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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]

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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.

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]

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.

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]

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.

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]

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]

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

This article introduced a three-dimension CAD model of a prosthesis testing platform using SolidWorks software to conduct a kinematic and dynamic analysis of the transfemoral prosthesis of the virtual model. Concurrently, the event-based motion simulation (EBMS) procedure was carried out on the CAD model. The concept of the operational strategy of the test platform was clarified through the machine’s real-life experience before being constructed in vitro. The platform model is capable of reproducing two active movements to simulate the locomotion of the thigh angle and hip vertical displacement for assessing the artificial knee angle motion during the gait cycle. These motions were controlled by two rotary forces (motors) that are utilized to implement control actions in EBMS. The prosthetic knee joint was built with a single axis that performs flexion and extension via the axial force of the spring. The simulation results of the thigh angle motion ranged from 20°2{0}^{^\circ } to −15°-1{5}^{^\circ }, while the maximum flexion of the prosthetic knee joint was (46°4{6}^{^\circ }). The mean absolute error was (2.727°2.72{7}^{^\circ }) and (8.338°8.33{8}^{^\circ }) for the thigh and knee joints, respectively. In conclusion, the findings can be utilized to facilitate the design and development of prostheses.

The pedobarography method makes it possible to objectify the dynamics of restoring the functional capacity of an injured limb. The value of the method is gaining popularity in determining the functional results of surgical treatment of the patients with ankle joint injuries. The details of pedobarographic monito­ring during the treatment allow to obtain digital indices, which with careful statistical analysis can significantly improve not only the diagnostic process, but also control the process of functional reco­very of the patients with injuries of the lower extremities. Changes in quantitative digital static and dynamic indices of pedobarography mean progress in the functional restoration of the injured limb or the need in the correction of the rehabilitation process. Howe­ver, there is a problem in the interpretation and specification of the known indices and indices in accordance with the pathology. A review of the scientific publications based on the Scopus, Web of Science and The Cochrane Library databases is designed to analyze the possibilities of using the pedobarography method in the diagnosis of ankle joint injuries, tibial bone fractures, which in accordance with quantitative indices make 20-28 % of all fractures of human bones. The kinematic features of the human body in normal and pathological conditions should be carefully analyzed and practically used in the monitoring process of the recovery of wal­king during the rehabilitation period in the patients with ankle joint fractures and not only. Control and detection of the deviations of pedobarographic indices should be analyzed in details with the aim of early detection of an unresolved problem before the appearance of clinical signs of chronic instability. The literature search proves that the analysis of the entire range of static and dynamic indices of pedobarography of damaged and healthy limbs during the period of postoperative recovery of walking makes it possible to evaluate the effectiveness of rehabilitation measures aimed at functional restoration of unstable injuries of the ankle joint.

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 Background Anterior femoral notching (AFN) is a severe complication of total knee replacement (TKR), which in a percentage of patients may lead to fractures after surgery. The purpose of this study was to investigate the stress distribution in patients with AFN and the safety depth of AFN during the gait cycle. Methods We performed a finite element (FE) analysis to analyse the mechanics around the femur during the gait cycle in patients with AFN. An adult volunteer was selected as the basis of the model. The TKR models were established in the 3D reconstruction software to simulate the AFN model during the TKR process, and the 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm AFN models were established, after which the prosthesis was assembled. Three key points of the gait cycle (0°, 22°, and 48°) were selected for the analysis. Results The stress on each osteotomy surface was stable in the 0° phase. In the 22° phase, the maximum equivalent stress at 3 mm was observed. In the 48° phase, with the increase in notch depth, each osteotomy surface showed an overall increasing trend, the stress range was more extended, and the stress was more concentrated. Moreover, the maximum equivalent force value (158.3 MPa) exceeded the yield strength (115.1 MPa) of the femur when the depth of the notch was ≥ 3 mm. Conclusions During the gait cycle, if there is an anterior femoral cortical notch ≥ 3 mm, the stress will be significantly increased, especially at 22° and 48°. The maximum equivalent stress exceeded the femoral yield strength and may increase the risk of periprosthetic fractures.

