Sagittal range of motion of the thoracic spine using inertial tracking device and effect of measurement errors on model predictions

Marker-Based Versus IMU-Based Kinematics for Estimates of Lumbar Spine Loads Using a Full-Body Musculoskeletal Model

Musculoskeletal modeling, typically implemented using marker-based systems in laboratory environments, is commonly used for noninvasive estimations of loads. Inertial measurement units (IMUs) have become an alternative for the evaluation of kinematics. However, estimates of spine joint contact forces using IMUs have yet to be thoroughly evaluated. Dynamics tasks and static postures from activities of daily living were captured on 11 healthy subjects using both systems simultaneously. Spine kinematics obtained from IMU- and marker-based systems and L4-L5 joint contact forces were compared. Lateral bending resulted in a weak agreement with significant differences between the 2 systems (P = .02, average root mean-squared error = 4.81), whereas flexion-extension and axial rotation exhibited the highest agreement with no significant differences (P < .05, average root mean-squared error = 5.51 and P < .31, average root mean-squared error = 5.08, respectively). All tasks showed excellent correlations (R2 = .76-.99) in estimated loads between systems. Differences in predicted loads at the L4-L5 were only observed during flexion-extension (1041 N vs 947 N, P = .0004) and walking with weights (814 N vs 727 N, P = .004). Different joint reaction force outcomes were obtained in 2 of the 8 tasks between systems, suggesting that IMUs can be robust tools allowing for convenient and less expensive evaluations and for longitudinal assessments inside and outside the laboratory setting.

An artificial neural network for full-body posture prediction in dynamic lifting activities and effects of its prediction errors on model-estimated spinal loads

Musculoskeletal models have indispensable applications in occupational risk assessment/management and clinical treatment/rehabilitation programs. To estimate muscle forces and joint loads, these models require body posture during the activity under consideration. Posture is usually measured via video-camera motion tracking approaches that are time-consuming, costly, and/or limited to laboratories. Alternatively, posture-prediction tools based on artificial intelligence can be trained using measured postures of several subjects performing many activities. We aimed to use our previous posture-prediction artificial neural network (ANN), developed based on many measured static postures, to predict posture during dynamic lifting activities. Moreover, effects of the ANN posture-prediction errors on dynamic spinal loads were investigated using subject-specific musculoskeletal models. Seven individuals each performed twenty-five lifting tasks while their full-body three-dimensional posture was measured by a 10-camera Vicon system and also predicted by the ANN as functions of the hand-load positions during the lifting activities. The measured and predicted postures (i.e., coordinates of 39 skin markers) and their model-estimated L5-S1 loads were compared. The overall root-mean-squared-error (RMSE) and normalized (by the range of measured values) RMSE (nRMSE) between the predicted and measured postures for all markers/tasks/subjects was equal to 7.4 cm and 4.1 %, respectively (R2 = 0.98 and p < 0.05). The model-estimated L5-S1 loads based on the predicted and measured postures were generally in close agreements as also confirmed by the Bland-Altman analyses; the nRMSE for all subjects/tasks was < 10 % (R2 > 0.7 and p > 0.05). In conclusion, the easy-to-use ANN can accurately predict posture in dynamic lifting activities and its predicted posture can drive musculoskeletal models.

Multi-task artificial neural networks and their extrapolation capabilities to predict full-body 3D human posture during one- and two-handed load-handling activities

Machine-learning based human posture-prediction tools can potentially be robust alternatives to motion capture measurements. Existing posture-prediction approaches are confined to two-handed load-handling activities performed at heights below 120 cm from the floor and to predicting a limited number of body-joint coordinates/angles. Moreover, the extrapolating power of these tools beyond the range of the input dataset they were trained for (e.g., for underweight, overweight, or left-handed individuals) has not been investigated. In this study, we trained/validated/tested two posture-prediction (for full-body joint coordinates and angles) artificial neural networks (ANNs) using both 70%/15%/15% random-hold-out and leave-one-subject-out methods, based on a comprehensive kinematic dataset of forty-one full-body skin markers collected from twenty right-handed normal-weight (BMI = 18-26 kg/m2) subjects. Subjects performed 204 one- and two-handed unloaded activities at different vertical (0 to 180 cm from the floor) and horizontal (up to 60 cm lateral and/or anterior) destinations. Subsequently, the extrapolation capability of the trained/validated/tested ANNs was evaluated using data collected from fifteen additional subjects (unseen by the ANNs); three individuals in five groups: underweight, overweight, obese, left-handed individuals, and subjects with a hand-load. Results indicated that the ANNs predicted body joint coordinates and angles during various activities with errors of ∼ 25 mm and ∼ 10°, respectively; considerable improvements when compared to previous posture-prediction ANNs. Extrapolation errors of our ANNs generally remained within the error range of existing ANNs with obesity and being left-handed having, respectively, the most and least compromising effects on their accuracy. These easy-to-use ANNs appear, therefore, to be robust alternatives to common posture-measurement approaches.

