Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system

Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

Mathematical Modeling and Simulation of Table Tennis Trajectory Based on Digital Video Image Processing

Digital sports training based on digital video image processing promises to reduce the reliance on the experience of coaches in the table tennis training process and to achieve a more general physical education base. Based on this approach, this paper describes the specific forms of exercise content, movement characteristics, and skill levels in the table tennis framework and specifies the calculation methods of motion capture and movement characteristics suitable for table tennis. Meanwhile, to further improve the accuracy of the inertial motion capture system in restoring the position posture of the trainees, this paper improves the original inertial motion capture system from two aspects: contact judgment of both feet and correction of the position posture based on the contact position constraint. The simulation results show that the corrected human posture has good action smoothness. This paper first proposes a knowledge-based generic sports-assisted training framework based on generalizing the traditional sports training model. The framework contains four main modules: domain knowledge, trainees, sport evaluation, and controller. The domain knowledge module is a digital representation of the knowledge of the exercise content, improvement instructions, and skill indicators of the sport; the trainee module is the active response of the trainee to the exercise content and improvement instructions; the motion evaluation module uses motion capture technology to obtain the raw motion data of the trainee and further calculates the motion characteristics; the controller module proposes improvement instructions to the trainee or makes him/her practice new content based on the results of the motion evaluation. Based on the results of the motion evaluation, the controller module proposes improvement instructions or makes the trainee practice new content until the trainee achieves the desired goal.

The Relationship Between Spatio-temporal Gait Parameters and Cognitive Function: Protocol for a Cross-sectional Study

BackgroundMotor dysfunctions, such as slower walking speed, precede the occurrence of dementia and mild cognitive impairment, suggesting that walking parameters may be effective biomarkers for detecting early sub-clinical cognitive risk. In fact, while our preliminary study had a small sample, we found several walking parameters obtained by three-dimensional motion capture system, to be correlated with computer-based assessments of various cognitive function modalities. The Cognitive-Gait (CoGait) Database Project, described in the current protocol, aims to establish a database of multi-dimensional walking and cognitive performance data, collected from a large sample of healthy participants, crucial for detecting early sub-clinical cognitive risk. Methods: The study will recruit healthy volunteers, 20 years or older, without any neurological or musculoskeletal disorders. The estimated sample size is 450 participants, including a 10% attrition rate. Using computer-based cognitive assessments, all participants will perform six tasks: (i) the simple reaction time task, (ii) Go/No-Go task, (iii) Stroop Color–Word Test, (iv) N-back test, (v) Trail making test, and (vi) Digit Span test. Gait will be measured through joint kinematics and global positioning in participants’ lower legs, using pants with an inertial measurement unit-based three-dimensional motion capture system, while walking at a comfortable and faster pace. Finally, we will establish a prediction model for various cognitive performance modalities, based on walking performance. Discussion: This will be the first study to reveal the relationship between walking and cognitive performance using multi-dimensional data collected from a large sample of healthy adults, from the general population. Although there are several methodological limitations, such as accuracy of measurements, the CoGait database is expected to be the standard value for both walking and cognitive functions, supporting the evaluation of psychomotor function in early sub-clinical cognitive risk, including motoric-cognitive risk syndrome.Trial registration: None.

Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson’s Disease

Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson’s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

Measurement of chest wall motion using a motion capture system with the one-pitch phase analysis method

Spirometry is a standard method for assessing lung function. However, its use is challenging in some patients, and it has limitations such as risk of infection and inability to assess regional chest wall motion. A three-dimensional motion capture system using the one-pitch phase analysis (MCO) method can facilitate high precision measurement of moving objects in real-time in a non-contacting manner. In this study, the MCO method was applied to examine thoraco-abdominal (TA) wall motion for assessing pulmonary function. We recruited 48 male participants, and all underwent spirometry and chest wall motion measurement with the MCO method. A significant positive correlation was observed between the vital capacity (Spearman’s ρ = 0.68, p < 0.0001), forced vital capacity (Spearman’s ρ = 0.62, p < 0.0001), and tidal volume (Spearman’s ρ = 0.61, p < 0.0001) of spirometry and the counterpart parameters of MCO method. Moreover, the MCO method could detect regional rib cage and abdomen compartment contributions and could assess TA asynchrony, indicating almost complete synchronous movement (phase angle for each compartment: − 5.05° to 3.86°). These findings suggest that this technique could examine chest wall motion, and may be effective in analyzing chest wall volume changes and pulmonary function.