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BACKGROUND: The assessment of asymmetric gait quality plays a pivotal role in guiding rehabilitation training; however, the link between gait quality and kinematic-kinetic gait parameters remains ambiguous. OBJECTIVE: To formulate a machine-learning model for evaluating gait quality based on gait parameters, identify factors sensitive to gait quality from asymmetric gait parameters, investigate the relationship between gait indicators and gait quality, and provide guidance for asymmetric gait training and rehabilitation. METHODS: An asymmetric gait database was established through the creation of asymmetric conditions. Kinematic and kinetic data were collected from 8 young and 8 elderly subjects (all male, right dominant population) during gait tests. Gait quality for each test data set was assessed using symmetry indices, resulting in the creation of a gait parameter-gait quality dataset. Utilizing the Random Forest algorithm, a gait quality evaluation model was developed and key quality parameter factors were identified through differential analysis. This model was iteratively refined. The model’s performance was evaluated through 10- fold cross-validation, and its effectiveness was verified using the cross-validation dataset. RESULTS AND CONCLUSION: (1) A gradient test was designed to categorize gait quality into optimal, suboptimal, intermediate, and poor groups, with 759, 329, 133, and 125 instances, respectively. (2) The application of the Random Forest algorithm in gait quality assessment was explored. A relationship model was established between gait indicators and gait quality, yielding a predictive model accuracy of 95.99%. (3) The 13 main parameters significantly influencing asymmetric gait quality were identified through the Random Forest model’s feature importance ranking. (4) An analysis of gait quality sensitivity factors using the 13 important parameters led to the identification of five key sensitivity indexes. The Random Forest model utilizing these sensitivity factors achieved a predictive accuracy of 94.20%. [ABSTRACT FROM AUTHOR]

BACKGROUND: Stroke is one of the major diseases endangering the health of Chinese citizens, characterized by high morbidity, disability, mortality and recurrence. Gait dysfunction or impairment is considered to be one of the most common and devastating physiological consequences of stroke. Active lower extremity rehabilitation can promote the recovery of gait function, improve the ability to perform activities of daily living, and improve the quality of life of patients. OBJECTIVE: To review and summarize research findings applicable to post-stroke gait quantification and analysis, with a focus on the latest gait analysis system technologies, post-stroke gait data processing and analysis techniques, and their feasibility and potential value in the clinical setting. METHODS: Search terms included “stroke, gait analysis, assessment, lower limb, spatiotemporal, kinematics, kinetics, plantar pressure, electromyography (EMG), machine learning, statistics” in Chinese and English. CNKI and PubMed databases were searched for relevant articles published from January 2000 to December 2021. A few of classical distant documents were also included. The initial screening was performed by reading the titles and abstracts of the articles. Duplicate studies, low-quality journals and irrelevant documents were excluded and 62 articles were finally included for review. RESULTS AND CONCLUSION: Traditional qualitative gait analysis is primarily based on observational gait and is therefore subjective and largely influenced by the observer’s experience. Quantitative gait analysis, on the other hand, provides measured parameters with good accuracy and repeatability for diagnostic and comparative assessment throughout the rehabilitation process. The rapid development of smart wearable technologies and artificial intelligence is increasingly drawing more attention to gait research. Although their use in clinical settings is not yet well exploited, these tools are expected to change the status quo in stroke gait quantification by providing the means to acquire, store, and analyze multifactorial complex gait data while capturing its nonlinear dynamic variability and providing the valuable benefit of predictive analysis. Gait analysis is often characterized by a number of specific parameters that describe normal or abnormal gait, including temporal, kinematic, kinetic, and electromyographic parameters. Understanding the factors associated with walking function can help clinicians and researchers identify gait-related parameters that should focus on when assessing walking function. As conventional statistical methods are no longer adequate to handle the large data volumes with high heterogeneity and complexity generated by instrumented gait analysis, and gait analysis involves a large number of interdependent parameters that are difficult to interpret due to the large amount of data and their interrelationships. To simplify the evaluation, the application of machine learning to gait analysis is a promising solution. [ABSTRACT FROM AUTHOR]

