A comprehensive survey on gait analysis: History, parameters, approaches, pose estimation, and future work

Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait

We present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw.

Gait and sEMG characteristics of lower limbs in children with unilateral spastic cerebral palsy during walking

BACKGROUND

Children with unilateral spastic cerebral palsy (USCP) have muscle hypertonia, balance, and coordination defects that affect gross motor skills, especially walking. Understanding the gait characteristics and lower limb muscle activation patterns of USCP children can provide an objective and quantitative basis for patient assessment and treatment plan formulation.

OBJECTIVE

This study compared the gait and lower limb muscle activation characteristics of children with USCP and with typical development (TD) during walking.

METHODS

We recorded gait and sEMG data of 20 children with USCP, and 20 with typical development. sEMG signals were acquired from the bilateral tibialis anterior (TA) and lateral gastrocnemius muscles (LG) during walking. The root mean square (RMS) value, integrated electromyographic (iEMG) value and co-contraction ratio (CR) were used to evaluate muscle activity. Student's t Test and non-parametric rank sum Test were used to compare the differences between the data groups (significance level of 0.05).

RESULTS

The stance time, step length, speed, single leg support time ratio, ground impact, pre-swing angle, and muscle strength of the affected side were significantly decreased compared to those of the unaffected side in children with USCP (P < 0.05), while the swing phase, muscle tonus of LG were significantly prolonged (P < 0.05). Compared with TD children, children with USCP exhibited reduced bilateral walking ability, particularly noticeable in their smaller pre-swing angle(P < 0.05), diminished muscle strength of the TA and LG, as well as LG spasms(P < 0.05).

SIGNIFICANCE

Children with USCP have decreased ambulatory gait stability. Step length, pull acceleration, pre-swing angle, and CR can be used as sensitive indicators for gait assessment. Strengthening the TA muscle and reducing ankle spasm may help improve gait and postural stability in children with USCP.

The effect of harvesting the anterior half of the peroneus longus tendon on foot morphology and gait

BACKGROUND AND OBJECTIVES

In anterior cruciate ligament reconstruction, the strength of the graft was found to be unsatisfactory usually the anterior half of the peroneus longus tendon was taken for supplementation, but the effect on foot and ankle function and gait in the donor area is unclear. This study aims to explore the changes in the ankle and gait after using the harvested anterior half of the peroneus longus tendon as a reconstruction graft for the anterior cruciate ligament.

METHODS

A total of 20 patients, 6 males and 14 females, aged 18 to 44 years, with unilateral anterior cruciate ligament injuries, underwent reconstruction using the harvested anterior half of the peroneus longus tendon as a graft between June 2021 and December 2021. The part on which the anterior half of the peroneus longus tendon was harvested was considered the experimental group, while the contralateral foot was the control group. At the 6-month follow-up, the Lysholm knee score, AOFAS ankle score, and gait-related data (foot length, arch index, arch volume, arch volume index, and gait cycle parameters: percentage of time in each gait phase, step frequency, step length, foot strike angle, and push-off angle) were assessed using a 3D foot scanner and wearable sensors for both groups.

RESULTS

All 20 patients completed the six-month follow-up. There were no statistically significant differences between the experimental and control groups regarding knee scores, ankle scores, foot length, arch index, arch volume, arch volume index, step frequency, and step length (P > 0.05). However, there were statistically significant differences between the experimental and control groups in terms of the gait cycle parameters, including the percentage of time in the stance, mid-stance, and push-off phases, as well as foot strike angle and push-off angle (P < 0.05).

CONCLUSION

Through our study of the surgical experimental group we have shown that harvesting the anterior half of the peroneus longus tendon does not affect foot morphology and gait parameters; however, it does impact the gait cycle.

Accuracy validation of a wearable IMU-based gait analysis in healthy female

OBJECTIVE

The aim of this study was to assess the accuracy and test-retest reliability of a wearable inertial measurement unit (IMU) system for gait analysis in healthy female compared to a gold-standard optoelectronic motion capture (OMC) system.

