bottom-up approach.

Detection and Quantification of Screw-Home Movement Using Nine-Axis Inertial Sensors

Purpose: Although previous studies on the screw-home movement (SHM) for autopsy specimen and walking of living persons conduc­t ed, the possibility of acquiring SHM based on inertial measurement units received little attention. This study aimed to investigate the possibility of measuring SHM for the non-weighted bearing using a micro-electro-mechanical system-based wearable motion capture system (MEMSS). Methods: MEMSS and camera-based motion analysis systems were used to obtain kinematic data of the knee joint. The knee joint moved from the flexion position to a fully extended position and then back to the start point. The coefficient of multiple correlation and the difference in the range of motion were used to assess the waveform similarity in the movement measured by two measurement sys­ tems. Results: The waveform similarity in the sagittal plane was excellent and the in the transverse plane was good. Significant differences were found in the sagittal plane between the two systems (p 0.05). Conclusion: The SHM during the passive motion without muscle contraction in the non-weighted bearing appeared in the entire range. We thought that the MEMSS could be easily applied to the acquisition of biomechanical data on the knee related to physical therapy.

Evaluating the feasibility of two post-hoc correction techniques for mitigating posture-induced measurement errors associated with wearable motion capture

Wearable motion capture systems are commonly used to measure body kinematics outside of laboratory settings. However, commercially available systems are designed to be used with typically developed adult populations, and assume users begin with a typical standing posture. Individuals with cerebral palsy and other neuromuscular pathologies often present atypical postures that can introduce significant errors in kinematics measurements from wearable motion capture. This study examines two post-hoc correction techniques for rectifying posture-induced errors in kinematic data: (1) Direct three-dimensional realignment of the measured body segment orientations, or (2) adding the initial static joint angle to the dynamic joint angle measurements. Gait kinematics were measured for eight able-bodied participants using a commercial wearable motion capture system. Participants walked with a typical gait, simulated crouch gait, and simulated equinus. The resulting kinematics from the uncorrected and post-hoc corrected trials were compared against simultaneously recorded measurements from an optoelectric motion capture system. Both correction techniques significantly decreased the posture-induced errors in lower-limb joint angle measurements. This work establishes a basis for the application of post-hoc correction techniques, aimed at improving the performance of wearable kinematic measurement systems when used with individuals having non-typical postures.

Inertial Measurement Unit Based Upper Extremity Motion Characterization for Action Research Arm Test and Activities of Daily Living.

In practical rehabilitation robot development, it is imperative to pre-specify the critical workspace to prevent redundant structure. This study aimed to characterize the upper extremity motion during essential activities in daily living. An IMU-based wearable motion capture system was used to access arm movements. Ten healthy subjects performed the Action Research Arm Test (ARAT) and six pre-selected essential daily activities. The Euler angles of the major joints, and acceleration from wrist and hand sensors were acquired and analyzed. The size of the workspace for the ARAT was 0.53 (left-right) × 0.92 (front-back) × 0.89 (up-down) m for the dominant hand. For the daily activities, the workspace size was 0.71 × 0.70 × 0.86 m for the dominant hand, significantly larger than the non-dominant hand (p ≤ 0.011). The average range of motion (RoM) during ARAT was 109.15 ± 18.82° for elbow flexion/extension, 105.23 ± 5.38° for forearm supination/pronation, 91.99 ± 0.98° for shoulder internal/external rotation, and 82.90 ± 22.52° for wrist dorsiflexion/volarflexion, whereas the corresponding range for daily activities were 120.61 ± 23.64°, 128.09 ± 22.04°, 111.56 ± 31.88°, and 113.70 ± 18.26°. The shoulder joint was more abducted and extended during pinching compared to grasping posture (p < 0.001). Reaching from a grasping posture required approximately 70° elbow extension and 36° forearm supination from the initial position. The study results provide an important database for the workspace and RoM for essential arm movements.