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Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participants level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the bodys center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearsons r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individuals stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Incline walking with an external load is a common activity in everyday life. Asymmetrical load carriage can lead to abnormal posture and back pain. Thus, this study aimed to examine the effect of walking uphill with an asymmetrical load in two positions on the spatiotemporal parameters of gait in young adults. Forty-one asymptomatic human volunteers were enrolled in this study. They were asked to walk at a self-selected pace on level and uphill (+5° incline) surfaces carrying a backpack in two asymmetrical positions (hand and shoulder). Spatiotemporal gait parameters were recorded using a photocell device. We observed a significant effect of incline and load position on gait parameters (p < 0.05). Although adaptation to walking on inclines was similar with and without a backpack, adaptation to load position was different when the load was hand-held and shoulder-held. Asymmetric loading with different load locations should be considered an important factor influencing daily gait patterns. In the future, this relationship should be further investigated in terms of pain disorders and postural abnormalities. [ABSTRACT FROM AUTHOR]

This study presents a methodology that combines convolution features with shallow classifiers for classifying the walking pattern on different surfaces. At first, convolution features are extracted from six different inertial measurement units (IMU) sensors mounted on the human body. The shallow classifiers namely quadratic SVM, wide neural network, fine KNN, and linear discriminant analysis are trained using convolution features that successfully pass through the global pooling layer of the CNN model. The proposed methodology is also evaluated on the features extracted from both individual IMU sensor and combined IMU sensors. It is observed that proposed methodology performs better for convolution features extracted from all IMU sensors combined together. The proposed methodology is also compared to the CNN model to demonstrate its efficacy. The obtained results show that the shallow classifier (quadratic SVM) achieves the maximum accuracy of 98.2%, whereas the CNN model achieves the accuracy of 90.73% only. Thus, the proposed methodology can be successfully utilised for classifying different walking surfaces based on the gait cycle data. The proposed approach can benefit in automatic gait adjustment of prosthetic foot based on the walking surfaces. [ABSTRACT FROM AUTHOR]

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Orthotics are the body support devices used for correction, immobilization, fixation, and prevention of paralysis. The greatest number of orthotics utilized by people suffering from plantarflexion and dorsiflexion disability, especially in Indonesia, is ankle foot orthotic (AFO). However, the duration associated with fabricating AFO through conventional methods is considered time-consuming. This paper aims to fabricate ankle foot orthotics (AFO) using innovative combinations of 3D scanning and 3D printing. The method begins with 3D scanning of the patient’s lower limb using photogrammetry (3DF Zephyr). The design is generated and adjusted, afterwards, the orthotic prototype is produced by fused deposition modeling (FDM) 3D printing using polypropylene (PP) material. This choice is attributed to the material's advantages, such as being lightweight, rigid, durable, and cost-effective. The 3D mesh model scanned using 3DF Zephyr shows good quality and more precise results. In addition, the prototype produced using 3D printing was tested by walking based on normal gait analysis’s angle of foot and calf measurement, which shows a maximum range of motion (ROM) of 16.1˚. The proposed methods of fabricating orthotic prototypes can successfully reduce the processing time by approximately 70% compared to the conventional method.

The effective procedure for activating muscles and improving movements in individuals who have a central nervous system injury is Functional electrical stimulation (FES). To have accurate movements of the limbs, appropriate stimulation patterns should be generated and exerted on the muscles. The challenging problem is the redundancy in the musculoskeletal system that makes it necessary to use optimal control to derive muscle activations. For doing this, the redundant musculoskeletal model of the ankle joint is derived in this paper, then an optimal control method for estimating muscle activation during the gait cycle is designed. The aim of optimal control theory is to minimize the cost function, including muscle activation as the control input and joint angle tracking error as the output of the model. Two optimal control algorithms are implemented in this research. These approaches present direct and indirect control methods to solve the optimization problem of ankle movement achieved by experimental research on two healthy males during the gait cycle. Based on the results, the direct method is smoother for nonlinear systems and estimates desired muscle activation more precisely and the cost function coefficients are chosen with logical and less values in comparison to the indirect method.

The effective procedure for activating muscles and improving movements in individuals who have a central nervous system injury is Functional electrical stimulation (FES). To have accurate movements of the limbs, appropriate stimulation patterns should be generated and exerted on the muscles. The challenging problem is the redundancy in the musculoskeletal system that makes it necessary to use optimal control to derive muscle activations. For doing this, the redundant musculoskeletal model of the ankle joint is derived in this paper, then an optimal control method for estimating muscle activation during the gait cycle is designed. The aim of optimal control theory is to minimize the cost function, including muscle activation as the control input and joint angle tracking error as the output of the model. Two optimal control algorithms are implemented in this research. These approaches present direct and indirect control methods to solve the optimization problem of ankle movement achieved by experimental research on two healthy males during the gait cycle. Based on the results, the direct method is smoother for nonlinear systems and estimates desired muscle activation more precisely and the cost function coefficients are chosen with logical and less values in comparison to the indirect method. [ABSTRACT FROM AUTHOR]