Effect of chest wall mobilization on respiratory muscle function in patients with severe chronic obstructive pulmonary disease (COPD): A randomized controlled trial

BACKGROUND

Clinical trials have demonstrated positive correlation between pulmonary function and chest wall expansion in COPD. Decrease in chest wall expansion in patients with COPD compromises rib cage mobility and functional length of respiratory muscles that ultimately jeopardize the efficacy and function of respiratory system.

METHOD

Thirty male adults (mean age: 74.97 ± 6.29) suffered with severe COPD were randomly allocated to either experimental group (chest wall mobilizations) or control group. Both groups received standardized education and walking exercise (twice/week) for 6 weeks. Patients in experimental group received additional chest wall mobilizations that include stretching and joints mobilization. Pulmonary function, respiratory muscle strength, thoracic excursion, cervical and thoracic range of movement were evaluated at baseline, post-program and at 3-month follow-up.

RESULTS

There were significantly greater improvements in respiratory muscle strength, thoracic excursion and thoracic range of movement (p < 0.01) except thoracic flexion. Lower thoracic excursion is strongly associated with increase in maximum inspiratory pressure (β = 13.64, p < 0.001) and maximum expiratory pressure (β = 16.23, p < 0.001). Thoracic range of movement especially extension (p < 0.001) and bilateral rotation (p < 0.01) exhibit a strong relationship with increase in lower thoracic excursion (adjusted R2 = 0.876) as shown in multiple regression analysis.

CONCLUSION

Additional chest wall mobilization in the rehabilitation of patients with COPD is likely to enhance thoracic extension and rotation which increase lower thoracic excursion. This significant improvement in chest expansion capacity allows respiratory muscles to work at an optimal functional length which result in greater respiratory muscle strength in patients with severe COPD.

The relationship between physical activity, structural deformity, and spinal mobility in adolescent idiopathic scoliosis patients

PURPOSE

Adolescent idiopathic scoliosis (AIS) patients experience structural spinal deformity, but the impact of AIS on physical activity is not widely studied. Reports of physical activity levels between children with AIS and their peers are mixed. This study sought to characterize the relationship between spinal deformity, spinal range of motion, and self-reported physical activity in AIS patients.

METHODS

Patients aged 11-21 completed self-reported measures of physical activity using the HSS Pedi-FABS and PROMIS Physical Activity questionnaires. Radiographic measures were obtained from standing biplanar radiographic imaging. Surface topographic (ST) imaging data was obtained using a whole-body ST scanning system. Hierarchical linear regression models analyzed the relationship between physical activity, ST, and radiographic deformity while controlling for age and BMI.

RESULTS

149 patients with AIS (mean age 14.5 ± 2.0 years, mean Cobb angle 39.7° ± 18.9°) were included. In the hierarchical regression predicting physical activity from Cobb angle, no factors were significant predictors of physical activity. When predicting physical activity from ST ROM measurements, age and BMI served as covariates. No covariates or ST ROM measurements were significant predictors of physical activity levels for either activity measure.

CONCLUSIONS

Physical activity levels of patients with AIS were not predicted by levels of radiographic deformity or surface topographic range of motion. Although patients may experience severe structural deformity and range of motion limitations, these factors do not appear to be associated with decreased physical activity level utilizing validated patient activity questionnaires.

LEVEL OF EVIDENCE

Level II.

