Motion Analysis System Research Articles

RGB-Depth Camera-Based Assessment of Motor Capacity: Normative Data for Six Standardized Motor Tasks.

Instrumental motion analysis constitutes a promising development in the assessment of motor function in clinical populations affected by movement disorders. To foster implementation and facilitate interpretation of respective outcomes, we aimed to establish normative data of healthy subjects for a markerless RGB-Depth camera-based motion analysis system and to illustrate their use. We recorded 133 healthy adults (56% female) aged 20 to 60 years with an RGB-Depth camera-based motion analysis system. Forty-three spatiotemporal parameters were extracted from six short, standardized motor tasks-including three gait tasks, stepping in place, standing-up and sitting down, and a postural control task. Associations with confounding factors, height, weight, age, and sex were modelled using a predictive linear regression approach. A z-score normalization approach was provided to improve usability of the data. We reported descriptive statistics for each spatiotemporal parameter (mean, standard deviation, coefficient of variation, quartiles). Robust confounding associations emerged for step length and step width in comfortable speed gait only. Accessible normative data usage was lastly exemplified with recordings from one randomly selected individual with multiple sclerosis. We provided normative data for an RGB depth camera-based motion analysis system covering broad aspects of motor capacity.

Agreement between Azure Kinect and Marker-Based Motion Analysis during Functional Movements: A Feasibility Study.

(1) Background: The present study investigated the agreement between the Azure Kinect and marker-based motion analysis during functional movements. (2) Methods: Twelve healthy adults participated in this study and performed a total of six different tasks including front view squat, side view squat, forward reach, lateral reach, front view lunge, and side view lunge. Movement data were collected using an Azure Kinect and 12 infrared cameras while the participants performed the movements. The comparability between marker-based motion analysis and Azure Kinect was visualized using Bland-Altman plots and scatter plots. (3) Results: During the front view of squat motions, hip and knee joint angles showed moderate and high level of concurrent validity, respectively. The side view of squat motions showed moderate to good in the visible hip joint angles, whereas hidden hip joint angle showed poor concurrent validity. The knee joint angles showed variation between excellent and moderate concurrent validity depending on the visibility. The forward reach motions showed moderate concurrent validity for both shoulder angles, whereas the lateral reach motions showed excellent concurrent validity. During the front view of lunge motions, both the hip and knee joint angles showed moderate concurrent validity. The side view of lunge motions showed variations in concurrent validity, while the right hip joint angle showed good concurrent validity; the left hip joint showed poor concurrent validity. (4) Conclusions: The overall agreement between the Azure Kinect and marker-based motion analysis system was moderate to good when the body segments were visible to the Azure Kinect, yet the accuracy of tracking hidden body parts is still a concern.

Selected Spatiotemporal and Joint Angle Parameters in Normal Gait and Nordic Walking with Classical and Mechatronic Poles in Aspects of Sex Differences.

The aim of this study was to compare selected spatiotemporal parameters and changes in the range of motion in the joints of lower and upper limbs during normal gait and during Nordic walking performed with classical and mechatronic poles of females and males. The study involved 19 physical education students (11 males and 8 females). The MyoMotion research motion analysis system was used to collect gait kinematic variables. The subject task was to cover a 100 m distance in a straight line with three types of gait: gait without poles, gait with classical poles, and gait with mechatronic poles at preferred velocity. Parameters were measured both on the right (RT) and on the left side (LT) of the body. The data was analyzed using two-way repeated measures ANOVA with the between-subject factor "sex." Friedman's test was used when necessary. The most significant differences in spatiotemporal parameters between males and females were revealed in gait with the classical and mechatronic pole (stance phase LT and RT, load response LT and RT, single support LT and RT, preswing LT and RT, swing phase LT and RT, double stance LT and RT, and step length LT), the least in gait without a pole (stance phase RT, load response LT, single support LT, preswing RT, and swing phase RT); whereas, the most significant differences in kinematic parameters were revealed in gait without poles (shoulder rotation RT, wrist radial-ulnar LT, hip flexion-extension LT and RT, knee flexion-extension LT and RT, ankle inversion-eversion LT, and ankle abduction-adduction LT and RT), the least in gait with mechatronic poles (knee flexion-extension LT and RT, ankle dorsiflexion-plantarflexion LT, ankle inversion-eversion LT, and ankle abduction-adduction LT and RT). Statistical analysis revealed many differences in spatiotemporal and kinematic parameters in normal gait, as well as in gait with the classical and mechatronic poles, which allows the conclusion that the gait of females and males should be analyzed separately.