The study of the phenomena occurring in the plantar region is remarkably intriguing, especially when performing a normal gait cycle where the foot is under loading conditions. The effects presented in the foot while walking provide relevant indicators regarding clinical means for enhancing regular performance or rehabilitation therapies. Nevertheless, more than traditional methods are needed to biomechanically evaluate foot structural conditions, leading to an incomplete database for determining the patient's needs so that advanced methodologies provide detailed medical assessment. Therefore, it is necessary to employ technological engineering tools to optimize biomechanical plantar pressure evaluations to reach suitable personalized treatments. This research initially evaluated numerically the pressure points in the foot sole region in each one of the five stance phases in a normal gait cycle. Medical imaging techniques were utilized to construct an anatomically accurate biomodel of the soft tissues of the right foot. The Finite Element Method was employed to predict peak plantar pressure in barefoot conditions for all stance phases; results from this case study presented a close alignment with gait experimental testing implemented to analyze the feasibility and validation of all mechanical considerations for the numerical analyses. Hence, having a solid foundation in the biomechanical behavior from the first case study close estimates, a 3D-printable patient-specific insole was designed and numerically analyzed to observe the mechanical response in the plantar critical zones utilizing a personalized orthotic device. Results from the second case study notably demonstrated a crucial decrement in excessive pressure values. Employing morphological customization orthopedics modeling combined with 3D-printable materials is revolutionizing assistive device design and fabrication techniques. The fundamental contribution of this research relies on deepening the knowledge of foot biomechanics from an interdisciplinary approach by numerically analyzing pressure distribution in critical regions for all five stances phases; thus, based on the methods employed, the results obtained contribute to the advances of patient-specific foot orthopedics. [ABSTRACT FROM AUTHOR]

The study of the phenomena occurring in the plantar region is remarkably intriguing, especially when performing a normal gait cycle where the foot is under loading conditions. The effects presented in the foot while walking provide relevant indicators regarding clinical means for enhancing regular performance or rehabilitation therapies. Nevertheless, more than traditional methods are needed to biomechanically evaluate foot structural conditions, leading to an incomplete database for determining the patient’s needs so that advanced methodologies provide detailed medical assessment. Therefore, it is necessary to employ technological engineering tools to optimize biomechanical plantar pressure evaluations to reach suitable personalized treatments. This research initially evaluated numerically the pressure points in the foot sole region in each one of the five stance phases in a normal gait cycle. Medical imaging techniques were utilized to construct an anatomically accurate biomodel of the soft tissues of the right foot. The Finite Element Method was employed to predict peak plantar pressure in barefoot conditions for all stance phases; results from this case study presented a close alignment with gait experimental testing implemented to analyze the feasibility and validation of all mechanical considerations for the numerical analyses. Hence, having a solid foundation in the biomechanical behavior from the first case study close estimates, a 3D-printable patient-specific insole was designed and numerically analyzed to observe the mechanical response in the plantar critical zones utilizing a personalized orthotic device. Results from the second case study notably demonstrated a crucial decrement in excessive pressure values. Employing morphological customization orthopedics modeling combined with 3D-printable materials is revolutionizing assistive device design and fabrication techniques. The fundamental contribution of this research relies on deepening the knowledge of foot biomechanics from an interdisciplinary approach by numerically analyzing pressure distribution in critical regions for all five stances phases; thus, based on the methods employed, the results obtained contribute to the advances of patient-specific foot orthopedics.

Gait analysis is a widely used technique for passive human identification and tracking, with potential applications in security and surveillance systems. However, existing gait recognition methods face challenges in handling changing angles and uncertain features. In this paper, we propose a novel gait recognition approach that leverages real-time spatio-temporal gait features, including step length, gait cycle, height, cadence, swing ratio, and foot length. We apply the Extreme Learning Machines (ELM) algorithm for classification, which has been shown to be effective in various applications due to its fast-learning speed and good generalization performance. To further enhance the recognition rate, we introduce an evolutionary BAT-optimized ELM algorithm that addresses the instability issue in ELM. The proposed BAT-ELM algorithm can optimize the hidden nodes and weights of ELM, which leads to improved efficiency in recognizing gait from multiple view angles ranging from 0° to 180°. Our comprehensive analysis of the proposed approach indicates that it outperforms other reported algorithms in terms of recognition rate and efficiency. Our work demonstrates the effectiveness of combining real-time spatio-temporal gait features with the BAT-ELM algorithm for gait recognition. The proposed approach has potential applications in various fields, including security and surveillance systems, healthcare, and robotics. Our findings highlight the importance of leveraging evolutionary algorithms to optimize machine learning models and achieve better performance in complex recognition tasks. [ABSTRACT FROM AUTHOR]

Walking rehabilitation following injury or disease involves voluntary gait modification, yet the specific brain signals underlying this process remains unclear. This aim of this study was to investigate the impact of an auditory cue on changes in brain activity when walking overground (O) and on a treadmill (T) using an electroencephalogram (EEG) with a 32-electrode montage. Employing a between-group repeated-measures design, 24 participants (age: 25.7 ± 3.8 years) were randomly allocated to either an O (n = 12) or T (n = 12) group to complete two walking conditions (self-selected speed control (sSC) and speed control (SC)). The differences in brain activities during the gait cycle were investigated using statistical non-parametric mapping (SnPM). The addition of an auditory cue did not modify cortical activity in any brain area during the gait cycle when walking overground (all p > 0.05). However, significant differences in EEG activity were observed in the delta frequency band (0.5–4 Hz) within the sSC condition between the O and T groups. These differences occurred at the central frontal (loading phase) and frontocentral (mid stance phase) brain areas (p < 0.05). Our data suggest auditory cueing has little impact on modifying cortical activity during overground walking. This may have practical implications in neuroprosthesis development for walking rehabilitation, sports performance optimization, and overall human quality-of-life improvement. [ABSTRACT FROM AUTHOR]

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = −0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers. [ABSTRACT FROM AUTHOR]

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens. [ABSTRACT FROM AUTHOR]

