KARATECH: A Practice Support System Using an Accelerometer to Reduce the Preliminary Actions of Karate.

Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as "pre-actions"), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite's punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate.

Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia.

Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.

Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study.

The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.

Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints.

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing.

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online.

Immersive Virtual Environments and Wearable Haptic Devices in rehabilitation of children with neuromotor impairments: a single-blind randomized controlled crossover pilot study.

BACKGROUND: The past decade has seen the emergence of rehabilitation treatments using virtual reality. One of the advantages in using this technology is the potential to create positive motivation, by means of engaging environments and tasks shaped in the form of serious games. The aim of this study is to determine the efficacy of immersive Virtual Environments and weaRable hAptic devices (VERA) for rehabilitation of upper limb in children with Cerebral Palsy (CP) and Developmental Dyspraxia (DD). METHODS: A two period cross-over design was adopted for determining the differences between the proposed therapy and a conventional treatment. Eight children were randomized into two groups: one group received the VERA treatment in the first period and the manual therapy in the second period, and viceversa for the other group. Children were assessed at the beginning and the end of each period through both the Nine Hole Peg Test (9-HPT, primary outcome) and Kinesiological Measurements obtained during the performing of similar tasks in a real setting scenario (secondary outcomes). RESULTS: All subjects, not depending from which group they come from, significantly improved in both the performance of the 9-HPT and in the parameters of the kinesiological measurements (movement error and smoothness). No statistically significant differences have been found between the two groups. CONCLUSIONS: These findings suggest that immersive VE and wearable haptic devices is a viable alternative to conventional therapy for improving upper extremity function in children with neuromotor impairments. Trial registration ClinicalTrials, NCT03353623. Registered 27 November 2017-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03353623.

Physically Consistent Whole-Body Kinematics Assessment Based on an RGB-D Sensor. Application to Simple Rehabilitation Exercises.

This work proposes to improve the accuracy of joint angle estimates obtained from an RGB-D sensor. It is based on a constrained extended Kalman Filter that tracks inputted measured joint centers. Since the proposed approach uses a biomechanical model, it allows physically consistent constrained joint angles and constant segment lengths to be obtained. A practical method that is not sensor-specific for the optimal tuning of the extended Kalman filter covariance matrices is provided. It uses reference data obtained from a stereophotogrammetric system but it has to be tuned only once since it is task-specific only. The improvement of the optimal tuning over classical methods in setting the covariance matrices is shown with a statistical parametric mapping analysis. The proposed approach was tested with six healthy subjects who performed four rehabilitation tasks. The accuracy of joint angle estimates was assessed with a reference stereophotogrammetric system. Even if some joint angles, such as the internal/external rotations, were not well estimated, the proposed optimized algorithm reached a satisfactory average root mean square difference of 9.7 ∘ and a correlation coefficient of 0.8 for all joints. Our results show that an affordable RGB-D sensor can be used for simple in-home rehabilitation when using a constrained biomechanical model.

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter.

Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by means of a Kalman filter that fuses acceleration, angular velocity, and magnetic field measures. A critical point when implementing a Kalman filter is the initialization of the covariance matrices that characterize mismodelling and input error from noisy sensors. The present study proposes a methodology to identify the initial values of these covariance matrices that optimize orientation estimation in the context of human motion analysis. The approach used was to apply motion to the sensor manually, and to compare the orientation obtained via the Kalman filter to a measurement from an optoelectronic system acting as a reference. Testing different sets of values for each parameter of the covariance matrices, and comparing each MIMU measurement with the reference measurement, enabled identification of the most effective values. Moreover, with these optimized initial covariance matrices, the orientation estimation was greatly improved. The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering. However, the methodology should be improved in order to reduce the duration of the whole process.

Open source platform for collaborative construction of wearable sensor datasets for human motion analysis and an application for gait analysis.

