Cervical facet joint degeneration, facet joint angle and paraspinal muscle degeneration are correlated with degenerative cervical spondylolisthesis at C4/5: A propensity score-matched study.

BACKGROUND CONTEXT: Prior studies have hypothesized that degenerative cervical spondylolisthesis (DCS) may be influenced by loss of stability due to disc, facet joint or cervical alignment. Meanwhile, it is commonly believed that the facet joints and paraspinal muscles participate in maintaining cervical spine stability. However, the impact of paraspinal muscle morphology and detailed facet joint features on DCS requires further investigation. PURPOSE: To compare facet joint characteristics, disc degeneration and muscle morphology between patients with DCS and those without DCS. STUDY DESIGN/SETTING: Retrospective cohort study. PATIENT SAMPLE: Consecutive surgical patients with degenerative cervical spondylosis from June 2016 to August 2023 were recruited. OUTCOME MEASURES: DCS was assessed on X-ray based on the translation distance. Cervical facet joint degeneration (CFD), the facet joint angle on the axial plane (FA-A) and the facet joint angle on the sagittal plane (FA-S), and facet joint tropism (FT) were measured on computerized tomography (CT). Paraspinal muscle degeneration was assessed on magnetic resonance imaging (MRI) including by the adjusted cross-sectional area (aCSA), the functional aCSA, the fat infiltration ratio (FI%). The Pfirrmann grade of the cervical disc was also evaluated. METHODS: Demographic and clinical data were compared in matched and unmatched cohorts. Disc degeneration, muscle degeneration and facet joint characteristics, including FA, FT and CFD, were compared between patients with and without DCS. Furthermore, the degree of CFD was compared with that of adjacent segments in both groups. Additionally, logistic regression was performed to determine independent risk factors for DCS. Finally, the receiver operating characteristic (ROC) curve, area under the curve (AUC) and cutoff value for the risk factors were calculated. RESULTS: A total of 431 surgical patients were propensity score matched for age, sex and BMI, and 146 patients were included in the final analysis, with 73 patients in the DCS group and 73 patients in the non-DCS group. DCS patients exhibited more severe CFD at C4/5 (segment with spondylolisthesis). Additionally, DCS was generally associated with more severe CFD, a more horizontal FA-S, more FT and worse paraspinal muscle health but similar disc degeneration. In addition, anterior spondylolisthesis was related to more severe CFD and decreased functional aCSA of the flexors and extensors. Finally, more severe CFD, a more horizontal FA-S and a higher FI% on deep extensor were revealed to be risk factors for DCS, with cutoff values of 1.5, 44.5̊ and 37.1%, respectively. CONCLUSIONS: This study demonstrated that CFD, the FA and FT and parasipnal muscle degeneration were associated with DCS. And may provide novel insight into the pathogenesis and nature history of DCS and suggest the evolution of degeneration in the cervical spine.

Biomechanics of step-off drop landings are affected by limb dominance and lead limb in task initiation.

This study examines the effects of limb dominance and lead limb in task initiation on the kinetics and kinematics of step-off drop landings. Nineteen male participants performed drop landings led by the dominant and non-dominant limbs at 45-cm and 60-cm drop heights. Ground reaction force (GRF) and lower body kinematic data were collected. Between-limb time differences at the initial ground contact were calculated to indicate temporal asymmetry. Statistical Parametric Mapping (SPM) was applied for waveform analysis while two-way repeated measures ANOVA was used for discrete parameters. SPM results revealed greater GRF and lesser ankle dorsiflexion in the lead limb compared to the trail limb in 3 out of 4 landing conditions. The dominant limb displayed a greater forefoot loading rate (45 cm: p=.009, ηp2 = 0.438; 60 cm: p=.035, ηp2 = 0.225) and greater ankle joint quasi-stiffness (45 cm: p < .001, ηp2 = 0.360; 60 cm: p < .001, ηp2 = 0.597) than the non-dominant limb. Not all 380 trials were lead-limb first landings, with a smaller between-limb time difference (p=.009, d = 0.60) at 60 cm (4.1 ± 2.3 ms) than 45 cm (5.6 ± 2.7 ms). In conclusion, the step-off drop landing is not an ideal protocol for examining bilateral asymmetry in lower limb biomechanics due to potential biases introduced by limb dominance and the step-off limb.

Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor.

Recently, posture recognition technology has advanced rapidly. Herein, we present a novel posture angle calculation system utilizing a single inertial measurement unit and a spatial geometric equation to accurately identify the three-dimensional (3D) motion angles and postures of both the upper and lower limbs of the human body. This wearable system facilitates continuous monitoring of body movements without the spatial limitations or occlusion issues associated with camera-based methods. This posture-recognition system has many benefits. Providing precise posture change information helps users assess the accuracy of their movements, prevent sports injuries, and enhance sports performance. This system employs a single inertial sensor, coupled with a filtering mechanism, to calculate the sensor's trajectory and coordinates in 3D space. Subsequently, the spatial geometry equation devised herein accurately computed the joint angles for changing body postures. To validate its effectiveness, the joint angles estimated from the proposed system were compared with those from dual inertial sensors and image recognition technology. The joint angle discrepancies for this system were within 10° and 5° when compared with dual inertial sensors and image recognition technology, respectively. Such reliability and accuracy of the proposed angle estimation system make it a valuable reference for assessing joint angles.

Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation.

Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint. Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA). Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly. Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.

Technological advances in lower-limb tele-rehabilitation: A review of literature.

Tele-rehabilitation is a healthcare practice that leverages technology to provide rehabilitation services remotely to individuals in their own homes or other locations. With advancements in remote monitoring and Artificial Intelligence, automatic tele-rehabilitation systems that can measure joint angles, recognize exercises, and provide feedback based on movement analysis are being developed. Such platforms can offer valuable information to clinicians for improved care planning. However, with various methods and sensors being used, understanding their pros, cons, and performance is important. This paper reviews and compares the performance of recent vision-based, wearable, and pressure-sensing technologies used in lower limb tele-rehabilitation systems over the past 10 years (from 2014 to 2023). We selected studies that were published in English and focused on joint angle estimation, activity recognition, and exercise assessment. Vision-based approaches were the most common, accounting for 42% of studies. Wearable technology followed at approximately 37%, and pressure-sensing technology appeared in 21% of studies. Identified gaps include a lack of uniformity in reported performance metrics and evaluation methods, a need for cross-subject validation, inadequate testing with patients and older adults, restricted sets of exercises evaluated, and a scarcity of comprehensive datasets on lower limb exercises, especially those involving movements while lying down.

Photogrammetric analysis of limb joint angles in normal-gait cows before and after hoof trimming.

The aim of this study was to evaluate the effect of hoof trimming on overall limb movements by comparing the changes in 8 limb joint angles before and after one week of hoof trimming. Seventeen Holstein-Friesian dairy cows that were able to move freely and had no history of hoof diseases were included in the study. The cows were walked on a rubber mat with a high friction coefficient (HFM) and a low friction coefficient by the spraying of sodium polyacrylate (LFM). A high-speed camera was set to 200 fps on the image analysis software, and the images of the cows that were given 15 reflective markers on their right side were captured while walking on the test mat. The tests were conducted before and after one week of hoof trimming, and the cows were trimmed by the functional hoof trimming method. With image analysis software, video clips of walking cows were confirmed visually and tracked during one gait cycle by each reflective marker attached to the hoof of the forelimb and hindlimb, after which the stance phase and swing phase were identified. The durations of the stance phase and swing phase of the forelimb and hindlimb, respectively, and the maximum, minimum, and range of motion (ROM) values of the 8 joint angles, shoulder joint, elbow joint, carpus joint, forelimb fetlock joint, hip joint, stifle joint, hock joint and hindlimb fetlock joint during one gait cycle were included in the analysis. The maximum and minimum angles of the hip and stifle joints were narrower after hoof trimming than before, although the ROM did not change and was clearer for HFM than for LFM. It was thought that the flexion of the proximal hindlimb would progress smoothly during walking after trimming.

Dynamics of the suprapatellar bursa during knee joint extension.