METHODS

In our study, we collected data from 5 healthy young females. Participants were attached with markers from both the OMC system and the IMU system simultaneously. Data was collected when participants walked on a 7 m walking path. Each participant performed 50 repetitions of walking on the path. To ensure the collection of complete gait cycle data, a gait cycle was considered valid only if the participant passed through the center of the walking path at the same time that the OMC system detected a valid marker signal. As a result, 5 gait cycles that met the standards of the OMC system were included in the final analysis. The stride length, cadence, velocity, stance phase and swing phase of the spatio-temporal parameters were included in the analysis. A generalized linear mixture model was used to assess the repeatability of the two systems. The Wilcoxon rank-sum test for continuous variables was used to compare the mean differences between the two systems. For evaluating the reliability of the IMU system, we calculated the Intra-class Correlation Coefficient (ICC). Additionally, Bland-Altman plots were used to compare the levels of agreement between the two systems.

RESULTS

The measurements of Spatio-temporal parameters, including the stance phase (P = 0.78, 0.13, L-R), swing phase (P = 0.78, 0.13, L-R), velocity (P = 0.14, 0.13, L-R), cadence (P = 0.53, 0.22, L-R), stride length (P = 0.05, 0.19, L-R), by the IMU system and OMC system were similar. Which suggested that IMU and OMC systems could be used interchangeably for gait measurements. The intra-rater reliability showed an excellent correlation for the stance phase, swing phase, velocity and cadence (Intraclass Correlation Coefficient, ICC > 0.9) for both systems. However, the correlation of stride length was poor (ICC = 0.36, P = 0.34, L) to medium (ICC = 0.56, P = 0.22, R). Additionally, the measurements of IMU systems were repeatable.

CONCLUSIONS

The results of IMU system and OMC system shown good repeatability. Wearable IMU system could analyze gait data accurately. In particular, the measurement of stance phase, swing phase, velocity and cadence showed excellent reliability. IMU system provided an alternative measurement to OMC for gait analysis. However, the measurement of stride length by IMU needs further consideration.

A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis

BACKGROUND

Finding the best subset of gait features among biomechanical variables is considered very important because of its ability to identify relevant sports and clinical gait pattern differences to be explored under specific study conditions. This study proposes a new method of metaheuristic optimization-based selection of optimal gait features, and then investigates how much contribution the selected gait features can achieve in gait pattern recognition.

METHODS

Firstly, 800 group gait datasets performed feature extraction to initially eliminate redundant variables. Then, the metaheuristic optimization algorithm model was performed to select the optimal gait feature, and four classification algorithm models were used to recognize the selected gait feature. Meanwhile, the accuracy results were compared with two widely used feature selection methods and previous studies to verify the validity of the new method. Finally, the final selected features were used to reconstruct the data waveform to interpret the biomechanical meaning of the gait feature.

RESULTS

The new method finalized 10 optimal gait features (6 ankle-related and 4-related knee features) based on the extracted 36 gait features (85 % variable explanation) by feature extraction. The accuracy in gait pattern recognition among the optimal gait features selected by the new method (99.81 % ± 0.53 %) was significantly higher than that of the feature-based sorting of effect size (94.69 % ± 2.68 %), the sequential forward selection (95.59 % ± 2.38 %), and the results of previous study. The interval between reconstructed waveform-high and reconstructed waveform-low curves based on the selected feature was larger during the whole stance phase.

SIGNIFICANCE

The selected gait feature based on the proposed new method (metaheuristic optimization-based selection) has a great contribution to gait pattern recognition. Sports and clinical gait pattern recognition can benefit from population-based metaheuristic optimization techniques. The metaheuristic optimization algorithms are expected to provide a practical and elegant solution for sports and clinical biomechanical feature selection with better economy and accuracy.