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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.

This article is about studying the placing direction effect of a piezoelectric sensor on the shoe insole in the GRF results. Where the sensors used in this research are in two directions, along and perpendicular to the foot midline. In the both cases the sensors were fixed on the shoe insole to sense the foot pressure. For the first set of sensors which are perpendicular to the foot midline the collected data has similar trend to the GRF collected from the force plate, as the small lateral strain in the shoe insole due to the patient weight and GRF is close to the GRF data collected from other measurement system. On the other hand, the collected data from the second set of sensors which are in a longitudinal direction with the foot midline will have different trend and values from the collected data from the force plate or any other GRF measurement system. This different in the results is due to the large longitudinal strain in the shoe insole due to the patient weight which produce dissimilar results from the force plate result data.

The choice of ceramic-on-ceramic coupling in total hip prosthesis has advantages over couplings with other combinations of materials that use polyethylene and metal materials in terms of high hardness, scratch resistance, low wear rate, and increased lubrication performance. To reduce the risk of primary postoperative failure, the selection of ceramic materials for ceramic-on-ceramic coupling is a strategic step that needs to be taken. The current study aims to analyze ceramic-on-ceramic coupling with commonly used ceramic materials, namely zirconium dioxide (ZrO2), silicon nitride (Si3N4), and aluminium oxide (Al2O3), according to Tressa failure criterion for the investigation of the stress distribution. A two-dimensional axisymmetric finite element-based computational model has been used to evaluate the Tresca stress on ceramic-on-ceramic coupling under gait cycle. The results show that the use of ZrO2-on-ZrO2 couplings can reduce Tresca stress by about 17.34% and 27.23% for Si3N4-on-Si3N4 and Al2O3-on-Al2O3 couplings, respectively. [ABSTRACT FROM AUTHOR]

A person's walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the clothing data, and it was possible to distinguish the stance and swing phases of walking based on features in the clothing data. Furthermore, simultaneously recording data from the waist, thigh, and shank was helpful in capturing the movement of the whole leg. [ABSTRACT FROM AUTHOR]

Medical technology companies have focused on gait analysis and monitoring for several years due to their importance in the diagnosis of various movement abnormalities. Studying pressure distribution on the foot is very important for the detection of abnormalities, unwanted symptoms, and consequences. This paper aims to design a wearable, low-cost, and real-time gait cycle monitoring system, based on a Flexiforce sensor. In the proposed design, eight force sensors were attached to the insole to estimate the pressure distribution on the foot. Pressure distribution monitoring helps in the estimation of foot disorders and assists in the design of medical shoes for manipulating pressure into the right positions. Sensors were connected to an appropriate microcontroller for real-time monitoring. MATLAB was used to visualize and simulate the real-time plantar pressure variation through static and dynamic states. The obtained experimental results show that the system was stable in both static and dynamic measurements, which could be used to estimate the pressure distribution on the foot. [ABSTRACT FROM AUTHOR]

Background: Anterior femoral notching (AFN) is a severe complication of total knee replacement (TKR), which in a percentage of patients may lead to fractures after surgery. The purpose of this study was to investigate the stress distribution in patients with AFN and the safety depth of AFN during the gait cycle.Methods: We performed a finite element (FE) analysis to analyse the mechanics around the femur during the gait cycle in patients with AFN. An adult volunteer was selected as the basis of the model. The TKR models were established in the 3D reconstruction software to simulate the AFN model during the TKR process, and the 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm AFN models were established, after which the prosthesis was assembled. Three key points of the gait cycle (0°, 22°, and 48°) were selected for the analysis.Results: The stress on each osteotomy surface was stable in the 0° phase. In the 22° phase, the maximum equivalent stress at 3 mm was observed. In the 48° phase, with the increase in notch depth, each osteotomy surface showed an overall increasing trend, the stress range was more extended, and the stress was more concentrated. Moreover, the maximum equivalent force value (158.3 MPa) exceeded the yield strength (115.1 MPa) of the femur when the depth of the notch was ≥ 3 mm.Conclusions: During the gait cycle, if there is an anterior femoral cortical notch ≥ 3 mm, the stress will be significantly increased, especially at 22° and 48°. The maximum equivalent stress exceeded the femoral yield strength and may increase the risk of periprosthetic fractures. [ABSTRACT FROM AUTHOR]