Background: In older age, walking ability gradually decreases due to factors including impaired balance, reduced muscle strength, and impaired vision and proprioception. Further, cognitive functions play a key role during walking and gradually decline with age. There is greater variability in gait parameters when the demands during walking increase, in dual- and multiple-task situations. The aim of this study was to analyze gait parameters while performing a demanding cognitive and motor dual task in three different age-related healthy elderly subject groups. Method: A total of 132 healthy individuals (54 males, 78 females) were divided into three groups—55 to 65, 66 to 75, and 76 to 85 years. The subjects performed a basic walking task, dual motor task, dual mental task, and combined motor and mental task while walking. The gait parameters cycle time, stride length, swing time, and double support time were noted, as well as the variability of those parameters. Results: Cycle time was longer and stride length was shorter in the >76-year-old group than in the 51–65-year-old group in all test conditions. A comparison of all three groups did not show a significant difference in swing time, while double support time was increased in the same group. Conclusions: Changes are observed when gait is performed simultaneously with an additional motor or cognitive task. Early detection of gait disorders can help identify elderly people at increased risk of falls. Employing a dual-task paradigm during gait assessment in healthy elderly subjects may help identify cognitive impairment early in the course of the disturbance. [ABSTRACT FROM AUTHOR]

Instrumented gait analysis is an established procedure in biomechanical assessment, requiring specially-trained analysts to interpret the complex graphical output generated. Does a new method of visual representation of lower limb kinematic gait analysis data provide a reliable and valid method of interpretation of biomechanical data for healthcare professionals? An innovative system based on the Traffic Lights System (TLS) was developed. Simulated abnormal gait was captured using a 16-camera optoelectronic motion capture system, and the results were presented in both the Traditional Graphical System (TGS) format and the new TLS. An online form was filled by health professionals who attempted to interpret normal and abnormal motion in the joints presented in the 2 output formats. Out of 26 raters, 18 preferred the new system because of its user-friendliness and its ease of interpretation. 2 raters preferred the TGS, with one of these raters clarifying that the preference is due to colour blindness. For intra-rater reliability, 2 trained raters provided a second response for the TGS (Cronbach's Alpha ranging between 0.733 and 0.918), whilst the TLS resulted in Cronbach's Alpha between 0.817 and 1.00 amongst 3 untrained raters. The Fleiss Multi-rater Kappa Test demonstrated low inter-rater reliability amongst raters in the TGS, whereas the overall Fleiss Multi-rater Kappa values of the TLS surpassed the TGS in all 3 studies. This study showed that whilst trained health professionals have high intra-rater reliability in interpreting traditional gait analysis results, those professionals inexperienced in the system, do not always comprehend the complex graphs generated by the system when presenting gait analysis data. When these graphs are transformed into coloured outputs representing the extent of the movement, the TLS has demonstrated high validity and high intra- and inter-rater reliability, significantly exceeding those of the TGS, especially in untrained health professionals. • trained health professionals reliably interpret traditional gait analysis results. • inexperienced professionals do not comprehend complex graphs presenting gait analysis data. • TLS has demonstrated high validity and high intra- and inter-rater reliability. • reliability of TLS exceeded those of the TGS in untrained health professionals. [ABSTRACT FROM AUTHOR]

To improve mobility and rehabilitation, precise and adaptive control mechanisms have been developed for lower limb exoskeletons. Brain-computer interface (BCI) provides advanced and intuitive control of assistive and rehabilitation exoskeletons to aid the user. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, and portable brain imaging modality, gained momentum in rehabilitation studies in the last decade. This study provides a novel approach to control a lower limb exoskeleton with enhanced classification accuracy using fNIRS-based BCI, the k-nearest neighbors (kNN) classifier, and optimal feature combination. The brain signals were acquired using fNIRS for walking vs rest for twenty healthy participants, having ten trials for each participant. The statistical measures: mean, peak, variance, skewness, kurtosis, and slope are extracted as features. Optimal feature combination was analyzed and selected for enhanced classification accuracy. kNN was analyzed and selected as an optimal classifier with optimal ‘k’ (number of nearest neighboring data points that the kNN considers while classifying a new data point) using elbow method to improve classification performance. The proposed method achieves an average classification accuracy of $88.19~\pm ~2.55$ %, in offline configuration. In order to control exoskeleton in online settings, simulated online classification was performed using one unknown trial, fed as real-time signal. Sliding window of 2.5 sec is used and achieved average classification accuracy of 97.5%. This research represents a major advancement in user-centric assistive technologies and advances the field of neuro-powered exoskeletons. It also lays the groundwork for future advancements in the integration of neuroimaging, machine learning, and rehabilitation.

Abstract Background Reference values for normal knee kinematics were limited in Asian population and were influenced by race and other factors. This study was aimed to establish the reference values and identify the factors associated with knee kinematics in healthy Thai adults, aged 18–40 years. Methods A retrospective cohort study was conducted between 2016 and 2020. Healthy Thai adults aged 18–40 years old with body mass index (BMI) between 18.5 and 24.9 kg/m2 were included. All eligible participants were attached with reflective markers. Their walking was captured by 8-digital cameras, and assessed by motion analysis software. The primary outcomes were average knee kinematic data (degrees) in three dimensional planes as valgus-varus, flexion-extension, and internal-external rotation. Paired t-test and multiple linear regression were applied to compare the outcomes and to determine their associated factors. Results Ninety-eight participants (60 females and 38 males) were included with mean age 28.5 ± 5.4 years, and BMI 21.1 ± 2.0 kg/m2. Knee kinematics showed slight adduction during the swing phase, flexion during the stance phase, and obvious external rotation throughout the gait cycle, with a peak of 30–31 degrees during mid-swing. Right knee was significantly more adducted, flexed and externally rotated than the left side, particularly at mid-stance (P = 0.047, 0.017, and