Diagnosis of generalized joint hypermobility with gait patterns using a deep neural network

Generalized joint hypermobility (GJH) describes the situation that the range of joint motion exceeds the normal range. GJH is found to increase the risk of knee-related injury and osteoarthritis, challenging the athletic ability of the population. Gait signals are directly related to hip and knee athletic conditions, and have been shown to have significant changes with GJH by our previous research. But gait data are noisy, and vary with age, gender, weight, and ethnicity, which makes them hard to analyze with traditional statistical methods. In this study, we proposed an end-to-end deep learning model to recognize the patterns of the gait signals. The model consists of convolutional network blocks, residual network blocks, and attention blocks. Our dataset is composed of 452 samples of gait data obtained by a three-dimension motion capture system, with the six-degree-of-freedom kinematic data of hip, knee, and ankle joints during level walking, downhill, and uphill walking. The model achieves 95.77% accuracy and 98.68% specificity with a recall of 76.84% while is more efficient than traditional machine learning methods. The trained model can be run on economical friendly devices, and provide help for immediate and precise diagnosis of GJH. It is also meaningful to consider its application in large-scale GJH screening, which can contribute to sports medicine.

Spatial and Temporal Gait Characteristics in Patients Admitted to a Neuro-Rehabilitation Department with Age-Related White Matter Changes: A Gait Analysis and Clinical Study

BACKGROUND

Patients with age-related white matter changes (ARWMC) frequently present a gait disorder, depression and cognitive impairment. Our aims are to define which alterations in the gait parameters are associated with motor or neuro-psychological impairment and to assess the role of motor, mood or cognitive dysfunction in explaining the variance of the gait parameters.

METHODS

Patients with gait disorders admitted to a Neuro-rehabilitation Department, affected by vascular leukoencephalopathy who had ARWMC confirmed by a brain MRI, were consecutively enrolled, classified by a neuroradiological scale (Fazekas 1987) and compared to healthy controls. We excluded subjects unable to walk independently, subjects with hydrocephalus or severe aphasia, with orthopaedic and other neurological pathologies conditioning the walking pattern. Patients and controls were assessed by clinical and functional scales (Mini Mental State Examination, Geriatric Depression Scale, Nevitt Motor Performance Scale, Berg Balance Scale, Functional Independence Measure), and computerised gait analysis was performed to assess the spatial and temporal gait parameters in a cross-sectional study.

RESULTS

We recruited 76 patients (48 males, aged 78.3 ± 6.2 years) and 14 controls (6 males, aged 75.8 ± 5 years). In the multiple regression analysis, the gait parameter with overall best model summary values, associated with the ARWMC severity, was the stride length even after correction for age, sex, weight and height (R² = 0.327). The motor performances justified at least in part of the gait disorder (R² change = 0.220), but the mood state accounted independently for gait alterations (R² change = 0.039). The increase in ARWMC severity, the reduction of motor performance and a depressed mood state were associated with a reduction of stride length (R = 0.766, R² = 0.587), reduction of gait speed (R² = 0.573) and an increase in double support time (R² = 0.421).

CONCLUSION

The gait disorders in patients with ARWMC are related to motor impairment, but the presence of depression is an independent factor for determining gait alterations and functional status. These data pave the way for longitudinal studies, including gait parameters, to quantitatively assess gait changes after treatment or to monitor the natural progression of the gait disorders.

High-resolution analysis of individual Drosophila melanogaster larvae uncovers individual variability in locomotion and its neurogenetic modulation

Neuronally orchestrated muscular movement and locomotion are defining faculties of multicellular animals. Due to its simple brain and genetic accessibility, the larva of the fruit fly Drosophila melanogaster allows one to study these processes at tractable levels of complexity. However, although the faculty of locomotion clearly pertains to the individual, most studies of locomotion in larvae use measurements aggregated across animals, or animals tested one by one, an extravagance for larger-scale analyses. This prevents grasping the inter- and intra-individual variability in locomotion and its neurogenetic determinants. Here, we present the IMBA (individual maggot behaviour analyser) for analysing the behaviour of individual larvae within groups, reliably resolving individual identity across collisions. We use the IMBA to systematically describe the inter- and intra-individual variability in locomotion of wild-type animals, and how the variability is reduced by associative learning. We then report a novel locomotion phenotype of an adhesion GPCR mutant. We further investigated the modulation of locomotion across repeated activations of dopamine neurons in individual animals, and the transient backward locomotion induced by brief optogenetic activation of the brain-descending 'mooncrawler' neurons. In summary, the IMBA is an easy-to-use toolbox allowing an unprecedentedly rich view of the behaviour and its variability of individual larvae, with utility in multiple biomedical research contexts.