Nearly every practical improvement in modeling human motion is well founded in a properly designed collection of data or datasets. These datasets must be made publicly available for the community could validate and accept them. It is reasonable to concede that a collective, guided enterprise could serve to devise solid and substantial datasets, as a result of a collaborative effort, in the same sense as the open software community does. In this way datasets could be complemented, extended and expanded in size with, for example, more individuals, samples and human actions. For this to be possible some commitments must be made by the collaborators, being one of them sharing the same data acquisition platform. In this paper, we offer an affordable open source hardware and software platform based on inertial wearable sensors in a way that several groups could cooperate in the construction of datasets through common software suitable for collaboration. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118Hz. Also, a proof-of-concept dataset is provided comprising sampled data from 12 subjects suitable for gait analysis.

An IMU-to-Body Alignment Method Applied to Human Gait Analysis.

This paper presents a novel calibration procedure as a simple, yet powerful, method to place and align inertial sensors with body segments. The calibration can be easily replicated without the need of any additional tools. The proposed method is validated in three different applications: a computer mathematical simulation; a simplified joint composed of two semi-spheres interconnected by a universal goniometer; and a real gait test with five able-bodied subjects. Simulation results demonstrate that, after the calibration method is applied, the joint angles are correctly measured independently of previous sensor placement on the joint, thus validating the proposed procedure. In the cases of a simplified joint and a real gait test with human volunteers, the method also performs correctly, although secondary plane errors appear when compared with the simulation results. We believe that such errors are caused by limitations of the current inertial measurement unit (IMU) technology and fusion algorithms. In conclusion, the presented calibration procedure is an interesting option to solve the alignment problem when using IMUs for gait analysis.

Wearable Goniometer and Accelerometer Sensory Fusion for Knee Joint Angle Measurement in Daily Life.

Human motion analysis is crucial for a wide range of applications and disciplines. The development and validation of low cost and unobtrusive sensing systems for ambulatory motion detection is still an open issue. Inertial measurement systems and e-textile sensors are emerging as potential technologies for daily life situations. We developed and conducted a preliminary evaluation of an innovative sensing concept that combines e-textiles and tri-axial accelerometers for ambulatory human motion analysis. Our sensory fusion method is based on a Kalman filter technique and combines the outputs of textile electrogoniometers and accelerometers without making any assumptions regarding the initial accelerometer position and orientation. We used our technique to measure the flexion-extension angle of the knee in different motion tasks (monopodalic flexions and walking at different velocities). The estimation technique was benchmarked against a commercial measurement system based on inertial measurement units and performed reliably for all of the various tasks (mean and standard deviation of the root mean square error of 1:96 and 0:96, respectively). In addition, the method showed a notable improvement in angular estimation compared to the estimation derived by the textile goniometer and accelerometer considered separately. In future work, we will extend this method to more complex and multi-degree of freedom joints.

A 3D analysis of the joint torques developed during driver's ingress-egress motion.

Providing an easy ingress-egress (I/E) movement remains a challenge for car designers. I/E has been largely studied in kinematics, but not in dynamics. This study proposes: (1) to evaluate and describe the motor torques developed in the lower limbs and lumbar joints during I/E motions and (2) to analyse the influence of the car geometry and subject anthropometry. An experiment was performed to observe 15 subjects of three anthropometrical groups getting in and out of a car mock-up simulating three different vehicle configurations. Motor torques were extracted using an inverse dynamics analysis. Both ingress and egress motions were primarily characterised by large torques. Overall, the taller a subject and the lower the seat of the vehicle were, the larger the peak torques were. Moreover, peak torques were higher for egress than ingress. These results are discussed in regard to the current knowledge on I/E ergonomics. PRACTITIONER SUMMARY: Car ingress­egress (I/E) is an ergonomics challenge. Little is known about the physical efforts developed in this motion. Developed motor torques were experimentally assessed for three anthropometrical groups and vehicle configurations. Results obtained were discussed in regard to the current knowledge on I/E ergonomics.

Markerless motion capture provides repeatable gait outcomes in patients with knee osteoarthritis.