Effects of Fatigue in Lower Back Muscles on Basketball Jump Shots and Landings

<strong>Introduction:</strong> The aim of this study was to explore effects of fatigue in lower back muscles on the performance of countermovement jumps (CMJ), jump shots (JS) and landing impact. <strong>Methods:</strong> Twelve male healthy collegiate basketball players (age: 22.8 ± 2.7 yrs, stature: 178.0 ± 5.1 cm, body mass: 74.5 ± 6.8 kg) volunteered to participate in this study. Data were collected utilizing a 3D motion analysis system, force plate, and surface electromyography. Field-goal percentage, the lowest point of the center of mass (CoM), the angles of joints during taking off phase and landing, and the electromyography parameters of the rectus femoris, erector spinae lumbalis, and of the lower limbs when performing CMJ and JS were recorded. Lower back muscle fatigue intervention was introduced and followed by a post-test to explore the effects of fatigue in the lower back muscles. Statistical analysis was performed using paired-samples <em>t</em>-test. <strong>Results:</strong> After lower back muscle fatigue, field-goal percentages dropped, and lowest point of CoM was increased during JS. Ankle plantar flexion of the hopping leg during CMJ and JS increased, and their knee flexion angles of the hopping leg were reduced when landing. CMJ and JS changed the contribution ratio of both legs after low back fatigue. <strong>Conclusions:</strong> Temporary low back muscles fatigue decreased the athletes’ performance and causes a change in landing strategy.

CONVERGENT VALIDITY AND RELIABILITY OF A NEW 2D MOTION ANALYSIS SYSTEM FOR QUANTIFICATION OF HIP AND KNEE KINEMATICS DURING OVER-GROUND RUNNING

Introduction: Analysis of running gait is necessary for prevention of hip and knee injuries. Two dimensional (2D) motion analysis systems are being widely used at a clinical level for quantication of kinematic parameters during running gait analysis. Observational methods of assessing gait are largely dependent on clinicians' ability to quantify and analyze the kinematic data in a reliable manner. Under these circumstances, use of motion analysis software with inbuilt running gait analysis protocols help to improve the reliability of data. To investigate the convergent validity, Objective: inter- and intra-rater reliability of a 2-D motion analysis system (GaitON®) for quantication of hip and knee kinematics at initial contact during running. 21 healthy male recreational runners were Methods: recorded using a clinical 2D video setup during over-ground running. Two blinded raters quantied joint angles on video using GaitON® and Kinovea software. The intra-rater reliability [intra-class correlation coefcient (ICC 3, 1)], interrater reliability, the limits of agreement (LOA) in the Bland-Altman plot were also determined. The a Results: ngles quantied using GaitON® were signicantly correlated with the angles quantied in Kinovea with r = 0.997; p = 0.01 for the hip and r = 0.982; p = 0.01 for the knee. The ICC values (intra-rater reliability) for the hip and knee were 0.998 and 0.990 respectively whereas ICC values (inter-rater reliability) of 0.996 and 0.978 were computed for the hip and knee respectively. The st Conclusion: rong validity and excellent inter- and intra-rater reliability results establish a concrete foundation for use of GaitON® as a tool for evaluating the running gait of a patient in a clinical setup.

A novel gait analysis system for detecting abnormal hemiparetic gait patterns during robot-assisted gait training: A criterion validity study among healthy adults.