The pedobarography method makes it possible to objectify the dynamics of restoring the functional capacity of an injured limb. The value of the method is gaining popularity in determining the functional results of surgical treatment of the patients with ankle joint injuries. The details of pedobarographic monitoring during the treatment allow to obtain digital indices, which with careful statistical analysis can significantly improve not only the diagnostic process, but also control the process of functional recovery of the patients with injuries of the lower extremities. Changes in quantitative digital static and dynamic indices of pedobarography mean progress in the functional restoration of the injured limb or the need in the correction of the rehabilitation process. However, there is a problem in the interpretation and specification of the known indices and indices in accordance with the pathology. A review of the scientific publications based on the Scopus, Web of Science and The Cochrane Library databases is designed to analyze the possibilities of using the pedobarography method in the diagnosis of ankle joint injuries, tibial bone fractures, which in accordance with quantitative indices make 20-28 % of all fractures of human bones. The kinematic features of the human body in normal and pathological conditions should be carefully analyzed and practically used in the monitoring process of the recovery of walking during the rehabilitation period in the patients with ankle joint fractures and not only. Control and detection of the deviations of pedobarographic indices should be analyzed in details with the aim of early detection of an unresolved problem before the appearance of clinical signs of chronic instability. The literature search proves that the analysis of the entire range of static and dynamic indices of pedobarography of damaged and healthy limbs during the period of postoperative recovery of walking makes it possible to evaluate the effectiveness of rehabilitation measures aimed at functional restoration of unstable injuries of the ankle joint. [ABSTRACT FROM AUTHOR]

Featured Application: The output of this research by means of energy harvesting through biomechanical motion has the potential to be applied to charging portable devices. Portable electronic devices are dependent on batteries as the ultimate source of power. Irrefutably, batteries only have a limited operating period as they need to be regularly replaced or recharged. In many situations, the power grid infrastructure is not easily accessible to recharge the batteries and the recharging duration is also not convenient for the user to wait. Enhancement of a reliable electronic system by preventing power interruptions in remote areas is essential. Similarly, modern medical instruments and implant devices need reliable, almost maintenance-free power to ensure they are able to operate in all situations without any power interruptions. In this paper, the small-sized electromagnetic generator was designed to produce higher power by utilizing the knee angle transition involved during the walking phase as the input rotary force. The proposed generator design was investigated through COMSOL Multiphysics simulation. The achieved output RMS power was in the range of 3.31 W to 14.95 W based on the RPM range between 360 RPM to 800 RPM. [ABSTRACT FROM AUTHOR]

Objective: To assess the ameliorative effects of Prolotherapy on the pain score of the gait cycle in Monosodium Iodoacetate (MIA) induced osteoarthritis in the rat knee joint. Study Design: Laboratory-based experimental study. Place and Duration of Study: Department of Anatomy, Army Medical College, Rawalpindi in collaboration with the National Institute of Health (NIH), Islamabad and Pathology Lab Pak Emirates Rawalpindi, Pakistan, from Aug to Nov 2021. Methodology: Thirty male Sprague Dawley rats were randomly divided into three groups (n=10 in each group). The control group was Group-A. To induce osteoarthritic changes in Group-B, a single dose of 1mg Monosodium Iodoacetate was injected intra-articularly into the right knee. Group-C received a single dose of 1mg Monosodium Iodoacetate injection in the right knee intra articularly to induce osteoarthritic changes, and was followed by 0.1ml Prolotherapy (3ml of 25% dextrose, 2ml of 2% Xylocaine, 1ml of injection thiamine, and 1ml of injection Methylcobalamin) at 2, 6 and 10 weeks intra articularly into the right knee joint. Before euthanasia, Pain scoring schemes for gait in rats of all groups were done at 0, 2 and 14 weeks. Results: The present study concluded that prolotherapy improved the gait cycle pain score in experimental group C (p-value 0.001 on the intergroup comparison at two weeks and 14 weeks while p-value 0.001 when compared pain score within the Group C at 0, 2 and 14 weeks). Conclusion: Prolotherapy had ameliorative effects on the pain score of the gait cycle in experimental Group C on Monosodium Iodoacetate induced osteoarthritic changes in the knee joint of a rat. [ABSTRACT FROM AUTHOR]