Incline walking with an external load is a common activity in everyday life. Asymmetrical load carriage can lead to abnormal posture and back pain. Thus, this study aimed to examine the effect of walking uphill with an asymmetrical load in two positions on the spatiotemporal parameters of gait in young adults. Forty-one asymptomatic human volunteers were enrolled in this study. They were asked to walk at a self-selected pace on level and uphill (+5° incline) surfaces carrying a backpack in two asymmetrical positions (hand and shoulder). Spatiotemporal gait parameters were recorded using a photocell device. We observed a significant effect of incline and load position on gait parameters (p < 0.05). Although adaptation to walking on inclines was similar with and without a backpack, adaptation to load position was different when the load was hand-held and shoulder-held. Asymmetric loading with different load locations should be considered an important factor influencing daily gait patterns. In the future, this relationship should be further investigated in terms of pain disorders and postural abnormalities.

The aim of the study was to investigate how high-heeled walking affects the coordination changes of timing of upper trunk muscle activation, and the possible occurrence of health problems in this part of the body of young women. We used surface electromyography (EMG) for data collection. The research group consisted of 30 women. Statistical significance of the changes in muscle coordination was confirmed when evaluating two of the four upper trunk muscles studied. M. trapezius and m. pectoralis major are not subject to changes in gait in high heels (HH) from the point of view of timing on a statistical level, but HH increase the intensity of muscle contraction of all monitored muscles, and therefore we recommend limiting the wearing of HH in case of health problems related to these muscles. [ABSTRACT FROM AUTHOR]

BACKGROUND: Anterior cruciate ligament rupture is one of the common sports injuries, which is generally treated by anterior cruciate ligament reconstruction. Balance and walking abilities are often decreased after surgery, and there is a lack of comprehensive comparative study. OBJECTIVE: To explore the balance and gait characteristics of patients after anterior cruciate ligament reconstruction during static standing and walking, and make a comprehensive comparative analysis with the healthy group to provide scientific guidance for rehabilitation treatment. METHODS: Totally 23 patients 6-8 weeks after anterior cruciate ligament reconstruction and 23 healthy controls were selected. The plantar pressure plate was used to collect 95% confidence elliptical area, path length, average velocity and major and minor axis length of the plantar center of pressure during static standing. Simultaneously, the center of pressure displacement, gait line length, maximum velocity, gait phase ratio and maximum pressure in the tripod area were collected. RESULTS AND CONCLUSION: (1) Static standing balance: The center of pressure 95% confidence ellipse area, path length, average moving speed and length of major and minor axes in the anterior cruciate ligament reconstruction group were significantly higher than those in the healthy control group (P < 0.05). Simultaneously, the weight-bearing of the forefoot in the anterior cruciate ligament reconstruction group was higher than that in the healthy control group (P < 0.05). (2) Characteristics of walking balance: Compared with the healthy control group, the medial and lateral displacement of the center of pressure in the anterior cruciate ligament reconstruction group was significantly larger (P < 0.05), and there was no significant difference in anterior and posterior displacement and maximum movement speed. Compared with the affected side and the healthy side in the anterior cruciate ligament reconstruction group, the affected side and the healthy control group, and the healthy side and the healthy control group, the gait line and single support line length of the former in the three groups were smaller than those of the latter (P < 0.05). (3) Gait cycle characteristics: The stride width and double support period of the anterior cruciate ligament reconstruction group were larger than those of the healthy control group (P < 0.05). Stride length was smaller in the anterior cruciate ligament reconstruction group than that in the healthy control group (P < 0.05). Compared with the healthy side, the standing phase, single leg support period, pre-swing period and heel to the forefoot time on the affected side of the anterior cruciate ligament reconstruction group decreased (P < 0.05), while the load-bearing response period and swing period increased (P < 0.05). Compared with the healthy control group, the foot deviation angle, load-bearing response period and pre-swing period increased, and the single leg support period decreased on the affected side (P < 0.05). Compared with the healthy control group, the standing phase, load-bearing response period, pre-swing period and the time from heel to the forefoot were significantly increased, and the swing period decreased on the healthy side (P < 0.05). (4) The maximum pressure of the tripod area: Compared with the healthy side, the maximum pressure of the forefoot, arch and heel of the affected side decreased significantly (P < 0.05). Compared with the healthy control group, the maximum pressure of the tripod area of the affected side also decreased significantly (P < 0.05). Compared with the healthy control group, the maximum pressure of the forefoot of the healthy side decreased significantly (P < 0.05), but there was no significant difference between the arch and the heel. (5) The results showed that the static and dynamic balance ability of the patients 6-8 weeks after anterior cruciate ligament reconstruction was significantly lower than that of the healthy subjects, and the balance function of the healthy side also decreased significantly. During walking, the patients had gait problems such as a shortening of stride length, an increase of stride width, a decrease of support period and increase of swing period. It is suggested that attention should be paid to bilateral balance and gait training during postoperative rehabilitation. [ABSTRACT FROM AUTHOR]