Robot-assisted gait training has been reported to improve gait in individuals with hemiparetic stroke. Ideally, the gait training program should be customized based on individuals' gait characteristics and longitudinal changes. However, a gait robot that uses gait characteristics to provide individually tailored gait training has not been proposed. The new gait training robot, "Welwalk WW-2000," permits modification of various parameters, such as time and load of mechanical assistance for a patient's paralyzed leg. The robot is equipped with sensors and a markerless motion capture system to detect abnormal hemiparetic gait patterns during robot-assisted gait training. Thus, it can provide individually tailored gait training. This study aimed to investigate the criterion validity of the gait analysis system in the Welwalk WW-2000 in healthy adults. Twelve healthy participants simulated nine abnormal gait patterns that were often manifested in individuals with hemiparetic stroke while wearing the robot. Each participant was instructed to perform a total of 36 gait trials, with four levels of severity for each abnormal gait pattern. Fifteen strides for each gait trial were recorded using the markerless motion capture system in the Welwalk WW-2000 and a marker-based three-dimensional (3D) motion analysis system. The abnormal gait pattern index was then calculated for each stride from both systems. The correlation of the index values between the two methods was evaluated using Spearman's rank correlation coefficients for each gait pattern in each participant. Using the participants' index values for each abnormal gait pattern obtained using the two motion analysis methods, the median Spearman's rank correlation coefficients ranged from 0.68 to 0.93, which corresponded to moderate to very high correlation. The gait analysis system in the Welwalk WW-2000 for real-time detection of abnormal gait patterns during robot-assisted gait training was suggested to be a valid method for assessing gait characteristics in individuals with hemiparetic stroke. [https://jrct.niph.go.jp], identifier [jRCT 042190109].

The Influence of Cell Phone Usage on Dynamic Stability of the Body During Walking.

Dual-task walking and cell phone usage, which is associated with high cognitive load and reduced situational awareness, can increase risk of a collision, a fall event, or death. The objective of this study was to quantify the effect of dual-task cell phone talking, texting, and reading while walking on spatiotemporal gait parameters; minimum foot clearance; and dynamic stability of the lower limb joints, trunk, and head. Nineteen healthy male participants walked on an instrumented treadmill at their self-selected speed as well as walking while simultaneously (1)reading on a cell phone, (2)texting, and (3)talking on a cell phone. Gait analyses were performed using an optical motion analysis system, and dynamic stability was calculated using the Maximum Lyapunov Exponent. Dual-task cell phone usage had a significant destabilizing influence on the lower limb joints during walking. Cell phone talking while walking significantly increased step width and length and decreased minimum foot clearance height (P < .05). The findings suggest that dual-task walking and cell phone conversation may present a greater risk of a fall event than texting or reading. This may be due to the requirements for more rapid information processing and cognitive demand at the expense of motor control of joint stability.

Biomechanical Characteristics of Long Stair Climbing in Healthy Young Individuals in a Real-World Study Using a Wearable Motion Analysis System

Background: Stair climbing is a part of the basic activities of daily living. Previous biomechanical analyses of stairs have been conducted in the laboratory, resulting in only a few steps. Therefore, the biomechanical characteristics of long stair climbing in the real world remain unclear. The purpose of this study was to identify differences in kinematic and kinetic in the lower limb between the beginning and end phases of long stair climbing in an outdoor environment using a wearable motion analysis system. Eight subjects (four males and four females) were included in the data analysis (age: 23.6 ± 0.5 years). The long stair was 66 consecutive steps out of 202 stone steps. A wearable motion analysis system comprised six inertial measurement units and foot pressure sensors. The maximum ankle joint flexion angle in the end phase was significantly increased more than in the beginning phase (p &lt; 0.001). On the other hand, the other kinematic, kinetic, and stair climbing speeds showed no significant difference between the phases. The findings indicated that fatigue during long stair climbing might increase ankle dorsiflexion to compensate for forwarding propulsion.

Machine learning can predict mild cognitive impairment in Parkinson's disease.

Clinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI). Finding significant features associated with PD-MCI through a machine learning approach. Patients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model. Seventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2. This study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.

A kinetic analysis of the judo osoto-gari technique: relationship to sweeping leg velocity

ABSTRACT The present study characterised supporting leg kinetics (sweeping and pivot legs) during osoto-gari and investigated the relationship between kinetic variables and sweeping leg velocity at sweep contact. Fifteen black belt judoka performed osoto-gari. Motion data were recorded using a Mac3D motion analysis system (250 Hz), and ground reaction force (GRF) data were collected using four force plates (1,000 Hz). During the swing phase, the peak anterior (r = −0.535, p = 0.040) and upward (r = −0.693, p = 0.005) GRFs generated by the sweeping leg and the peak plantar flexion moment (r = 0.548, p = 0.034) and power (r = −0.700, p < 0.005) of the sweeping leg ankle joint significantly correlated with the sweeping leg velocity at sweep contact. During the throwing phase, the peak clockwise moment (r = −0.604, p = 0.017) correlated with the peak sweeping leg velocity. The peak knee extension moment (r = 0.602, p = 0.018), hip flexion moment (r = −0.589, p = 0.021) and knee power (r = −0.618, p = 0.016) of the pivot leg also correlated with the sweeping leg velocity at sweep contact. The results indicated that exertion of the sweeping leg ankle plantar flexors positively contributed to the sweeping velocity. Increasing whole-body rotation by contracting the pivot leg knee extensors is a crucial biomechanical factor in accelerating the sweeping leg at sweep contact.