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (

BackgroundThe aim of osteomyoplastic transfemoral amputation (OTFA) is to produce sustained, robust prosthetic gait performance by residuum reconstructing. A better understanding of residuum-socket interface pressures (RSI) and residuum muscle activation should uniquely reveal gait stability to better inform long-term rehabilitation goals.ObjectivesThe objectives of this study are to characterize RSI pressures and residuum muscle activation in men with OTFA while walking at two speeds and compare temporospatial muscle activation with intact controls.MethodsIn this study, we observed and compared healthy men with OTFA and controls during 2-min gait trials at brisk and self-paced speeds, two visits, and 1 year apart. RSI pressures and hip adductors, hamstrings, and quadriceps activation were recorded for those with OTFA. OTFA temporospatial muscle activation patterns were compared with the controls. Within the extracted strides, heel-strike and toe-off events and EMG activation peak times were characterized and compared. Peak times for pressure and EMG activity were examined in individual muscles and antagonist muscles of residual and intact limbs.ResultsSix men with OTFA exhibited adductor, hamstring, and quadriceps co-contraction within intact and residual limbs, regardless of walking speed or trial. Co-contraction within their intact limb occurred throughout the gait cycle. Within the residuum, co-contraction occurred during weight transference. The 75% most likely RSI peaks occurred during stance. EMG peaks were 75% most likely to occur during early stance, terminal stance-initial swing, and terminal swing.ConclusionParticipants with OTFA demonstrated adductors-hamstrings-quadriceps co-contraction in the intact thigh and residuum with corresponding RSI pressure increase, primarily during transitions between stance and swing, indicating gait instability, demonstrating the need to explicitly address these deficits continuously in rehabilitation and wellness settings.

The motor modules during human walking are identified using non-negative matrix factorization (NNMF) from surface electromyography (EMG) signals. The extraction of motor modules in healthy participants is affected by the change in pre-processing of EMG signals, such as low-pass filters (LPFs); however, the effect of different pre-processing methods, such as the number of necessary gait cycles (GCs) in post-stroke patients with varying steps, remains unknown. We aimed to specify that the number of GCs influenced the motor modules extracted in the consideration of LPFs in post-stroke patients. In total, 10 chronic post-stroke patients walked at a self-selected speed on an overground walkway, while EMG signals were recorded from the eight muscles of paretic lower limb. To verify the number of GCs, five GC conditions were set, namely, 25 (reference condition), 20, 15, 10, and 5 gate cycles with three LPFs (4, 10, and 15 Hz). First, the number of modules, variability accounted for (VAF), and muscle weightings extracted by the NNMF algorithm were compared between the conditions. Next, a modified NNMF algorithm, in which the activation timing profiles among different GCs were unified, was performed to compare the muscle weightings more robustly between GCs. The number of motor modules was not significantly different, regardless of the GCs. The difference in VAF and muscle weightings in the different GCs decreased with the LPF of 4 Hz. Muscle weightings in 15 GCs or less were significantly different from those in 25 GCs using the modified NNMF. Therefore, we concluded that the variability extracted motor modules by different GCs was suppressed with lower LPFs; however, 20 GCs were needed for more representative extraction of motor modules during walking in post-stroke patients. [ABSTRACT FROM AUTHOR]