Mobile, sensor-based gait analysis in Parkinson’s disease (PD) facilitates the objective measurement of gait parameters in cross-sectional studies. Besides becoming outcome measures for clinical studies, the application of gait parameters in personalized clinical decision support is limited. Therefore, the aim of this study was to evaluate whether the individual response of PD patients to dopaminergic treatment may be measured by sensor-based gait analysis. 13 PD patients received apomorphine every 15 min to incrementally increase the bioavailable apomorphine dose. Motor performance (UPDRS III) was assessed 10 min after each apomorphine injection. Gait parameters were obtained after each UPDRS III rating from a 2 × 10 m gait sequence, providing 41.2 ± 9.2 strides per patient and injection. Gait parameters and UPDRS III ratings were compared cross-sectionally after apomorphine titration, and more importantly between consecutive injections for each patient individually. For the individual response, the effect size Cohen’s d for gait parameter changes was calculated based on the stride variations of each gait sequence after each injection. Cross-sectionally, apomorphine improved stride speed, length, gait velocity, maximum toe clearance, and toe off angle. Between injections, the effect size for individual changes in stride speed, length, and maximum toe clearance correlated to the motor improvement in each patient. In addition, significant changes of stride length between injections were significantly associated with UPDRS III improvements. We therefore show, that sensor-based gait analysis provides objective gait parameters that support clinical assessment of individual PD patients during dopaminergic treatment. We propose clinically relevant instrumented gait parameters for treatment studies and especially clinical care. [ABSTRACT FROM AUTHOR]

Leg problems have become more frequent in fast-growing turkeys. The objective of this study was to evaluate the effects of common leg defects on kinetic parameters of gait and biomechanical properties of bone. Nine hundred, day-old, male, Large White turkeys were raised in 48 floor pens. At 42 d of age, turkeys were divided into four categories of leg condition as determined by visual evaluation: Normal, Crooked toes, Shaky legs, and Valgus. Fifteen toms were selected from each group and trained to walk on a pressure sensitive walkway. Gait kinetic data were collected at 92, 115 and 144 d of age. At 145 d of age, turkeys were sacrificed and bones were collected and frozen until analysis. Morphological measurements of femur, tibia and tarsus-metatarsus were recorded. Bone mineral density (BMD) and content (BMC) were obtained using DEXA. Bone strength of tibias was evaluated using a four-point bending test and femurs with a torsion test. ANOVA was used to detect differences among groups, and Tukey's test used for mean separation. There were no differences in BW among different leg conditions. Gait parameters changed as turkeys aged, and age-group interactions were observed on peak vertical force (PVF), contact time, step length (SL) and bipedal cycle. No differences (P > 0.05) were detected in morphological measurements of femur or tibia. Relative asymmetry of femur length was lower (P < 0.05) in Normal and Valgus turkeys than in toms with Crooked toes. There were no differences (P > 0.05) among groups for femur BMD, BMC or strength. Tibia BMD and the area moment of inertia of turkeys with Crooked toes were lower (P < 0.05) than in toms with Valgus. With all four leg conditions, femur strength was positively correlated with PVF and negatively correlated with SL; BMD and BMC were correlated with tibia strength and gait kinetic parameters. In conclusion, only crooked toes caused consistent differences in gait patterns, bone properties and bone strength, but in general, gait kinetics was correlated with bone biomechanics in turkeys. [ABSTRACT FROM AUTHOR]

Gait analysis is the systematic study of human motion and aims at quantifying the gait characteristics with various temporal and spatial gait parameters. Gait has proven to be linked to certain diseases, such as hemiplegic paralysis and Parkinson’s disease (PD). In this work, we present our designed and developed gait sensing platform that can capture human movement, called uGait, and we further present its application in discriminating patients with Parkinson’s disease from healthy subjects. First, we show the overall system architecture of uGait. Second, we detail how to extract and select meaningful spatial-temporal gait parameters and present an effective wrapper-based feature selection method. Typically, we consider the gait parameters associated with the situation where one walks on a u-shape walkway rather than only on a straight walkway, and we consider the influence of human body height on gait parameters extraction. We then apply our proposed feature selector to identify a discriminant subset of gait features and construct a classifier with better generalization ability for PD detection. Finally, we collect experimental data with uGait and conduct extensive experiments with three different classifiers and three different feature selectors. Experimental results demonstrate power of the quantitative gait analysis in classifying PD patients. [ABSTRACT FROM AUTHOR]