Effects of change in walking speed on time-distance parameters in post-stroke hemiplegic gait.

In stroke patients, the assessment of gait ability over time is important. For quantitative gait assessment using measuring devices, the walking speed condition for measurement is generally based on the patient's preferred walking speed or the maximum walking speed at the time of measurement. However, because walking speed often increases during the convalescent stage, understanding the effects of change in walking speed on gait when comparing the course of recovery is necessary. Although several previous studies have reported the effects of change in walking speed on gait in stroke patients, the time-distance parameters described in these reports may not be generalizable because of the small case numbers. Therefore, we measured treadmill gait at the preferred walking speed (PWS) and 1.3 times the PWS (130% PWS) in 43 post-stroke hemiplegic patients and analyzed the effects of change in walking speed on time-distance parameters. Forty-three patients with hemiplegia after a first stroke, who were able to walk on a treadmill under supervision, were recruited as subjects. Using a three-dimensional motion analysis system, treadmill gait was assessed under two conditions: PWS and 130% PWS. The primary outcome measures were the time-distance parameters, which were compared between the PWS and 130% PWS conditions. Cadence, stride length, and step length of the affected and unaffected lower limbs increased significantly at 130% PWS compared with at PWS. In terms of actual time, single stance time and initial and terminal double stance time in both affected and unaffected limbs decreased significantly at 130% PWS. In terms of relative time (% of the gait cycle), compared with PWS, relative single stance time increased significantly, whereas relative initial and terminal double stance times decreased significantly at 130% PWS in both the affected and unaffected limbs. This study on treadmill gait in patients with hemiplegia after a first stroke confirmed the effects of change in walking speed on time-distance parameters. Our results will help in the interpretation of time-distance parameters measured under different walking speed conditions.

Properties of the iliotibial band and their relationships with gait parameters among patients with knee osteoarthritis.

This study aimed to determine the thickness and stiffness of the iliotibial band(ITB) in patients with knee osteoarthritis (KOA) and to identify the gait parameters that are associated with ITB properties. Eighteen female patients with radiographically diagnosed medial KOA and knee pain (age: 69.7 ± 5.9 years, body mass index: 23.0 ± 3.1 kg/m2 ) and 22 age-matched female individuals without knee pain (age: 69.1 ± 7.0 years, body mass index: 21.6 ± 3.6 kg/m2 ) were included. Shear wave elastography images were obtained at the height of the proximal pole of the patella with the participants in the supine position, and the ITB thickness and shear wave velocity, which is a surrogate measure of stiffness, were calculated. In patients with KOA, the knee and hip joint angles and moments during walking were calculated using a motion analysis system. The shear wave velocity was significantly higher in patients with KOA than in asymptomatic adults (11.3 ± 1.0 vs. 10.0 ± 1.8 m/s, respectively; p = 0.010); however, the thickness did not differ between them (2.1 ± 0.3 vs. 2.0 ± 0.3 mm, respectively; p = 0.705). The time-integral value of the knee adduction moment (β = 0.507, p = 0.032) and maximum value of the hip flexion moment (β = 0.498, p = 0.036) were associated with the shear wave velocity. Meanwhile, no parameters were associated with the thickness. The ITB was stiffer in patients with KOA than in asymptomatic adults; such a stiffer ITB was associated with greater knee adduction and hip flexion moments during walking. Clinical Significance: Greater mechanical loading was associated with a stifferITBin patients with KOA.

The association of dynamic spinal alignment on gait endurance of patients with adult spinal deformity: a cross-sectional study.

