Cadence analysis of temporal gait patterns for seismic discrimination between human and quadruped footsteps

Robotic devices can provide physical assistance to people who have suffered neurological impairments such as stroke. Neurological disorders related to this condition induce abnormal gait patterns, which impede the independence to execute different Activities of Daily Living (ADLs). From the fundamental role of the ankle in walking, Powered Ankle-Foot Orthoses (PAFOs) have been developed to enhance the users’ gait patterns, and hence their quality of life. Ten patients who suffered a stroke used the actuation system of the T-FLEX exoskeleton triggered by an inertial sensor on the foot tip. The VICONmotion capture system recorded the users’ kinematics for unassisted and assisted gait modalities. Biomechanical analysis and usability assessment measured the performance of the system actuation for the participants in overground walking. The biomechanical assessment exhibited changes in the lower joints’ range of motion for 70% of the subjects. Moreover, the ankle kinematics showed a correlation with the variation of other movements analyzed. This variation had positive effects on 70% of the participants in at least one joint. The Gait Deviation Index (GDI) presented significant changes for 30% of the paretic limbs and 40% of the non-paretic, where the tendency was to decrease. The spatiotemporal parameters did not show significant variations between modalities, although users’ cadence had a decrease of 70% of the volunteers. Lastly, the satisfaction with the device was positive, the comfort being the most user-selected aspect. This article presents the assessment of the T-FLEX actuation system in people who suffered a stroke. Biomechanical results show improvement in the ankle kinematics and variations in the other joints. In general terms, GDI does not exhibit significant increases, and the Movement Analysis Profile (MAP) registers alterations for the assisted gait with the device. Future works should focus on assessing the full T-FLEX orthosis in a larger sample of patients, including a stage of training.

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Biomechanical analysis of gait patterns in children with intellectual disabilities

Journal of Intellectual Disability Research ◽

10.1111/jir.12872 ◽

2021 ◽

Vol 65 (10) ◽

pp. 912-921

Author(s):

Y. Ma ◽

K. Zhang ◽

S. Li ◽

L. Wang ◽

T. Wang

Keyword(s):

Intellectual Disabilities ◽

Biomechanical Analysis ◽

Gait Patterns

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Lower Limb Kinematics in Individuals with Hip Osteoarthritis during Gait: A Focus on Adaptative Strategies and Interlimb Symmetry

Bioengineering ◽

10.3390/bioengineering8040047 ◽

2021 ◽

Vol 8 (4) ◽

pp. 47

Author(s):

Micaela Porta ◽

Massimiliano Pau ◽

Bruno Leban ◽

Michela Deidda ◽

Marco Sorrentino ◽

...

Keyword(s):

Lower Limb ◽

Functional Limitations ◽

Hip Osteoarthritis ◽

Movement Analysis ◽

Human Movement ◽

Point Of View ◽

Negative Effects ◽

Gait Patterns ◽

Limb Kinematics

Among the functional limitations associated with hip osteoarthritis (OA), the alteration of gait capabilities represents one of the most invalidating as it may seriously compromise the quality of life of the affected individual. The use of quantitative techniques for human movement analysis has been found valuable in providing accurate and objective measures of kinematics and kinetics of gait in individuals with hip OA, but few studies have reported in-depth analyses of lower limb joint kinematics during gait and, in particular, there is a scarcity of data on interlimb symmetry. Such aspects were investigated in the present study which tested 11 individuals with hip OA (mean age 68.3 years) and 11 healthy controls age- and sex-matched, using 3D computerized gait analysis to perform point-by-point comparisons of the joint angle trends of hip, knee, and ankle. Angle-angle diagrams (cyclograms) were also built to compute several parameters (i.e., cyclogram area and orientation and Trend Symmetry) from which to assess the degree of interlimb symmetry. The results show that individuals with hip OA exhibit peculiar gait patterns characterized by severe modifications of the physiologic trend at hip level even in the unaffected limb (especially during the stance phase), as well as minor (although significant) alterations at knee and ankle level. The symmetry analysis also revealed that the effect of the disease in terms of interlimb coordination is present at knee joint as well as hip, while the ankle joint appears relatively preserved from specific negative effects from this point of view. The availability of data on such kinematic adaptations may be useful in supporting the design of specific rehabilitative strategies during both preoperative and postoperative periods.

