Evaluation of an ankle–foot orthosis effect on gait transitional stability during ramp ascent/descent

Mahmood, Imran; Raza, Anam; Maqbool, Hafiz Farhan; Dehghani-Sanij, Abbas A.

doi:10.1007/s11517-022-02587-z

Cited by 4 publications

(10 citation statements)

References 52 publications

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“…The CoP waveforms have been used earlier to evaluate gait stability during weight-loading and unloading transitions [9,14,27]. Following that, the CoP raw data was extracted from the GaitRec repository both in anterior-posterior (AP) and medial-lateral (ML) directions.…”

Section: Data Collection and Participantsmentioning

confidence: 99%

“…A lower-order filter was selected to avoid unnecessary time delays and over-smoothening that can diminish the difference between fractured and normal waveforms. Similar filter specs were also used earlier for cleaning CoP-velocity signals [14,27,32]. The rate-of-change (derivative) of CoP signals was computed and thereafter averaging algorithm was applied to reduce the derivative-induced noise following prior studies [9,14,27].…”

Section: Data Processingmentioning

confidence: 99%

“…Similar filter specs were also used earlier for cleaning CoP-velocity signals [14,27,32]. The rate-of-change (derivative) of CoP signals was computed and thereafter averaging algorithm was applied to reduce the derivative-induced noise following prior studies [9,14,27]. The average CoP-velocity illustrated two characteristic impulses: one major impulse at the start and the other minor impulse at the end of the waveforms as shown in figure 2.…”

Section: Data Processingmentioning

confidence: 99%

“…Here, the time series CoP-velocity waveforms illustrated artefacts in the data due to variations in subjects' anthropometry, lack of repeatability of trials, and adaptability to the experimental conditions. Thus, the data arranged for each variant was cleaned from artefacts by applying Principal Component Analysis (PCA); a computational tool used previously for data cleaning and linearization [14,27,28]. PCA was implemented independently for the loading and unloading phases in all variants.…”

Section: Data Processingmentioning

confidence: 99%

“…Gait dynamic stability assessment methods are broadly categorised into three ways: (1) the Lyapunov exponent [19,20] and variability analysis [21,22] methods use whole gait cycle data to compute the stability metrics; (2) the margin of stability computes body's centre of mass (CoM) difference from the base of support at discrete gait events (heel contact and toeoff) [21,[23][24][25]; (3) Nyquist and Bode (N&B) methods compute critical stability margins within a sub-phase of a gait cycle [9,14,[26][27][28][29]. Among these reported techniques, stability assessment using whole gait cycle waveforms or at discrete gait events has obvious limitations of stability evaluation during gait inner phases such as weight loading and unloading.…”

mentioning

confidence: 99%

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Gait dynamic stability evaluation in patients undergoing hip joint fractures – tools to measure rehabilitation effectiveness

Mahmood,

Maqbool,

Raza

et al. 2024

Biomed. Phys. Eng. Express

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A hip joint fracture includes a break in the thigh (femur) or coxa bone near the pelvis. During fracture healing, stability and weight bearing by the affected limb are key indicators to measure patients’ improvement. Conventionally, the rehabilitation effectiveness is monitored through clinical examinations, patients’ feedback, and few studies also reported instrumented gait evaluations. A gap remains there to numerically quantify the recovery in patients’ stability and weight bearing in response to rehabilitation therapies. This study introduces Nyquist and Bode (N&B) methods to analyse the instrumented gait signals further and evaluate gait stability in hip fracture patients during weight loading and unloading transitions. The centre of pressure (CoP) data was recorded using force plates for conditions: coxa hip fracture (HC), femur hip fracture (HF), and normal hip joint (NH). The time rate of CoP signals illustrated two major impulses during the loading and unloading phases which were modelled in time and frequency domains. The frequency models were further analysed by applying N&B methods and stability margins were computed for both impaired and healthy conditions. Results illustrated a significant decrease (Kruskal–Wallis’s test, p < 0.001) in the intralimb walking stability of both fracture conditions. Further, Spearman’s correlation between CoP velocities of fractured and intact limbs illustrated significant interlimb dependencies to maintain walking stability (p < 0.001) during weight loading and unloading transitions. Overall, the HF impairment illustrated the least intralimb walking stability and relatively greater interlimb dependencies. Clinically, these methods and findings are important to measure the recovery in patients undergoing rehabilitation after a hip joint or other lower limb impairments.

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Section: Data Collection and Participantsmentioning

confidence: 99%