The purpose of this study was to evaluate the gait posture of patients with adult spinal deformity (ASD) using a 3-dimensional motion analysis system (3DMAS) and to investigate whether it affects gait endurance. Fifty-one patients with ASD aged 50years or older who were admitted to our hospital between March 2016 and March 2018 were included in the study. The subjects completed the 6-min walking test, which is an indicator of gait endurance. Static standing posture was assessed by whole-spine x-ray examination (coronal cobb angle, CCA; sagittal vertical axis, SVA; pelvic tilt, PT; and pelvic incidence minus lumbar lordosis, PI-LL). In addition, the gait posture was evaluated by a 3DMAS (dynamic trunk tilt angle, DTA; and dynamic pelvic tilt angle, DPA). The relationship between standing and gait postures and gait endurance was investigated by multivariable analysis. In univariable analysis, SVA, PI-LL, and DTA were associated with gait endurance. Furthermore, in the multivariable analysis, DTA showed the strongest association among the static and dynamic parameters (R2 = 0.61, β = - 0.35, P < 0.05). An association was found between gait posture and gait endurance in patients with ASD. These findings can be useful to health care providers treating patients with ASD. It is advisable to assess the gait posture of patients with ASD because they present postural abnormalities during gait.

A literature overview of modern biomechanical-based technologies for bike-fitting professionals and coaches

The optimization of cycling position is essential to improve performance and prevent overuse injuries. Bike-fitting methods, based on biomechanical variables, have been proposed in the scientific literature. To facilitate and generalize their use, the bike-fitting industry has developed various technologies to study and analyze the cycling position. The vast majority of bike-fitting protocols are based on joint kinematics, which can be evaluated in laboratory with two- or three-dimensional motion analysis systems. Joint kinematics can also be assessed in outdoor conditions with inertial measurement units, but currently, these tools provide a limited number of variables compared to laboratory systems. In addition, the bike-fitting professional can analyse pedalling technique with pedal forces to understand the effects of the bike adjustments on pedalling effectiveness. To complete the biomechanical evaluation, pressure mapping sensors allow for the measurement of the pressure load and distribution on the interfaces between the cyclist and the bicycle to detect imbalances and choose bike components (e.g., saddle). To go further in the analysis, muscular activity can be assessed with surface electromyography sensors to detect imbalances or asymmetry. The aim of this literature overview is to clearly define the role of these technologies in a bike-fitting protocol and identify their characteristics and limitations while proposing perspectives for future developments. Therefore, this work is intended for bike-fitting professionals and coaches wishing to choose the most suitable technologies to study and improve the cycling position, and for the bike-fitting industry, in order to optimize existing technologies and help develop new concepts.

A kinematic-geometric model based on ankles’ depth trajectory in frontal plane for gait analysis using a single RGB-D camera

The emergence of RGB-D cameras and the development of pose estimation algorithms offer opportunities in biomechanics. However, some challenges still remain when using them for gait analysis, including noise which leads to misidentification of gait events and inaccuracy.Therefore, we present a novel kinematic-geometric model for spatio-temporal gait analysis, based on ankles’ trajectory in the frontal plane and distance-to-camera data (depth). Our approach consists of three main steps: identification of the gait pattern and modeling via parameterized curves, development of a fitting algorithm, and computation of locomotive indices. The proposed fitting algorithm applies on both ankles’ depth data simultaneously, by minimizing through numerical optimization some geometric and biomechanical error functions.For validation, 15 subjects were asked to walk inside the walkway of the OptoGait, while the OptoGait and an RGB-D camera (Microsoft Azure Kinect) were both recording. Then, the spatio-temporal parameters of both feet were computed using the OptoGait and the proposed model. Validation results show that the proposed model yields good to excellent absolute statistical agreement (0.86 ≤ Rc ≤ 0.99).Our kinematic-geometric model offers several benefits: (1) It relies only on the ankles’ depth trajectory both for gait events extraction and spatio-temporal parameters’ calculation; (2) it is usable with any kind of RGB-D camera or even with 3D marker-based motion analysis systems in absence of toes’ and heels’ markers; and (3) it enables improving the results by denoising and smoothing the ankles’ depth trajectory. Hence, the proposed kinematic-geometric model facilitates the development of portable markerless systems for accurate gait analysis.