Background: Reference values for normal knee kinematics were limited in Asian population and were influenced by race and other factors. This study was aimed to establish the reference values and identify the factors associated with knee kinematics in healthy Thai adults, aged 18–40 years. Methods: A retrospective cohort study was conducted between 2016 and 2020. Healthy Thai adults aged 18–40 years old with body mass index (BMI) between 18.5 and 24.9 kg/m2 were included. All eligible participants were attached with reflective markers. Their walking was captured by 8-digital cameras, and assessed by motion analysis software. The primary outcomes were average knee kinematic data (degrees) in three dimensional planes as valgus-varus, flexion-extension, and internal-external rotation. Paired t-test and multiple linear regression were applied to compare the outcomes and to determine their associated factors. Results: Ninety-eight participants (60 females and 38 males) were included with mean age 28.5 ± 5.4 years, and BMI 21.1 ± 2.0 kg/m2. Knee kinematics showed slight adduction during the swing phase, flexion during the stance phase, and obvious external rotation throughout the gait cycle, with a peak of 30–31 degrees during mid-swing. Right knee was significantly more adducted, flexed and externally rotated than the left side, particularly at mid-stance (P = 0.047, 0.017, and < 0.001, respectively). Females had more knee abduction, flexion and external rotation than males. Age, sex, and BMI were significantly correlated with knee abduction at terminal stance (correlation coefficient − 0.12, 95% confidence interval (CI) -0.23, -0.01; -1.37, 95%CI -2.54, -0.20; and − 0.32, 95%CI -0.61, -0.39, respectively), and rotation at mid-swing (correlation coefficient − 0.36, 95%CI -0.69, -0.02; -7.37, 95%CI -10.82, -3.92; and 0.89, 95%CI 0.01, 1.78, respectively). Conclusion: Knee kinematics demonstrates external tibial rotation throughout the gait cycle, significant side differences, and are associated with age, sex, and BMI. Reference values from this study will be useful for functional gait assessment in healthy Thais. However, further comprehensive knee kinetic study including spatio-temporal parameter is recommended. [ABSTRACT FROM AUTHOR]

Lower limb amputees face significant challenges in day-to-day life for locomotion. A prosthetic foot is used to restore mobility. The push-off effect from the prosthetic foot plays a vital role as it reduces gait asymmetry and sound limb loading. In the present work, four prosthetic foot, two conventional prosthetic foot (Jaipur foot and Ranger foot) and two energy-storing and return prosthetic foot (Nylon foot and Carbon fibre foot) are investigated for gait parameters of the unilateral transtibial amputee. Also, a novel wearable setup is used to acquire the amputee data. It comprises of two rotary potentiometers at the knee joint and two wearable slippers with four force sensitive resistor sensors on each. The gait parameters investigated in this study are the range of motion (ROM) of the knee joint, loading force on limbs, spatial and temporal gait parameters. Stability analysis between amputated and sound limbs is analyzed by measuring gait symmetry for gait parameters. Also, the coefficient of variation is calculated to see how gait parameters vary with the change of prosthetic foot. ROM symmetry and loading force symmetry is obtained maximum for Carbon fibre foot whereas minimum ROM symmetry is observed for Ranger foot. Furthermore, Carbon fibre foot attains maximum loading force symmetry of 0.98 indicating maximum push-off among all the four selected foot. [ABSTRACT FROM AUTHOR]

Walking is one of the most important activities. However, when abnormalities occur during walking, falls occur. Fractures caused by falls may significantly reduce activities of daily living and quality of life. Falls due to external factors occur during the swing phase when the leg is floating. Detection of the period from the moment the lower leg becomes vertical to the moment of the heel contact may help elucidate the phenomenon of falls in the future and may prevent falls by some manipulation during this period. Therefore, in this study, we develop an algorithm for detecting terminal swing independent of walking speed using a 6-axis sensor attached to the heel of shoes. Preliminary experiments were conducted on a treadmill at walking speeds of 2, 3, 4, and 5 km/h. The theoretical time of the terminal swing was measured using motion capture. Preliminary experimental results showed a correlation between the maximum angular velocity of the lateral direction axis of the 6-axis sensor and the acceleration in the toe direction at the beginning of the terminal swing. Therefore, this study proposes an algorithm for detecting terminal swing using the relationship between the maximum angular velocity of the lateral direction axis and the acceleration in the toe direction at the beginning of the terminal swing. Comparing the time of the beginning of the terminal swing between the proposed method and motion capture, it was confirmed that the algorithm could detect the terminal swing at walking speeds of 3, 4, and 5 km/h with a smaller error than in previous studies.

BackgroundMyopia, a condition affecting approximately one-quarter of the world' s population, has been projected to double in prevalence by the year 2050. It can have an impact on postural control during walking and can increase the risk of falls and injuries.Objective(1) To examine the abnormal performance of postural control during walking in male college students who used convex lenses for myopia intervention from a kinematic perspective; (2) to establish theoretical foundation for preventing falls and injuries in the myopic population.MethodsA total of 22 male college students participated in this study. The center of gravity (COG), the percentage of gait cycle (PGC) and the joint angle(JT) were collected as indications of postural control during walking. A quantitative analysis was conducted using a One-Way Repeated Measures ANOVA to examine the variations among the three groups.ResultsDuring myopic interventions, (1) the range of vertical COG changes is significant to be greater compared with normal vision (P < 0.05). (2) there was an significant increase in the PGC in single-legged support, accompanied by a decrease in the PGC in double-legged support, compared with normal vision (P < 0.05). (3) The myopic intervention leads to increased variability in JT of the hip and the knee during the single-leg support and swing, as compared to individuals with normal vision (P < 0.05). Severe myopic interventions result in more changes in JT of ankle.ConclusionMyopia has been found to have a negative impact on postural control during walking, leading to changes in balance, increased instability, and an elevated risk of injury.