The human gait can be described as the synergistic activity of all individual components of the sensory–motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This paper proposes a new method for assessing temporal dynamic synergies. Principal component analysis (PCA) has been applied on the signals acquired by wearable sensors (inertial measurement units, IMU and ground reaction force sensors, GRF mounted on feet) to detect temporal synergies in the space of two-dimensional PCA cyclograms. The temporal synergy results for different gait speeds in healthy subjects and stroke patients before and after the therapy were compared. The hypothesis of invariant temporal synergies at different gait velocities was statistically confirmed, without the need to record and analyze muscle activity. A significant difference in temporal synergies was noticed in hemiplegic gait compared to healthy gait. Finally, the proposed PCA-based cyclogram method provided the therapy follow-up information about paretic leg gait in stroke patients that was not available by observing conventional parameters, such as temporal and symmetry gait measures. [ABSTRACT FROM AUTHOR]

Transfemoral prostheses have evolved from mechanical devices to microprocessor-based, electronically controlled knee joints, allowing amputees to regain control of their limbs. For improved amputee experience at varying ambulation rates, these devices provide controlled damping throughout the swing and stance phases of the gait cycle. Commercially available microprocessor-based prosthetic knee (MPK) joints use linear controllers, heuristic-based methods, and finite state machine based algorithms to track the refence gait cycle. However, since the amputee experiences a variety of non-linearities during ambulation, such as uneven terrains, walking backwards and climbing stairs, therefore, traditional controllers produces error, abnormal movements, unstable control system and require manual-tuning. As a result, novel controllers capable of replicating and tracking reference gait cycles for a range of reference signals are needed to reduce the burden on amputees and improve the rehabilitation process. Therefore, the current study proposes two non-linear control techniques, the Lyapunov-redesign controller and the sliding mode controller for real-time tracking of various signals, such as walking on level ground at a normal speed and ambulation on uneven terrains. State-space model of MPK was developed along with the mathematical modelling of non-linear controllers. Simulations and results are presented using MATLAB to verify the ability of proposed non-linear controllers for constantly and dynamically tracking and maintaining desired motion dynamics. Furthermore, for selected reference signals, a linear controller was applied to the same mathematical model of MPK. During tracking of reference angel in case of general gait cycle, an accuracy of 99.95% and 99.96% was achieved for sliding mode controller and Lyapunov-redesign controller respectively. Whereas, for the same case, linear controller had an accuracy of 95.5% only. Therefore, it can be concluded that the performance of non-linear controllers was better than their linear counterparts while tracking various reference signals for microprocessor based prosthetic knee. [ABSTRACT FROM AUTHOR]

The motor modules during human walking are identified using non-negative matrix factorization (NNMF) from surface electromyography (EMG) signals. The extraction of motor modules in healthy participants is affected by the change in pre-processing of EMG signals, such as low-pass filters (LPFs); however, the effect of different pre-processing methods, such as the number of necessary gait cycles (GCs) in post-stroke patients with varying steps, remains unknown. We aimed to specify that the number of GCs influenced the motor modules extracted in the consideration of LPFs in post-stroke patients. In total, 10 chronic post-stroke patients walked at a self-selected speed on an overground walkway, while EMG signals were recorded from the eight muscles of paretic lower limb. To verify the number of GCs, five GC conditions were set, namely, 25 (reference condition), 20, 15, 10, and 5 gate cycles with three LPFs (4, 10, and 15 Hz). First, the number of modules, variability accounted for (VAF), and muscle weightings extracted by the NNMF algorithm were compared between the conditions. Next, a modified NNMF algorithm, in which the activation timing profiles among different GCs were unified, was performed to compare the muscle weightings more robustly between GCs. The number of motor modules was not significantly different, regardless of the GCs. The difference in VAF and muscle weightings in the different GCs decreased with the LPF of 4 Hz. Muscle weightings in 15 GCs or less were significantly different from those in 25 GCs using the modified NNMF. Therefore, we concluded that the variability extracted motor modules by different GCs was suppressed with lower LPFs; however, 20 GCs were needed for more representative extraction of motor modules during walking in post-stroke patients.