Development of a Human Motion Analysis System Based on Sensorized Insoles and Machine Learning Algorithms for Gait Evaluation

Human gait analysis can provide an excellent source for identifying and predicting pathologies and injuries. In this respect, sensorized insoles also have a great potential for extracting gait information. This, combined with mathematical techniques based on machine learning (ML), can potentialize biomechanical analyses. The present study proposes a proof-of-concept of a system based on vertical ground reaction force (vGRF) acquisition with a sensorized insole that uses an ML algorithm to identify different patterns of vGRF and extract biomechanical characteristics that can help during clinical evaluation. The acquired data from the system was clustered by an immunological algorithm (IA) based on vGRF during gait. These clusters underwent a data mining process using the classification and regression tree algorithm (CART), where the main characteristics of each group were extracted, and some rules for gait classification were created. As a result, the system proposed was able to collect and process the biomechanical behavior of gait. After the application of IA and CART algorithms, six groups were found. The characteristics of each of these groups were extracted and verified the capability of the system to collect and process the biomechanical behavior of gait, offering verification points that can help focus during a clinical evaluation.

Effects of preoperative spinopelvic compensation states on the patient-reported outcomes of adult spinal deformity surgery: three-dimensional motion analysis results.

This study aimed to investigate how preoperative motion analysis results affect the postoperative clinical outcomes of patients undergoing surgery for adult spinal deformity (ASD). Patients who underwent surgery for ASD and whose motion analysis results were available were included. All patients underwent preoperative three-dimensional gait analysis using a motion analysis system. Univariate and multivariate regression analyses were performed to determine the predictive parameters of the 12-month postoperative Oswestry Disability Index (ODI). According to the mean anterior pelvic tilt (Ant-PT) angle in the motion analysis, the patients were divided into two groups: small and high Ant-PT angle groups. The 12-month postoperative ODI between the two groups was compared. A total of 111 patients who met the inclusion criteria were enrolled in the study. In the multivariate regression analysis, the preoperative sacral slope and mean Ant-PT angle were significant predictors of the 12-month postoperative ODI (p = 0.013 and p = 0.009, respectively). The high Ant-PT angle group showed a poorer 12-month postoperative ODI than did the small Ant-PT angle group, with a mean ODI of 21.9 ± 8.4 and 16.7 ± 8.5, respectively (p = 0.002). Simple linear regression analysis revealed that the Ant-PT angle was positively correlated with the postoperative sagittal vertical axis in all follow-up periods. The study highlights that a high preoperative Ant-PT angle in motion analyses is associated with poor clinical outcomes after surgery for ASD. Therefore, it is necessary to observe and consider the dynamic gait pattern related to the compensatory mechanism for sagittal imbalance in the decision-making process for ASD surgery.

Concurrent validity of DorsaVi wireless motion sensor system Version 6 and the Vicon motion analysis system during lifting

BackgroundWearable sensor technology may allow accurate monitoring of spine movement outside a clinical setting. The concurrent validity of wearable sensors during multiplane tasks, such as lifting, is unknown. This study assessed DorsaVi Version 6 sensors for their concurrent validity with the Vicon motion analysis system for measuring lumbar flexion during lifting.MethodsTwelve participants (nine with, and three without back pain) wore sensors on T12 and S2 spinal levels with Vicon surface markers attached to those sensors. Participants performed 5 symmetrical (lifting from front) and 20 asymmetrical lifts (alternate lifting from left and right). The global-T12-angle, global-S2-angle and the angle between these two sensors (relative-lumbar-angle) were output in the sagittal plane. Agreement between systems was determined through-range and at peak flexion, using multilevel mixed-effects regression models to calculate root mean square errors and standard deviation. Mean differences and limits of agreement for peak flexion were calculated using the Bland Altman method.ResultsFor through-range measures of symmetrical lifts, root mean squared errors (standard deviation) were 0.86° (0.78) at global-T12-angle, 0.90° (0.84) at global-S2-angle and 1.34° (1.25) at relative-lumbar-angle. For through-range measures of asymmetrical lifts, root mean squared errors (standard deviation) were 1.84° (1.58) at global-T12-angle, 1.90° (1.65) at global-S2-angle and 1.70° (1.54) at relative-lumbar-angle. The mean difference (95% limit of agreement) for peak flexion of symmetrical lifts, was − 0.90° (-6.80 to 5.00) for global-T12-angle, 0.60° (-2.16 to 3.36) for global-S2-angle and − 1.20° (-8.06 to 5.67) for relative-lumbar-angle. The mean difference (95% limit of agreement) for peak flexion of asymmetrical lifts was − 1.59° (-8.66 to 5.48) for global-T12-angle, -0.60° (-7.00 to 5.79) for global-S2-angle and − 0.84° (-8.55 to 6.88) for relative-lumbar-angle.ConclusionThe root means squared errors were slightly better for symmetrical lifts than they were for asymmetrical lifts. Mean differences and 95% limits of agreement showed variability across lift types. However, the root mean squared errors for all lifts were better than previous research and below clinically acceptable thresholds. This research supports the use of lumbar flexion measurements from these inertial measurement units in populations with low back pain, where multi-plane lifting movements are assessed.