In recent years biometrics play a vital role in recognizing the person and authentication. Recent studies prove that the gait cycle is unique for every individual. Gait refers to the walking pattern of an individual. Gait cycle is calculated by the right toe on the same right toe on period. Human Gait cycle and the angles calculated from head-to-toe portions of a person are important measures for both habitual and clinical analysis purposes. In most cases these parameters would furnish sufficient details for further deep learning and analysis serving as medical clues. These parametersprovide useful and validated results for medical rehabilitation. This paper proposes evaluation of Gait cycle for abnormal walking pattern of different diseases. The graphs were plotted for Gait cycle versus knee flexion angle and gait cycle versus hip to ankle angle variations which provide sufficient information for classifying normal and abnormal walk pattern. The main idea proposed here is to classify the different walk patterns without the involvement of medical tools. This proposed work will be helpful in obtaining the required and initial clinical details of persons by means of their gait without any direct medical investigation on them and facilitates in classifying different abnormal walk patterns.

Background and Objetives: The foot is a part of the body's kinetic chain and needs to be efficient during the entire gait cycle. Electronic Sensor Gait analysis is useful and an important tool within the area of podiatry to assess the physical state of patients that helps the comprehensive intervention in situations where the daily activity is limited. The aim of this research is to evaluate if the presence of a hallux limitus (HL) can alter gait space–time parameters and consequently can affect the take-off phase of the gait and the limitation of the range of motion (ROM) of the hallux. Materials and Methods: A case–control study was designed to verify whether there are alterations in the spatiotemporal parameters of the gait cycle between subjects with structural HL compared to the group of subjects with a normal hallux range. A total of n = 138 participants, cases (68 HL subjects) and healthy controls (70 subjects) were studied using an OptoGait LED sensor system to identify gait imbalances using OptoGait photocell gait analysis sensors. Results: Significant differences were found between the two groups with respect to stride length, gait cycle duration in seconds (for both feet) and for total stride and load response (p < 0.05). Conclusions: The limitation of the Hallux ROM may alter the normal gait patterns measured with an Optogait system. The early identification and treatment of gait disturbances due to HL are important to achieve normal gait physical activity to maintain a healthy lifestyle. [ABSTRACT FROM AUTHOR]

The lower limb exoskeletons are mainly used in the field of medical assistance for gait rehabilitation and military for maximizing user's strength and endurance. This work is inspired by the work of "Bionic Boot", invented by Keahi Seymour that enables to run faster with ostrich back-leg framework. The leg structure of ostrich can store double the elastic energy per step than human, due to their long elastic tendons. The existing Bionic Boot design aids in gaining speed while running without any external payloads. The objective of this work is to design and analyse lower limb exoskeleton that can carry external payload by reducing the consumption of metabolic energy during the motion. The entire mechanical structure of exoskeleton is designed in SolidWorks 3D modeling platform using three materials: aluminium alloy, stainless steel and titanium alloy. Finite element analysis of the design is performed in the same modeling package simulating the human running gait cycle for different phases to examine the stability of the structure. The deformations in aluminium and titanium alloy were larger by 66% and 40% than stainless steel respectively. However, in terms of maximum stress, stainless steel and titanium alloy experienced respectively 3.8% and 1.3% higher compared to aluminium alloy. The design is further analysed for sustainability with a view of minimizing its impact on the environment. The environmental impact parameters indicated that, stainless steel had minimal impact on the environment compared to aluminium and titanium alloy. Thus, these type of exoskeletons can reduce the dependence on the fossil-fuel powered vehicles, thereby reducing environmental pollution. [ABSTRACT FROM AUTHOR]

Walking rehabilitation following injury or disease involves voluntary gait modification, yet the specific brain signals underlying this process remains unclear. This aim of this study was to investigate the impact of an auditory cue on changes in brain activity when walking overground (O) and on a treadmill (T) using an electroencephalogram (EEG) with a 32-electrode montage. Employing a between-group repeated-measures design, 24 participants (age: 25.7 ± 3.8 years) were randomly allocated to either an O (n = 12) or T (n = 12) group to complete two walking conditions (self-selected speed control (sSC) and speed control (SC)). The differences in brain activities during the gait cycle were investigated using statistical non-parametric mapping (SnPM). The addition of an auditory cue did not modify cortical activity in any brain area during the gait cycle when walking overground (all p > 0.05). However, significant differences in EEG activity were observed in the delta frequency band (0.5–4 Hz) within the sSC condition between the O and T groups. These differences occurred at the central frontal (loading phase) and frontocentral (mid stance phase) brain areas (p < 0.05). Our data suggest auditory cueing has little impact on modifying cortical activity during overground walking. This may have practical implications in neuroprosthesis development for walking rehabilitation, sports performance optimization, and overall human quality-of-life improvement.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant’s level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body’s center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson’s r = −0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual’s stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Introduction: Gait variability is a measure of the changes in a person's walking pattern over time. Gait variability may be a useful marker of balance and coordination in people with hypertension. The objective of this investigation was to assess and contrast the gait variability exhibited by hypertensive and normotensive individuals at their preferred walking speed. Methods: A total of fifty-six participants {hypertensive (N = 28) and normotensive (N = 28)} were selected for this cross-sectional study. Their anthropometric characteristics were as mean age: 40.64 ± 6.24years; height 174.87 ± 3.45 cm, body weight 73.76 ± 6.63 kg; body mass index 23.34 ± 4.24 kg/m2. The automatic blood pressure monitor was utilized for the measurement of blood pressure. Gait variability was measured using a Physilog inertial sensors from GaitUp that recorded the gait variability parameters. Each participant walked on a 15-m straight and zero-level hard surface at a self-preferred speed. Anthropometric data were analyzed using a t-test. Whereas the gait variability parameters were analyzed using Mann-Whitney U test. Result: Significant differences were found for anthropometric characteristics, except for the height (p = .068). The significant differences between the hypertensive and normotensive participants have been shown as variability during the complete gait performance (z = 6.54,p = .000), Asymmetry between left and right foot (z = 6.54,p = .000), full speed during gait performance (z = 6.55,p = .000) was found. Conclusions: This study demonstrates that hypertensive individuals have higher gait variability compared to normotensive individuals. These findings highlight the importance of monitoring gait variability in hypertensive individuals as it may be a useful marker for evaluating walking ability and cardiovascular health. The underlying mechanisms that lead to the greater gait variability in hypertensive people need to be further studied.