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Amoebic Foraging Model of Metastatic Cancer Cells

Symmetry ◽

10.3390/sym13071140 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1140

Author(s):

Daiki Andoh ◽

Yukio-Pegio Gunji

Keyword(s):

Bayesian Inference ◽

Cancer Cells ◽

Metastatic Cancer ◽

Gait Patterns ◽

Lévy Walk ◽

Levy Walk ◽

Unicellular Organisms ◽

Living Organisms ◽

Walk Algorithm ◽

Metastatic Cancer Cells

The Lévy walk is a pattern that is often seen in the movement of living organisms; it has both ballistic and random features and is a behavior that has been recognized in various animals and unicellular organisms, such as amoebae, in recent years. We proposed an amoeba locomotion model that implements Bayesian and inverse Bayesian inference as a Lévy walk algorithm that balances exploration and exploitation, and through a comparison with general random walks, we confirmed its effectiveness. While Bayesian inference is expressed only by P(h) = P(h|d), we introduce inverse Bayesian inference expressed as P(d|h) = P(d) in a symmetry fashion. That symmetry contributes to balancing contracting and expanding the probability space. Additionally, the conditions of various environments were set, and experimental results were obtained that corresponded to changes in gait patterns with respect to changes in the conditions of actual metastatic cancer cells.

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Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage

Procedia Computer Science ◽

10.1016/j.procs.2021.05.030 ◽

2021 ◽

Vol 185 ◽

pp. 282-291

Author(s):

Nizam U. Ahamed ◽

Kellen T. Krajewski ◽

Camille C. Johnson ◽

Adam J. Sterczala ◽

Julie P. Greeves ◽

...

Keyword(s):

Machine Learning ◽

Inertial Sensor ◽

Load Carriage ◽

Sensor Data ◽

Gait Patterns

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Gait Patterns and Mood in Everyday Life: A Comparison Between Depressed Patients and Non-depressed Controls

Cognitive Therapy and Research ◽

10.1007/s10608-021-10215-7 ◽

2021 ◽

Author(s):

Dirk Adolph ◽

Wolfgang Tschacher ◽

Helen Niemeyer ◽

Johannes Michalak

Keyword(s):

Everyday Life ◽

Walking Speed ◽

Laboratory Findings ◽

Gait Patterns ◽

Gait Characteristics ◽

Clinical Diagnoses ◽

Current Mood ◽

General Movement ◽

And Gender ◽

Vertical Up

Abstract Background Previous laboratory findings suggest deviant gait characteristics in depressed individuals (i.e., reduced walking speed and vertical up-and-down movements, larger lateral swaying movements, slumped posture). However, since most studies to date assessed gait in the laboratory, it is largely an open question whether this association also holds in more naturalistic, everyday life settings. Thus, within the current study we (1) aimed at replicating these results in an everyday life and (2) investigated whether gait characteristics could predict change in current mood. Methods We recruited a sample of patients (n = 35) suffering from major depressive disorder and a sample of age and gender matched non-depressed controls (n = 36). During a 2-day assessment we continuously recorded gait patterns, general movement intensity and repetitively assessed the participant’s current mood. Results We replicated previous laboratory results and found that patients as compared to non-depressed controls showed reduced walking speed and reduced vertical up-and-down movements, as well as a slumped posture during everyday life episodes of walking. Moreover, independent of clinical diagnoses, higher walking speed, and more vertical up-and-down movements significantly predicted more subsequent positive mood, while changes in mood did not predict subsequent changes in gait patterns. Conclusion In sum, our results support expectations that embodiment (i.e., the relationship between bodily expression of emotion and emotion processing itself) in depression is also observable in naturalistic settings, and that depression is bodily manifested in the way people walk. The data further suggest that motor displays affect mood in everyday life.

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