Reliability and agreement of Azure Kinect and Kinect v2 depth sensors in the shoulder joint range of motion estimation.

Depth sensor-based motion analysis systems are of interest to researchers with low cost, fast analysis capabilities, and portability; thus, their reliability is a matter of interest. Our study examined the agreement and reliability in estimating the basic shoulder movements of Azure Kinect, Microsoft's state-of-the-art depth sensor, and its predecessor, Kinect v2, by comparing them with the gold standard marker-based motion analysis system. In our study, the shoulder joint ranges of motion of 20 healthy individuals were analyzed during dominant-side flexion, abduction, and rotation movements. The reliability and agreement between methods were evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman method. Compared to the gold standard method, the old- and new-generation Kinect showed similar performance in terms of reliability in the estimation of flexion (ICC=0.86 vs. 0.82) and abduction (ICC=0.78 vs. 0.79) movements, respectively. In contrast, the new-generation sensor showed higher reliability than its predecessor in internal (ICC=0.49 vs. 0.75) and external rotation (ICC=0.38 vs. 0.67) movement. Compared to its predecessor, Kinect Azure has higher reliability in analyzing movements in a lower range and variability, thanks to its state-of-the-art hardware. However, the sensor should also be tested on multiaxial movements, such as combing hair, drinking water, and reaching back, which are the tasks that simulate extremity movements in daily life.

EMG-based co-contraction indexes for the assessment of motor impairments in persons post-stroke

Neuromuscular coordination can be described by activations of agonist–antagonist (AA) muscle pairs and plays an important role in the performing of daily activities. It is an important mechanism used by the central nervous system to regulate joint stability and provide movement accuracy [1]. As a result of a stroke there is an alteration in these coordination patterns that results in motor deficits of the arm function [2]. In fact, persons post-stroke have an exaggerated AA muscle co-contraction [2] associated to a higher joint stiffness [1]. The amount of co-contraction can be quantified through the use of EMG- based co-contraction indexes (CCIs) [1,2]. Therefore, the purpose of the study was to evaluate whether such EMG-based CCIs are able to assess impairment in the motor control of the upper limb in persons post-stroke. Thirty-five persons post-stroke, in chronic or sub-acute stages, were recruited in this study. Both paretic and non-paretic arms were evaluated during the execution of functional tasks related to activities of daily living (i.e., reaching, object placing and forearm pronation). The upper limb kinematics and EMG data of three AA muscle pairs (anterior/posterior deltoids, triceps/biceps and pronator/supinator) were recorded through a movement analysis system. Ten healthy subjects (HS) provided the normative data. The Rudolph’s CCI [1] was computed as proposed by Don (range 0-100%, higher values indicate a greater AA muscle co-contraction) [3]. All participants post-stroke were clinically assessed through the Fugl-Meyer Motor Assessment of Upper Extremity (FM-UE). The differences of Rudolph’s CCI between HS and subjects post-stroke, considering both the more affected (MA) and the less affected (LA) arm, were tested by ANOVA. Bonferroni post-hoc analysis was also used to establish if there were statistically significant differences among the three groups. A deviation from normative data for the MA CCIs was found in almost all functional tasks and antagonist muscle pairs. The LA CCIs also showed a significant deviation from HS. Such pattern is reported in Fig. 1 for Rudolph’s CCI during the forearm pronation task. Higher values of the EMG-based CCI were found both in LA and MA arms in persons post-stroke, indicating alterations in the neuromotor coordination for both sides. The altered AA muscle activation of the LA arm (non-paretic, ipsilateral to the brain lesion) in persons post-stroke, despite its kinematics not being compromised, could be a consequence of the over-use of the non-paretic limb in performing functional tasks. Therefore, also the non-paretic upper limb may benefit from rehabilitation, as recently suggested [4]. Our results show that EMG-based CCI is a quantitative and objective measure able to detect motor control impairment, not detectable by clinical scales.