Introduction: The static and dynamic analyzes in the biomechanical field developed at the level of the locomotor system occupy a very extensive space of theoretical and experimental research, with different applications, from theoretical foundation to objective evaluations, to find performance solutions, improvement, recovery/rehabilitation of movements performed by the human body. The purpose of the study was to analyze the general aspects and, in some situations, and particulars of the different areas of applicability but also of the diversity of methods and means possible to use in biomechanics studies of the human body. Material and method: The domains were analyzed and a model was proposed to determine an efficiency score for the rapid identification of useful experimental solutions. Results: The analysis of the variants of dedicated systems for biomechanics studies allowed the development of a methodology to evaluate the efficiency of the choice of these systems to complement each other, to combine to obtain more information and to validate the particular experiments. It was also found that open systems, which can be developed according to particular procedures, represent a first step for the validation of modular products and much easier to make compatible. Conclusions: From an application point of view, the authors believe that the creation of a complete and complex database of mechanical, electronic, optical, video or complementary system variants possible to use in biomechanics analyzes will be able to streamline and improve the performance of determinations and it will be possible develop a fast compatibility mechanism. [ABSTRACT FROM AUTHOR]

BACKGROUND: At present, most of biomechanical studies on medial meniscus posterior root tearing to accelerate knee joint degeneration are limited to testing the complete tear model of the posterior root of the medial meniscus on the basis of static simulation design. However, the biomechanical behavior of medial meniscus posterior root tear in the full gait cycle is not clear. OBJECTIVE: To compare the biomechanical differences between the normal knee joint model and the medial meniscus posterior root partial tear model in the complete gait cycle using dynamic finite element analysis method. METHODS: Based on the CT scan data of the right knee joint of healthy adults, the finite element model of healthy knee joint including bone, meniscus and articular cartilage was established, and the posterior root tear model of medial meniscus of knee joint was further established on the basis of healthy model. The standard gait load of ISO was applied to the two models for simulation test. The differences of stress, displacement and contact area of the corresponding parts of the two models were compared. RESULTS AND CONCLUSION: (1) Under the complete gait cycle, the stress distribution of the posterior root of the medial meniscus in the healthy model was uniform, while the pathological model showed stress concentration in the injured area. The maximum stress of the former appeared at the inner edge of the lateral meniscus at 30% time, with a value of 29.68 MPa. The latter maximum stress appeared at the inner edge of the lateral meniscus at 50% time, with a value of 30.34 MPa. (2) During the complete gait cycle, the stress distribution of tibial cartilage in healthy and pathological models was roughly the same. The maximum stress of the two models appeared at 50% and 20% around the gait, respectively, and the values were 5.11 MPa and 6.85 MPa, respectively. (3) The difference between the medial intercompartment group and the lateral intercompartment group was greater than that in the lateral intercompartment group. The average stress of the medial tibial plateau of the pathological model was 9.3% higher than that of the normal model, and the maximum value of 5.73 MPa appeared in the middle of the support phase. The peak contact area of femur and tibia appeared at 40% time, and the average contact area of femoral cartilage in each phase of the pathological model decreased by 14.6% compared with the normal model. (4) The results show that alterations in the biomechanical properties of the knee joint after a partial tear of the medial meniscus posterior root increase the risk of osteoarthritis. [ABSTRACT FROM AUTHOR]

Background: Hyperopia is a common blurred vision phenomenon that affects postural control in gait; however, current research has focused on the alteration and correction of hyperopia's physiological characteristics, ignoring the effect of hyperopia on gait kinematic characteristics. The effect of hyperopia on the basic form of movement walking is a worthy concern. Objective: To investigate the gait kinematic characteristics of male college students with varying degrees of visual acuity (normal vision, hyperopia 150°, and hyperopia 450°), as well as to provide a theoretical foundation for the effect of visual acuity on gait and fall risk reduction. Methods: Twenty-two male college students with normal visual acuity were chosen. Their vision was tested using a standard visual acuity logarithm table at normal and with 150° and 450° concave lenses. Gait kinematic data were collected under normal vision and hyperopic conditions using the PN3 Pro advanced inertial motion capture system and Axis Studio application program. Results and conclusion: 1. The change of center of gravity in Pre-double support was smaller than normal vision; Late-single support and Late-swing was larger than normal vision; 2. The percentage of the double-leg support decreased; the percentage of the single-leg support and the Late-swing increased; 3. For the joints' range of motion, Trunk flexion and extension range of motion in Pre-single support, Late-double support and Pre-swing smaller than normal visual acuity, and Late-swing larger than normal; hip internal abduction and adduction and internal and external rotation are larger than normal vision in Late-single support; knee and ankle in abduction and adduction direction are larger than normal vision in the swing stage; hip flexion and extension, internal external rotation are larger than normal vision in the swing stage. Hyperopic interventions have an impact on the kinematic characteristics of gait in male college students, mainly in terms of altered balance, increased instability, increased difficulty in maintaining trunk stability, and increased risk of injury. [ABSTRACT FROM AUTHOR]

