Interpreting Spatiotemporal Parameters, Symmetry, and Variability in Clinical Gait Analysis

Gouelle, Arnaud; Mégrot, Fabrice

doi:10.1007/978-3-319-30808-1_35-1

Cited by 15 publications

(30 citation statements)

References 25 publications

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“…The second level specifies the categories of parameters extracted from the analysis, which can include rhythm and phase, pace, variability, postural control, or asymmetry. Rhythm and phase parameters are variables that reflect gait rhythm, timing, and duration; pace parameters give information about speed and/or length measurements; postural control is an integrative process used to maintain body's position relative to gravity and of its segments relative to each other; asymmetry parameters are those that look for differences between limbs; and variability is the fluctuation of parameters, which can offer a complementary way of quantifying and indicating mobility deficits [104].…”

Section: A Taxonomy For Gait Balance Rom Analysismentioning

confidence: 99%

“…The most common gait parameters associated with rhythm and phases of gait are gait cycles phases, stance time, swing time, step time, stride time, cadence, and walking ratio [80,104].…”

Section: Gait Analysis: Rhythm and Phasesmentioning

confidence: 99%

“…Stance time is the time that passes during the stance phase, starting once the foot makes contact with the floor until it moves off the floor again. Researchers have found that stance phase is longer when the subject has a balance problem [104]. Stride time is the duration of a stride and the same procedure used to measure swing time and stance time can be used to calculate stride time.…”

Section: Gait Analysis: Rhythm and Phasesmentioning

confidence: 99%

“…It can be calculated by dividing the mean step length by the cadence. Researchers have found that the mean walking ratio is 0.58 and it decreases when the person walks with fast, shorter steps such as in person with Parkinson's disease or Alzheimers Disease [104].…”

Section: Gait Analysis: Rhythm and Phasesmentioning

confidence: 99%

“…According to Baker, it can be calculated using Equation (6), where gait velocity (GV) is expressed in meters per second, cadence is in steps per minute, and stride length (SL) is in meters [80]. Researchers have found that while cadence increases linearly, step length increases logarithmically, and it tends to stabilize at high speeds but changes at low speeds [104]. Gait velocity is correlated with functional ability and balance confidence and can be used to determine outcomes such as functional status, discharge location, and the need of rehabilitation [111].…”

Section: Gait Analysis: Pacementioning

confidence: 99%

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Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

2019

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More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems.

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Section: A Taxonomy For Gait Balance Rom Analysismentioning

confidence: 99%

“…The most common gait parameters associated with rhythm and phases of gait are gait cycles phases, stance time, swing time, step time, stride time, cadence, and walking ratio [80,104].…”

Section: Gait Analysis: Rhythm and Phasesmentioning

confidence: 99%

Section: Gait Analysis: Rhythm and Phasesmentioning

confidence: 99%

Section: Gait Analysis: Rhythm and Phasesmentioning

confidence: 99%

Section: Gait Analysis: Pacementioning

confidence: 99%

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Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

2019

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Continuous gait monitoring discriminates community‐dwelling mild Alzheimer's disease from cognitively normal controls

Varma

Ghosal

Hillel

et al. 2021

A&D Transl Res & Clin Interv

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Introduction Few studies have explored whether gait measured continuously within a community setting can identify individuals with Alzheimer's disease (AD). This study tests the feasibility of this method to identify individuals at the earliest stage of AD. Methods Mild AD (n = 38) and cognitively normal control (CNC; n = 48) participants from the University of Kansas Alzheimer's Disease Center Registry wore a GT3x+ accelerometer continuously for 7 days to assess gait. Penalized logistic regression with repeated five‐fold cross‐validation followed by adjusted logistic regression was used to identify gait metrics with the highest predictive performance in discriminating mild AD from CNC. Results Variability in step velocity and cadence had the highest predictive utility in identifying individuals with mild AD. Metrics were also associated with cognitive domains impacted in early AD. Discussion Continuous gait monitoring may be a scalable method to identify individuals at‐risk for developing dementia within large, population‐based studies.

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Motion acquisition of gait characteristics one week after total hip arthroplasty: a factor analysis

Cattaneo,

Ghidotti,

Catellani

et al. 2024

Arch Orthop Trauma Surg

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Introduction Clinical gait analysis can be used to evaluate the recovery process of patients undergoing total hip arthroplasty (THA). The postoperative walking patterns of these patients can be significantly influenced by the choice of surgical approach, as each procedure alters distinct anatomical structures. The aim of this study is twofold. The first objective is to develop a gait model to describe the change in ambulation one week after THA. The secondary goal is to describe the differences associated with the surgical approach. Materials and methods Thirty-six patients undergoing THA with lateral (n = 9), anterior (n = 15), and posterior (n = 12) approaches were included in the study. Walking before and 7 days after surgery was recorded using a markerless motion capture system. Exploratory Factor Analysis (EFA), a data reduction technique, condensed 21 spatiotemporal gait parameters to a smaller set of dominant variables. The EFA-derived gait domains were utilized to study post-surgical gait variations and to compare the post-surgical gait among the three groups. Results Four distinct gait domains were identified. The most pronounced variation one week after surgery is in the Rhythm (gait cycle time: $$+32.9\mathrm{\%}$$ + 32.9 % ), followed by Postural control (step width: $$+27.0\mathrm{\%}$$ + 27.0 % ), Phases (stance time: $$+11.0\%$$ + 11.0 % ), and Pace (stride length: − $$9.3\%$$ 9.3 % ). In postsurgical walking, Phases is statistically significantly different in patients operated with the posterior approach compared to lateral (p-value = 0.017) and anterior (p-value = 0.002) approaches. Furthermore, stance time in the posterior approach group is significantly lower than in healthy individuals (p-value < 0.001). Conclusions This study identified a four-component gait model specific to THA patients. The results showed that patients after THA have longer stride time but shorter stride length, wider base of support, and longer stance time, although the posterior group had a statistically significant shorter stance time than the others. The findings of this research have the potential to simplify the reporting of gait outcomes, reduce redundancy, and inform targeted interventions in regards to specific gait domains.

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Interpreting Spatiotemporal Parameters, Symmetry, and Variability in Clinical Gait Analysis

Cited by 15 publications

References 25 publications

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

Continuous gait monitoring discriminates community‐dwelling mild Alzheimer's disease from cognitively normal controls

Motion acquisition of gait characteristics one week after total hip arthroplasty: a factor analysis

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