Motion analysis has seen minimal adoption for orthopaedic clinical assessments. Markerless motion capture solutions, namely Theia3D, address limitations of previous methods and provide gait outcomes that are robust to clothing choice and repeatable in healthy adults. Repeatability in orthopaedic populations has not been investigated and is important for clinical utility and adoption. The purpose of this study was to evaluate the repeatability of Theia3D for gait analysis in a knee osteoarthritis population. Ten orthopaedic patients with knee osteoarthritis underwent gait analysis on three visits, with an average of 8 days between. Participants were recorded during one-minute overground walking trials at self-selected typical and fast speeds by 8 synchronized video cameras. Video data were processed using Theia3D. Intraclass correlations were used to examine the repeatability of temporal distance metrics as well as segment lengths of the underlying kinematic model. Inter-trial and inter-session variability of lower extremity joint angles were estimated for each point of the gait cycle. Intraclass correlations were greater than 0.98 for all temporal distance metrics for both speeds. Lower body segment lengths had intraclass correlations above 0.90. Participant average joint angle waveforms displayed consistent patterns between visits. The average inter-trial and inter-session variability in joint angles across speeds were 1.17 and 1.45 degrees, respectively. The variability in joint angles between visits was less than typically reported for marker-based methods. Gait outcomes measured with Theia3D were highly repeatable in patients with knee osteoarthritis providing further validation for its use in clinical assessment and longitudinal studies.

Multimodal human motion dataset of 3D anatomical landmarks and pose keypoints.

In this paper, we present a dataset that takes 2D and 3D human pose keypoints estimated from images and relates them to the location of 3D anatomical landmarks. The dataset contains 51,051 poses obtained from 71 persons in A-Pose while performing 7 movements (walking, running, squatting, and four types of jumping). These poses were scanned to build a collection of 3D moving textured meshes with anatomical correspondence. Each mesh in that collection was used to obtain the 3D locations of 53 anatomical landmarks, and 48 images were created using virtual cameras with different perspectives. 2D pose keypoints from those images were obtained using the MediaPipe Human Pose Landmarker, and their corresponding 3D keypoints were calculated by linear triangulation. The dataset consists of a folder for each participant containing two Track Row Column (TRC) files and one JSON file for each movement sequence. One TRC file is used to store the 3D data of the triangulated 3D keypoints while the other contains the 3D anatomical landmarks. The JSON file is used to store the 2D keypoints and the calibration parameters of the virtual cameras. The anthropometric characteristics of the participants are annotated in a single CSV file. These data are intended to be used in developments that require the transformation of existing human pose solutions in computer vision into biomechanical applications or simulations. This dataset can also be used in other applications related to training neural networks for human motion analysis and studying their influence on anthropometric characteristics.

Physical human locomotion prediction using manifold regularization.

Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature.

The helical axis of anatomical joints: calculation methods, literature review, and software implementation.

The calculation of the helical axis, a.k.a. screw axis, is a functional technique that was introduced for the characterization of the motion and the stability of a human joint. Examples are its applications in the design of prostheses and its use for evaluating the joint performance in post-operatory follow-up. The typical way of studying the variations in the helical axis is to instantaneously compare it to some reference. The reference is typically assumed as (i) an anatomical or geometrical reference (e.g., the condyle to condyle axis or an anatomical plane); (ii) a functional reference, i.e., some axis calculated in a functional way. Calculating the helical axis means determining its orientation and its position, based on the recorded motion of the joint. This paper reviewed the calculation methods of the helical axis, its clinical applications, and the most relevant findings. The operative equations of the most common procedures were clearly and synthetically illustrated. More in detail, the focus of this review was set on the calculation of (i) the instantaneous helical axis; (ii) the finite helical axis; (iii) the average helical axis; (iv) a functional coordinate system attached to the helical axis; and (v) the analysis of the time variations of helical axis. The calculation of those quantities was implemented in MATLAB and the code was proposed as supplementary material. The calculation of the discussed quantities was demonstrated on a sample dataset.

Healthcare applications of single camera markerless motion capture: a scoping review.

Background: Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Survey Methodology: Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. Results: A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Conclusions: Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.

Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation.

In medical tasks such as human motion analysis, computer-aided auxiliary systems have become the preferred choice for human experts for their high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors, or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives (ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with 10 participants. The results validate ProMPs as a robust and effective feature extractor for human motions.