PURPOSE: The suprapatellar bursa is located in the proximal deep layer of the patella and is thought to reduce tissue friction by changing from a single-membrane structure to a double-membrane structure during knee joint motion. However, the dynamics of the suprapatellar bursa have only been inferred from positional relationships, and the actual dynamics have not been confirmed. METHODS: Dynamics of the suprapatellar bursa during knee joint motion were observed in eight knees of four Thiel-fixed cadavers and the angle at which the bursa begins to show a double membrane was revealed. The flexion angles of knee joints were measured when the double-membrane structure of the suprapatellar bursa began to appear during knee joint extension. RESULTS: The suprapatellar bursa changes from a single membrane to a double-membrane structure at 91 ± 4° of flexion, when the knee joint is moved from a flexed position to an extended position. CONCLUSION: The suprapatellar bursa may be involved in limitations to knee joint range of motion and pain at an angle of approximately 90°. Further studies are needed to verify whether the same dynamics are observed in living subjects.

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model.

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis.

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications.

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

Effects of foot orthoses on running kinetics and kinematics: A systematic review and meta-analysis.

BACKGROUND: Foot orthoses (FOs) are often prescribed by clinicians to treat foot and ankle conditions, prevent running injuries, and enhance performance. However, the lack of higher-order synthesis of clinical trials makes it challenging for clinicians to adopt an evidence-based approach to FOs' prescriptions. RESEARCH QUESTION: Do FOs with different modifications alter lower extremity running kinematics and kinetics? METHODS: A systematic search of seven databases was conducted from inception to February 2023. The analysis was restricted to healthy adults without foot musculoskeletal impairments and studies that compared the FOs effects with the controls. The methodological quality of the 35 studies that met the eligibility criteria was evaluated using the modified Downs and Black checklist. The random effects model estimated the standardized mean difference (SMD) with 95% confidence intervals and effect sizes. Sub-group analyses based on FOs type were performed to assess the potential effects of the intervention. RESULTS: Our findings indicated that both custom and off-the-shelf arch-support FOs reduced peak plantar pressure at the medial heel (SMD=-0.35, and SMD=-1.03), lateral heel (SMD=-0.50, and SMD=-0.53), and medial forefoot (SMD=-0.20, and SMD=-0.27), but increased plantar pressure at the mid-foot (SMD=0.30, and SMD=0.56). Compared with the controls, significant increases (SMD=0.36) in perceived comfort were found with custom FOs. A reduction (SMD=-0.58) in initial ankle inversion was found when a raised heel cup was integrated with arch-support FOs. A medial post integrated with arch support exhibited a reduced ankle (SMD=-1.66) and tibial (SMD=-0.63) range of motion. Custom FOs, however, unfavorably affected the running economy (SMD=-0.25) and perceived exertion (SMD=0.20). SIGNIFICANCE: Although FOs have been reported to have some positive biomechanical effects in healthy populations without musculoskeletal impairments or running-related issues, they need to be optimized and generalized to achieve better running performance and prevent injury.

Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles.

Human-machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects' dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.

Joint angle estimation during shoulder abduction exercise using contactless technology.

BACKGROUND: Tele-rehabilitation, also known as tele-rehab, uses communication technologies to provide rehabilitation services from a distance. The COVID-19 pandemic has highlighted the importance of tele-rehab, where the in-person visits declined and the demand for remote healthcare rises. Tele-rehab offers enhanced accessibility, convenience, cost-effectiveness, flexibility, care quality, continuity, and communication. However, the current systems are often not able to perform a comprehensive movement analysis. To address this, we propose and validate a novel approach using depth technology and skeleton tracking algorithms. METHODS: Our data involved 14 participants (8 females, 6 males) performing shoulder abduction exercises. We collected depth videos from an LiDAR camera and motion data from a Motion Capture (Mocap) system as our ground truth. The data were collected at distances of 2 m, 2.5 m, and 3.5 m from the LiDAR sensor for both arms. Our innovative approach integrates LiDAR with the Cubemos and Mediapipe skeleton tracking frameworks, enabling the assessment of 3D joint angles. We validated the system by comparing the estimated joint angles versus Mocap outputs. Personalized calibration was applied using various regression models to enhance the accuracy of the joint angle calculations. RESULTS: The Cubemos skeleton tracking system outperformed Mediapipe in joint angle estimation with higher accuracy and fewer errors. The proposed system showed a strong correlation with Mocap results, although some deviations were present due to noise. Precision decreased as the distance from the camera increased. Calibration significantly improved performance. Linear regression models consistently outperformed nonlinear models, especially at shorter distances. CONCLUSION: This study showcases the potential of a marker-less system, to proficiently track body joints and upper-limb angles. Signals from the proposed system and the Mocap system exhibited robust correlation, with Mean Absolute Errors (MAEs) consistently below [Formula: see text]. LiDAR's depth feature enabled accurate computation of in-depth angles beyond the reach of traditional RGB cameras. Altogether, this emphasizes the depth-based system's potential for precise joint tracking and angle calculation in tele-rehab applications.