Carpal sinus hairs on the forearms are assumed to have evolved within the stem lineage of Theria. The presence and similar position of these specialized tactile hairs in scansorial and terrestrial species as well as earlier studies on rats indicate a biological role in sensing substrate irregularities in high structured environments to ensure the dynamic stability of the body during locomotion. While these sensors were considered as one functional unit until so far, the present study deals with the biological role of the single tactile hairs of the trident, assuming a role in sensing substrate diameters and adapting limb coordination and body posture to different arboreal inclinations. To investigate the influence of each hair, we studied the locomotion of rats on poles of two different diameters whereby we selectively removed individual carpal sinus hairs. The rats walked at speeds ranging from 0.12 m/s to 0.58 m/s. Normal-light high-speed cameras and x-ray fluoroscopy visualized the hairs and body dynamics during locomotion. The time lag between first contact of the hairs to the branch until contact of the forepaw was 56–108 ms. Within this time window the pronation/supination of the paw and anterior body posture are adjusted to the substrate diameter. We presume that the most proximal sinus hair (located between the medial and lateral one) senses the paw-substrate distance through the increasing bend from its first branch-contact until the contact of the paw. The medial and the lateral hairs touch the pole sides and thereby, may collect information about the properties of the small-diameter substrate. The removal of single hairs from the group results in minor changes of kinematic parameters, but locomotor stability is seriously impaired when more than one hair is cut. The kinematic responses span from a more crouched body posture and higher forearm pronation to paw slipping, muscle tremor or complete refusal to walk on the narrow substrate. • Single carpal sinus hairs are assumed to have different biological roles. • Sensory input of carpal sinus hairs essential for dynamic stability of the trunk during walking on small diameters. • Each carpal sinus hair has its own position during substrate contact on small diameter branches. • Sensorimotor connection between sinus hairs and limbs is proposed. [ABSTRACT FROM AUTHOR]

Background: Comprehensive analysis of the inherent laws and the biomechanic principles of human walking with load carriage and building kinematics, and kinematics model of human walking with load carriage, are very meaningful for the development of devices and apparatus that are related to human walking with load carriage, such as a lower limb exoskeleton.Objective: The gait experiment of human walking with load carriage is designed and performed in this paper.Methods: The obtained video is marked and analyzed by using SIMI motion analytical software. The space motion coordinates at each body's mark point that is needed in the kinematics model of established human walking with load carriage is obtained. Based on inverse kinematics, a dynamic model of human walking with load carriage is established. The SPSS statistical analysis software is used for statistical processing for determining key gait parameters.Results: The influence of load and speed on the walking gait parameters is analyzed systematically.Conclusions: The method provides a theoretical basis for the design of an exoskeleton. [ABSTRACT FROM AUTHOR]

Highlights: [•] Carrying light objects impacted children's arm postures, with children adopting less sophisticated arms positions during object carriage. [•] Carrying light objects failed to impact walking skill parameters. [•] Arm position was related to walking skill, but only for older children and when not carrying objects. [•] Relation between arm position and walking skill parameters is impacted by object carriage, even small, light loads. [Copyright &y& Elsevier]

Background: The relationship between torsional bony deformities and rotational gait parameters has not been sufficiently investigated. This study was to investigate the degree of contribution of torsional bony deformities to rotational gait parameters in patients with diplegic cerebral palsy (CP). Methods: Thirty three legs from 33 consecutive ambulatory patients (average age 9.5 years, SD 6.9 years; 20 males and 13 females) with diplegic CP who underwent preoperative three dimensional gait analysis, foot radiographs, and computed tomography (CT) were included. Adjusted foot progression angle (FPA) was retrieved from gait analysis by correcting pelvic rotation from conventional FPA, which represented the rotational gait deviation of the lower extremity from the tip of the femoral head to the foot. Correlations between rotational gait parameters (FPA, adjusted FPA, average pelvic rotation, average hip rotation, and average knee rotation) and radiologic measurements (acetabular version, femoral anteversion, knee torsion, tibial torsion, and anteroposteriortalo-first metatarsal angle) were analyzed. Multiple regression analysis was performed to identify significant contributing radiographic measurements to adjusted FPA. Results: Adjusted FPA was significantly correlated with FPA (r=0.837, p<0.001), contralateral FPA (r=0.492, p=0.004), pelvic rotation during gait (r=-0.489, p=0.004), knee rotation during gait (r=0.376, p=0.031), and femoral anteversion (r=0.350, p=0.046). In multiple regression analysis, femoral anteversion (p=0.026) and tibial torsion (p=0.034) were found to be the significant contributing structural deformities to the adjusted FPA (R2=0.247). Conclusions: Femoral anteversion and tibial torsion were found to be the significant structural deformities that could affect adjusted FPA in patients with diplegic CP. Femoral anteversion and tibial torsion could explain only 24.7% of adjusted FPA. [ABSTRACT FROM AUTHOR]

Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life. [ABSTRACT FROM AUTHOR]