In this paper, an interpretable classifier for Parkinson's Disease (PD) diagnosis based on analyzing the gait cycle is presented. The proposed method utilizes clinical features extracted from the vertical Ground Reaction Force (vGRF) measured by wearable sensors. Therefore, experts can verify the decision made by the proposed method. Type-2 fuzzy logic is applied to increase the robustness against noisy sensor data. First, the initial fuzzy rules are extracted using a K-Nearest-Neighbor-based clustering approach. Next, a novel quasi-Levenberg-Marquardt (qLM) learning approach is proposed and applied to fine-tune the initial rules based on minimizing the cross-entropy loss function using a trust-region optimization method. Finally, complementary online learning is proposed to improve rules by encountering new labeled samples. The performance of the method is evaluated to classify patients and healthy subjects in different conditions, including the presence of noise or observing new samples. Moreover, the performance of the model is compared to some previous supervised and unsupervised machine learning approaches. The final Accuracy, Precision, Recall, and F1 Score of the proposed method are 97.61%, 97.58%, 99.02%, and 98.30%. [ABSTRACT FROM AUTHOR]

The most important feature of bipedal robots is the high number of joints. Therefore, it is very important that the robot's joints move in coordination during walking. In this study, static walking method was used to obtain the walking pattern of the bipedal robot and the application details were discussed. In addition, the Rancho Los Amigos terminology, zero moment point, and forward and reverse kinematics methods were used to create the static walking pattern of the bipedal robot. The walking of robot was performed by applying the created walking pattern to the bipedal robot. Video footage taken with the camera was analyzed and then compared according to the Rancho Los Amigos terminology. It has been determined that the static walking pattern created in the comparison and the actual walking pattern of the bipedal robot were the same. [ABSTRACT FROM AUTHOR]

Smoking is one of the predictors of decreased cardiopulmonary endurance. Gait disturbance may be due to many reasons, including cardiovascular endurance. This study aimed to determine differences in gait parameters between non-smoker and smoker participants. A cross-sectional design was employed, involving thirty non-smokers and thirty-seven smokers as participants. Detailed interviews were conducted to gather information on smoking habits, status, and history. Gait parameters were measured using a high-quality 3D accelerometer, 3D gyroscope, and barometric pressure sensors (Physilog4 from GaitUp). Anthropometric characteristics were described, and mean values with standard deviations (SD) were calculated. An independent two-tailed t-test was conducted to compare gait parameters between non-smokers and smokers, with statistical significance set at p<0.05. The analysis revealed significant differences in various gait parameters between non-smokers and smokers. Specifically, significant differences were found in cadence (t=9.95, p=0.001), stride length (t=6.85, p=0.001), stride velocity (t=-6.58, p=0.001), stance (t=2.02, p=0.001), swing (t=3.46, p=0.001), foot flat (t=-8.94, p=0.001), pushing (t=3.53, p=0.001), and double support (t=-13.35, p=0.001). However, no significant difference was found between non-smokers and smokers in the loading phase (t=-1.57, p= 0.121). There were significant differences in general and temporal gait parameters between smokers and non-smokers. Gait parameters provide valuable insights for evaluating functional performance and providing objective and quantitative data to assess gait disorders. Future studies should include longitudinal studies with large sample sizes to explore the effects of potential confounders on gait parameters. [ABSTRACT FROM AUTHOR]

Today, human gait analysis is commonly used for clinical diagnosis, rehabilitation and performance improvement in sports. However, although previous research works in the literature address the use of motion capture systems by means of optoelectronic sensors, Inertial Measurement Units (IMUs) and depth cameras, few of them discuss their conception, guidelines and algorithms for measuring and calculating gait metrics. Moreover, commercially available motion capture systems, although efficient, are cost restrictive for most of the low-income institutions. In this research work, a new computer vision-based system (CVS) for gait analysis is developed and proposed. The aim is to close the gap found in the literature about the design and development of such systems by providing the requirements, considerations, algorithms and methodologies used to develop a gait analysis system with acceptable precision and accuracy, and at low cost. For this purpose, a linear computer vision method based on the non-homogeneous solution of the calibration matrix was used. The spatio-temporal and angular gait parameters were implemented in the proposed system, and compared with those reported in the literature. The denoising of the spatial gait trajectories and the strategies to detect gait events, are also presented and discussed. The results have shown that the proposed system is satisfactory for human gait analysis in terms of precision, computational performance and low cost. [ABSTRACT FROM AUTHOR]

Abstract Background The adverse consequences of medial meniscus posterior root tears have become increasingly familiar to surgeons, and treatment strategies have become increasingly abundant. In this paper, the finite element gait analysis method was used to explore the differences in the biomechanical characteristics of the knee joint under different conditions. Methods Based on CT computed tomography and MR images, (I) an intact knee (IK) model with bone, cartilage, meniscus and main ligaments was established. Based on this model, the posterior root of the medial meniscus was resected, and (ii) the partial tear (PT) model, (iii) the entire radial tear (ERT) model, and (iv) the entire oblique tear (EOT) model were established according to the scope and degree of resection. Then, the (v) meniscus repair (MR) model and (vi) partial meniscectomy (PM) model were developed according to the operation method. The differences in stress, displacement and contact area among different models were evaluated under ISO gait loading conditions. Results Under gait loading, there was no significant difference in the maximum stress of the medial and lateral tibiofemoral joints among the six models. Compared with the medial tibiofemoral joint stress of the IK model, the stress of the PM model increased by 8.3%, while that of the MR model decreased by 18.9%; at the same time, the contact stress of the medial tibiofemoral joint of the ERT and EOT models increased by 17.9 and 25.3%, respectively. The displacement of the medial meniscus in the ERT and EOT models was significantly larger than that in the IK model (P