Analysis of the Active Measurement Systems of the Thoracic Range of Movements of the Spine: A Systematic Review and a Meta-Analysis

(1) Objective: to analyze current active noninvasive measurement systems of the thoracic range of movements of the spine. (2) Methods: A systematic review and meta-analysis were performed that included observational or clinical trial studies published in English or Spanish, whose subjects were healthy human males or females ≥18 years of age with reported measurements of thoracic range of motion measured with an active system in either flexion, extension, lateral bending, or axial rotation. All studies that passed the screening had a low risk of bias and good methodological results, according to the PEDro and MINORS scales. The mean values and 95% confidence interval of the reported measures were calculated for different types of device groups. To calculate the differences between the type of device measures, studies were pooled for different types of device groups using Review Manager software. (3) Results: 48 studies were included in the review; all had scores higher than 7.5 over 10 on the PEDro and MINORs methodological rating scales, collecting a total of 2365 healthy subjects, 1053 males and 1312 females; they were 39.24 ± 20.64 years old and had 24.44 ± 3.81 kg/m² body mass indexes on average. We summarized and analyzed a total of 11,892 measurements: 1298 of flexoextension, 1394 of flexion, 1021 of extension, 491 of side-to-side lateral flexion, 637 of right lateral flexion, 607 of left lateral flexion, 2170 of side-to-side rotation, 2152 of right rotation and 2122 of left rotation. (4) Conclusions: All collected and analyzed measurements of physiological movements of the dorsal spine had very disparate results from each other, the cause of the reason for such analysis is that the measurement protocols of the different types of measurement tools used in these measurements are different and cause measurement biases. To solve this, it is proposed to establish a standardized measurement protocol for all tools.

A Wearable System Based on Multiple Magnetic and Inertial Measurement Units for Spine Mobility Assessment: A Reliability Study for the Evaluation of Ankylosing Spondylitis

Spinal mobility assessment is essential for the diagnostic of patients with ankylosing spondylitis. BASMI is a routine clinical evaluation of the spine; its measurements are made with goniometers and tape measures, implying systematic errors, subjectivity, and low sensitivity. Therefore, it is crucial to develop better mobility assessment methods. The design, implementation, and evaluation of a novel system for assessing the entire spine’s motion are presented. It consists of 16 magnetic and inertial measurement units (MIMUs) communicated wirelessly with a computer. The system evaluates the patient’s movements by implementing a sensor fusion of the triaxial gyroscope, accelerometer, and magnetometer signals using a Kalman filter. Fifteen healthy participants were assessed with the system through six movements involving the entire spine to calculate continuous kinematics and maximum range of motion (RoM). The intrarater reliability was computed over the observed RoM, showing excellent reliability levels (intraclass correlation >0.9) in five of the six movements. The results demonstrate the feasibility of the system for further clinical studies with patients. The system has the potential to improve the BASMI method. To the best of our knowledge, our system involves the highest number of sensors, thus providing more objective information than current similar systems.

Spinal segment ranges of motion, movement coordination, and three-dimensional kinematics during occupational activities in normal-weight and obese individuals

Measurements of spinal segment ranges of motion (RoMs), movement coordination, and three-dimensional kinematics during occupational activities have implications in occupational/clinical biomechanics. Due to the large amount of adipose tissues, obese individuals may have different RoMs, lumbopelvic coordination, and kinematics than normal-weight ones. We aimed to measure/compare trunk, lumbar, and pelvis primary RoMs in all anatomical planes/directions, lumbopelvic ratios (lumbar to pelvis rotations at different trunk angles) in all anatomical planes/directions and three-dimensional spine kinematics during twelve symmetric/asymmetric statics load-handling activities in healthy normal-weight and obese individuals. Kinematics/motion data were collected from nine healthy young male normal-weight and nine age/height/sex matched obese individuals via a ten-camera Vicon motion capture system. Obese individuals had significantly smaller (p < 0.05) lumbar flexion (~9° in average) and larger pelvis right lateral bending (~5°) RoMs as well as smaller lumbopelvic ratios (~37%) in lateral bending and axial rotation movements as compared to normal-weight individuals. Moreover, the two groups had generally non-significant different segmental orientations (<20° and in most cases < 10°) in load-handling tasks that depended on the magnitude of load asymmetry angle (p < 0.05). Differences were larger for tasks performed near the floor, away from body, and at larger load asymmetry angles. Biomechanical models simulating pure lateral bending, axial rotation, or tasks involving large load asymmetry may therefore need subject-specific, rather than population-based, motion analysis due to the effects from body weight. In clinical applications, it should be noted that healthy obese individuals may have different RoMs and lumbopelvic rhythms than healthy normal-weight individuals in some anatomical planes/directions.