Analysis of Biomechanical Characteristics of Lower Limbs During Stair Descent in Patients with Hemiplegia / 医用生物力学

Objective To conduct a comparative analysis of the biomechanical characteristics of the lower limbs during stair descent in patients with hemiplegia using different method to provide theoretical references for reducing fall risk during stair descent.Methods Ten healthy subjects and 20 patients with hemiplegia were selected,and their kinematic and dynamic data during stair descent were collected using the Qualisys Motion capture system and the Kistler three-dimensional dynamometer.Their biomechanical characteristics and fall risks were also analyzed.Results Compared with that of healthy subjects and patients that step on the healthy side(SHS),the range of motion(ROM)of the affected side in the lower-limb joints of patients that step on the affected side(SAS)was smaller.SHS reduced the flexion and extension ranges of the healthy side of the knee joint,and the ROM of the affected side in the lower-limb joints of SHS patients was greater than that of SAS patients.The ground reaction force(GRF)curve changes of SAS patients in left and right directions during stair descent were relatively consistent with those of normal subjects.The maximum vertical GRF of the affected side in SAS patients at the moment of landing was 1.05 times the body weight,whereas that of the healthy side was 1.25 times the body weight,which was lower than that of normal subjects(1.5 times the body weight).The maximum vertical GRF of the healthy side in SHS patients at the moment of landing was 1.85 times the body weight,which was higher than that of SAS patients and normal subjects.Conclusions Compared with that of SAS patients,the affected limb side of SHS patients has a greater ROM and vertical GRF at the moment of landing during stair descent,making SHS difficult to master.SAS is most consistent with the biomechanical characteristics during stair descent of patients with hemiplegia.

Inertial measurement unit sensor-based gait analysis in adults and older adults: A cross-sectional study.

BACKGROUND: Gait assessment has been used in a wide range of clinical applications, and gait velocity is also a leading predictor of disease and physical functional aspects in older adults. RESEARCH QUESTION: The study aim to examine the changes in IMU-based gait parameters according to age in healthy adults aged 50 and older, to analyze differences between aging patients. METHODS: A total of 296 healthy adults (65.32 ± 6.74 yrs; 83.10 % female) were recruited. Gait assessment was performed using an IMU sensor-based gait analysis system, and 3D motion information of hip and knee joints was obtained using magnetic sensors. The basic characteristics of the study sample were stratified by age category, and the baseline characteristics between the groups were compared using analysis of variance (ANOVA). Pearson's correlation analysis was used to analyze the relationship between age as the dependent variable and several measures of gait parameters and joint angles as independent variables. RESULTS: The results of this study found that there were significant differences in gait velocity and both terminal double support in the three groups according to age, and statistically significant differences in the three groups in hip joint angle and knee joints angle. In addition, it was found that the gait velocity and knee/hip joint angle changed with age, and the gait velocity and knee/hip joint angle were also different in the elderly and adult groups. CONCLUSIONS: We found changes in gait parameters and joint angles according to age in healthy adults and older adults and confirmed the difference in gait velocity and joint angles between adults and older adults.

Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.

Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.

Sensor Fusion-Based Anthropomorphic Control of a Robotic Arm.

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.

Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control.

To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb's structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.

The overlooked symptom of coccydynia: evaluation of sacrococcygeal morphologic and morphometric findings.

