Kinematics Adaptation and Inter-Limb Symmetry during Gait in Obese Adults

Capodaglio, Paolo; Leban, Bruno; Porta, Micaela; Galli, Manuela; Cimolin, Verônica

doi:10.3390/s21175980

Cited by 13 publications

(9 citation statements)

References 62 publications

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“…More than half of the papers focused on gait tasks: (i) gait overground [ 30 , 31 , 33 , 34 , 35 , 40 , 47 , 48 , 49 , 53 , 55 , 58 , 59 , 62 , 67 , 70 , 71 , 73 , 74 ]; (ii) gait on the treadmill [ 28 , 41 , 44 , 46 , 57 , 64 ]; and (iii) the comparison between the overground and treadmill [ 29 , 37 , 78 ]. Moreover, perturbed gait [ 61 ], inclined walking [ 42 , 68 , 77 ], and gaits with different loads in the dominant hand [ 43 ] were investigated.…”

Section: Resultsmentioning

confidence: 99%

Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review

Monfrini

Rossetto

Scalona

et al. 2023

Sensors

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Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From this perspective, this systematic review identified and summarized the main technologies specifically used to acquire and quantify movements in scientific studies involving obese subjects. The search for articles was carried out on electronic databases, i.e., PubMed, Scopus, and Web of Science. We included observational studies performed on adult obese subjects whenever reporting quantitative information concerning their movement. The articles must have been written in English, published after 2010, and concerned subjects who were primarily diagnosed with obesity, thus excluding confounding diseases. Marker-based optoelectronic stereophotogrammetric systems resulted to be the most adopted solution for movement analysis focused on obesity; indeed, wearable technologies based on magneto-inertial measurement units (MIMUs) were recently adopted for analyzing obese subjects. Further, these systems are usually integrated with force platforms, so as to have information about the ground reaction forces. However, few studies specifically reported the reliability and limitations of these approaches due to soft tissue artifacts and crosstalk, which turned out to be the most relevant problems to deal with in this context. In this perspective, in spite of their inherent limitations, medical imaging techniques—such as Magnetic Resonance Imaging (MRI) and biplane radiography—should be used to improve the accuracy of biomechanical evaluations in obese people, and to systematically validate less-invasive approaches.

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Section: Resultsmentioning

confidence: 99%

Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review

Monfrini

Rossetto

Scalona

et al. 2023

Sensors

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“…The underlying causes include physical performance declines in advancing age that are associated with progressive losses of muscle mass, decreased joint flexibility, declines in force- and power-generating capacity, as well as age-related changes of the cardiovascular system ( Ganse and Degens, 2021 ). In obesity, the spatiotemporal gait parameters, parameters of pedography and joint kinematics differed compared with normal-weight matched control groups ( Choi et al, 2021 ; Pau et al, 2021 ). In detail, during walking significantly greater peak pressure values were reported for the front and rear of the foot in normal compared to obese people ( Choi et al, 2021 ).…”

Section: Introductionmentioning

confidence: 95%

“…In detail, during walking significantly greater peak pressure values were reported for the front and rear of the foot in normal compared to obese people ( Choi et al, 2021 ). In addition, obese people had a lower gait speed and stride length, shorter stance and swing phases, as well as longer double support phases ( Pau et al, 2021 ). The ankle, knee and hip ranges of motion were smaller in the obese compared to the non-obese ( Pau et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, obese people had a lower gait speed and stride length, shorter stance and swing phases, as well as longer double support phases ( Pau et al, 2021 ). The ankle, knee and hip ranges of motion were smaller in the obese compared to the non-obese ( Pau et al, 2021 ). Compared to obese people, people who are both, obese and old walked with higher center of pressure (CoP) velocity, shorter stride, and spent more time in the support phase ( Maktouf et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle

Wolff

Steinheimer²,

Warmerdam³

et al. 2023

Front. Bioeng. Biotechnol.

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The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmeanload and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.

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“…In clinical applications, wearable sensors were used in several pathological states, such as stroke [ 4 , 5 , 6 ], obese [ 7 , 8 ], and elderly [ 9 ] patients; patients with lower limb amputation [ 10 ]; patients with Parkinson’s disease [ 4 , 11 , 12 ]; and patients hospitalized for knee joint rehabilitation [ 13 ]. In particular, wearable systems were used both to quantify the functional limitations of the patients, during several movements (gait [ 5 , 7 , 8 , 9 ], upper limb [ 6 , 11 ], time up and go test [ 10 ], and unconstraint activities at home [ 12 ]) and to evaluate their accuracy and precision in comparison with the gold standard [ 4 ]. These papers support the clinical usability of wearable technology for clinical movement assessment.…”

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confidence: 99%