Evaluation of spinal posture during gait with inertial measurement units

The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt-twist method was adopted to evaluate flexion-extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context.

Application-Based Production and Testing of a Core-Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles

Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core–sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the development of a smart sleeveless shirt for measuring the kinematic angles of the trunk relative to the pelvis in complicated three-dimensional movements. The sensor’s piezoresistive properties and characteristics were studied with respect to the type of core material used. Sensor performance was optimized by straining above the intended working region to increase the consistency and accuracy of the piezoresistive sensor. The accuracy of the sensor when tracking random movements was tested using a rigorous 4-h random wave pattern to mimic what would be required for satisfactory use in prototype devices. By processing the raw signal with a machine learning algorithm, we were able to track a strain of random wave patterns to a normalized root mean square error of 1.6%, highlighting the consistency and reproducible behavior of the relatively simple sensor. Then, we evaluated the performance of these sensors in a prototype motion capture shirt, in a study with 12 participants performing a set of eight different types of uniaxial and multiaxial movements. A machine learning random forest regressor model estimated the trunk flexion, lateral bending, and rotation angles with errors of 4.26°, 3.53°, and 3.44° respectively. These results demonstrate the feasibility of using smart textiles for capturing complicated movements and a solution for the real-time monitoring of daily activities.

Coupled artificial neural networks to estimate 3D whole-body posture, lumbosacral moments, and spinal loads during load-handling activities

Biomechanical modeling approaches require body posture to evaluate the risk of spine injury during manual material handling. The procedure to measure body posture via motion-analysis techniques as well as the subsequent calculations of lumbosacral moments and spine loads by, respectively, inverse-dynamic and musculoskeletal models are complex and time-consuming. We aim to develop easy-to-use yet accurate artificial neural networks (ANNs) that predict 3D whole-body posture (ANN_posture), segmental orientations (ANN_angle), and lumbosacral moments (ANN_moment) based on our measurements during load-handling activities. Fifteen individuals each performed 135 load-handling activities by reaching (0 kg) or handling (5 and 10 kg) weights located at nine different horizontal and five vertical (0, 30, 60, 90, and 120 cm from the floor) locations. Whole-body posture was measured via a motion capture system and lumbosacral moments were calculated via a 3D top-down eight link-segment inverse-dynamic model. ANN_posture, ANN_angle, and ANN_moment were trained (RMSEs = 6.7 cm, 29.8°, and 16.2 Nm, respectively) and their generalization capability was tested (RMSE = 7.0 cm and R² = 0.97, RMSE = 29.9° and R² = 0.85, and RMSE = 16.5 Nm and R² = 0.97, respectively). These ANNs were subsequently coupled to our previously-developed/validated ANN_load, which predicts spinal loads during 3D load-handling activities. The results showed outputs of the coupled ANNs for L4-L5 intradiscal pressure (IDPs) during a number of activities were in agreement with measured IDPs (RMSE = 0.37 MPa and R² = 0.89). Hence, coupled ANNs were found to be robust tools to evaluate posture, lumbosacral moments, spinal loads, and thus risk of injury during load-handling activities.

Thoracic spine disc degeneration, translation, and angular motion: An analysis using thoracic spine kinematic MRI (kMRI)

The aim of this study was to evaluate disc degeneration and kinematic changes in translation and angular motion of the thoracic spine using kinematic MRI (kMRI). 105 thoracic spine kMRI were analyzed from T4-5 to T11-12 using MRAnalyzer3. Translational and angular motion were evaluated in neutral, flexion, and extension positions. Thoracic disc height and disc degeneration grading were measured in the neutral position. Intraclass Correlation Coefficients were used to analyze agreement among three observers. The Friedman's test was used to analyze the difference in disc height, disc degeneration, translational motion, and angular motion. The Wilcoxon-signed rank test was used for post-hoc analysis with a Bonferroni correction. A p-value of 0.00625 was used to establish a statistically significant difference. Analysis using the Friedman's test revealed that translational motion, disc height, and disc degeneration were significantly different from T4-5 to T11-12 (p < 0.001). The T4-5 level showed the least translational motion, while the T10-11 showed the most translational motion. The lower thoracic level (T8-12) showed significantly more translational motion, more advanced disc degeneration, and greater disc height than the upper thoracic level (T4-8, p < 0.001). T11-12 showed the most advanced disc degeneration. There was a significant negative correlation between disc degeneration and translational motion at the upper thoracic level (p = 0.013). The lower thoracic region (below T8) had significantly more translational motion, more advanced disc degeneration, and greater disc height. This information is crucial in further understanding thoracic spinal kinematics and may contribute to determining the stopping level in fusion surgeries involving the thoracic spine.