BACKGROUND: Coccydynia is one of the most overlooked symptoms in daily clinical practice. Definitions for radiologic evaluation are controversial. OBJECTIVES: We aimed to compare the morphology and morphometric measurements of the sacrococcygeal region with those of a healthy population to support radiologic decision-making. MATERIALS AND METHODS: In total, 26 traumatic and 50 idiopathic cases of coccydynia as well as 74 healthy control cases were retrospectively compared. The morphologic type of the coccyx, the presence of fusion, and the number of coccygeal segments were evaluated in both groups. Morphometric parameters such as sacrococcygeal angle (SCA), sacrococcygeal joint angle (SCJA), intercoccygeal angle (ICA), sacral slope (SS), coccyx curved length (CCL), sacrum curved length (SCL), coccyx length (CL), sacrum length (SL), and sacrococcygeal total length (SCTL) were investigated. RESULTS: Significant differences were found between the coccydynia group and the healthy control group in morphologic parameters such as female gender, coccyx segment, coccyx morphology, presence of sacrococcygeal joint, and segment of sacrococcygeal joint fusion (p < 0.05). In morphologic measurements, SCJA, SCL, SL, coccyx and sacrum curvature indexes were significantly increased (p < 0.05). No significant difference was found in the morphologic and morphometric parameters evaluated when compared with the duration of coccydynia (p > 0.05). CONCLUSION: An increase in the SCJA, SCL, SL, SCI, and coccyx curvature index measurements predisposes to coccydynia. It would be more accurate to perform radiological evaluation by familiarization with these morphologic and morphometric parameters.

Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity.

Background and purpose: Continuous monitoring of lower-limb movement may help in the early detection and control/reduction of diseases (such as the progression of orthopedic diseases) by applying suitable interventions. Therefore, it is invaluable to calculate the lower-limb movement (sagittal joint angles) while walking daily for continuous evaluation of such risks. Although cameras in a motion capture system are necessary for calculating lower-limb sagittal joint angles during gait, the method is unrealistic considering the setting is difficult to achieve in daily life. Therefore, the estimation of lower-limb sagittal joint angles during walking based on variables, which can be measured using wearable sensors (e.g., foot acceleration and angular velocity), is important. This study estimates the lower-limb sagittal joint angles during gait from the norms of foot acceleration and angular velocity using machine learning and validates the accuracy of the estimated joint angles with those obtained using a motion capture system. Methods: Healthy adults (n = 200) were asked to walk at a comfortable speed (10 trials), and their lower-limb sagittal joint angles, foot accelerations, and angular velocities were obtained. Using these variables, we established a feedforward neural network and estimated the lower-limb sagittal joint angles. Results: The average root mean squared errors of the lower-limb sagittal joint angles during gait ranged between 2.5°-7.0° (hip: 7.0°; knee: 4.0°; and ankle: 2.5°). Conclusion: These results show that we can estimate the lower-limb sagittal joint angles during gait using only the norms of foot acceleration and angular velocity, which can help calculate the lower-limb sagittal joint angles during daily walking.

Gait analysis comparison between manual marking, 2D pose estimation algorithms, and 3D marker-based system.

Introduction: Recent advances in Artificial Intelligence (AI) and Computer Vision (CV) have led to automated pose estimation algorithms using simple 2D videos. This has created the potential to perform kinematic measurements without the need for specialized, and often expensive, equipment. Even though there's a growing body of literature on the development and validation of such algorithms for practical use, they haven't been adopted by health professionals. As a result, manual video annotation tools remain pretty common. Part of the reason is that the pose estimation modules can be erratic, producing errors that are difficult to rectify. Because of that, health professionals prefer the use of tried and true methods despite the time and cost savings pose estimation can offer. Methods: In this work, the gait cycle of a sample of the elderly population on a split-belt treadmill is examined. The Openpose (OP) and Mediapipe (MP) AI pose estimation algorithms are compared to joint kinematics from a marker-based 3D motion capture system (Vicon), as well as from a video annotation tool designed for biomechanics (Kinovea). Bland-Altman (B-A) graphs and Statistical Parametric Mapping (SPM) are used to identify regions of statistically significant difference. Results: Results showed that pose estimation can achieve motion tracking comparable to marker-based systems but struggle to identify joints that exhibit small, but crucial motion. Discussion: Joints such as the ankle, can suffer from misidentification of their anatomical landmarks. Manual tools don't have that problem, but the user will introduce a static offset across the measurements. It is proposed that an AI-powered video annotation tool that allows the user to correct errors would bring the benefits of pose estimation to professionals at a low cost.