Cross-correlation between spine and hip joint kinematics differs in healthy individuals and subgroups of ankylosing spondylitis patients during trunk lateral flexion

BACKGROUND

The effects of sacroiliitis and syndesmophyte formation on the cross-correlation between spine and hip joint kinematics in ankylosing spondylitis (AS) are poorly understood.

OBJECTIVE

To investigate the cross-correlation between spine and hip joint kinematics differs in healthy individuals and ankylosing spondylitis patients during trunk lateral flexion.

METHODS

Fifty AS patients and thirty-nine healthy adults (controls) were recruited from a medical center. The patients were divided into two subgroups, namely the sacroiliitis (n = 28) and syndesmophyte (n = 22) subgroups. An inertial motion system was used to record kinematic data of spine, pelvic and hip joints during lateral trunk flexion. The maximal cross-correlation coefficient (CCF) and time lag of motion between the spine and hip joint were analyzed.

RESULTS

The syndesmophyte group had the smallest range of motion in all recorded motion. The sacroiliitis group exhibited higher thoracic flexion, pelvic pitch, and pelvic rotation than the other two groups. In the syndesmophyte group, the CCF between lumbar lateral flexion (LLF) and hip abduction were weakly and LLF and hip rotation were strongly correlated. Considering in time sequence, LLF occurred earlier than hip abduction and hip rotation during trunk lateral flexion; however, both AS subgroups exhibited longer time lags than in the control group.

CONCLUSION

The cross-correlation between spine and hip joint kinematics differs in healthy individuals and AS patients during trunk lateral flexion. The motion pattern changes in patients with AS of differing severity may also alter the loads on the spine and hip joints.

The shape and mobility of the thoracic spine in asymptomatic adults - A systematic review of in vivo studies

A comprehensive knowledge of the thoracic shape and kinematics is essential for effective risk prevention, diagnose and proper management of thoracic disorders and assessment of treatment or rehabilitation strategies as well as for in silico and in vitro models for realistic applications of boundary conditions. After an extensive search of the existing literature, this study summarizes 45 studies on in vivo thoracic kyphosis and kinematics and creates a systematic and detailed database. The thoracic kyphosis over T1-12 determined using non-radiological devices (34°) was relatively less than measured using radiological devices (40°) during standing. The majority of kinematical measurements are based on non-radiological devices. The thoracic range of motion (RoM) was greatest during axial rotation (40°), followed by lateral bending (26°), and flexion (21°) when determined using non-radiological devices during standing. The smallest RoM was identified during extension (13°). The lower thoracic level (T8-12) contributed more to the RoM than the upper (T1-4) and middle (T4-8) levels during flexion and lateral bending. During axial rotation and extension, the middle level (T4-8) contributed the most. Coupled motion was evident, mostly during lateral bending and axial rotation. With aging, the thoracic kyphosis increased by about 3° per decade, whereas the RoM decreased by about 5° per decade for all load directions. These changes with aging mainly occurred in the lower region (T6-12). The influence of sex on thoracic kyphosis and the RoM has been described as partly contradictory. Obesity was found to decrease the thoracic RoM. Studies comparing standing, sitting and lying reported the effect of posture as significant.

Novel Flexible Wearable Sensor Materials and Signal Processing for Vital Sign and Human Activity Monitoring

Advances in flexible electronic materials and smart textile, along with broad availability of smart phones, cloud and wireless systems have empowered the wearable technologies for significant impact on future of digital and personalized healthcare as well as consumer electronics. However, challenges related to lack of accuracy, reliability, high power consumption, rigid or bulky form factor and difficulty in interpretation of data have limited their wide-scale application in these potential areas. As an important solution to these challenges, we present latest advances in novel flexible electronic materials and sensors that enable comfortable and conformable body interaction and potential for invisible integration within daily apparel. Advances in novel flexible materials and sensors are described for wearable monitoring of human vital signs including, body temperature, respiratory rate and heart rate, muscle movements and activity. We then present advances in signal processing focusing on motion and noise artifact removal, data mining and aspects of sensor fusion relevant to future clinical applications of wearable technology.