Discriminating features of ground reaction forces in overweight old and young adults during walking using functional principal component analysis

Center of mass motion and plantar pressure distribution during walking in overweight individuals

BACKGROUND

Gait imbalance has been reported in overweight individuals and could further impair their mobility and quality of life. As the feet are the most distal part of the body and sensitively interface with external surroundings, evaluating the plantar pressure distribution can provide critical insights into their roles in regulating gait balance control. Therefore, the purpose of this study was to evaluate the effect of body weight and different gait speeds on the plantar pressure distribution and whole-body center of mass (COM) motion during walking.

METHODS

Eleven overweight individuals (OB) and 13 non-overweight individuals (NB) walked on a 10-meter walkway at three speed conditions (preferred, 80% and 120% of preferred speed). Gait balance was quantified by the mediolateral COM sway. Plantar pressure data were obtained using wireless pressure-sensing insoles that were inserted into a pair of running shoes. Analysis of variance models were used to examine the effect of body size, gait speeds, or their interactions on peak mediolateral COM and peak plantar pressure during walking.

RESULTS

Significant group effects of peak plantar pressure under the lateral forefoot (P = 0.03), lateral midfoot (P = 0.02), and medial heel (P = 0.02) were observed. However, the mediolateral COM motion and spatiotemporal gait parameters only revealed significant speed effects.

SIGNIFICANCE

Findings from this study indicated that overweight individuals exhibited increased plantar pressure under the lateral aspect of the foot, particularly during the late stance phase of walking, in an effort to maintain a comparable mediolateral COM motion to that of non-overweight individuals. Such elevated pressure in overweight individuals may potentially increase the risk of musculoskeletal pathology in the long term. The identified patterns are noteworthy as they have practical implications for designing targeted interventions and improving the overall health of individuals with a high BMI.

The effect of severe obesity on three-dimensional ground reaction force signals during walking

BACKGROUND

The gait pattern of adults with class I obesity [30 ≤ body mass index < 35kg/m²] was characterized by altered three-dimensional ground reaction force signals compared to lean adults (18.5 ≤ body mass index < 25 kg/m²). However, results might not be generalizable to adults with severe obesity (class II and III; body mass index ≥ 35 kg/m²). Hence, the purpose of the present study was to investigate the differences in relative ground reaction force signals, i.e., normalized by body weight, between adults with severe obesity and lean adults using functional principal component analysis.

METHODS

Thirteen lean and eighteen sedentary adults with severe obesity performed a 5-min walking trial (1.11 m/s) on an instrumented treadmill. The first five functional principal components of the relative force signals (mediolateral, anterior-posterior, and vertical directions) were obtained using functional principal component analysis. Functional principal component scores were compared between groups using an analysis of covariance with age as covariable.

FINDINGS

Functional principal component analysis reported a statistically significant group effect for first functional principal component score for mediolateral (P = 0.004), and second and fifth functional principal component scores for anterior-posterior (P ≤ 0.02) force signals. Adults with severe obesity displayed a greater mediolateral force during most of the stance but similar magnitudes of the anterior-posterior and vertical forces compared to lean adults.

INTERPRETATION

Therefore, increasing the obesity level accentuates differences in mediolateral force but promotes no specific changes in anterior-posterior force likely due to chronic loading adaptation.

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

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.

Evaluating the difference in walk patterns among normal-weight and overweight/obese individuals in real-world surfaces using statistical analysis and deep learning methods with inertial measurement unit data

Unusual walk patterns may increase individuals' risks of falling. Anthropometric features of the human body, such as the body mass index (BMI), influences the walk patterns of individuals. In addition to the BMI, uneven walking surfaces may cause variations in the usual walk patterns of an individual that will potentially increase the individual's risk of falling. The objective of this study was to statistically evaluate the variations in the walk patterns of individuals belonging to two BMI groups across a wide range of walking surfaces and to investigate whether a deep learning method could classify the BMI-specific walk patterns with similar variations. Data collected by wearable inertial measurement unit (IMU) sensors attached to individuals with two different BMI were collected while walking on real-world surfaces. In addition to traditional statistical analysis tools, an advanced deep learning-based neural network was used to evaluate and classify the BMI-specific walk patterns. The walk patterns of overweight/obese individuals showed a greater correlation with the corresponding walking surfaces than the normal-weight population. The results were supported by the deep learning method, which was able to classify the walk patterns of overweight/obese (94.8 ± 4.5%) individuals more accurately than those of normal-weight (59.4 ± 23.7%) individuals. The results suggest that application of the deep learning method is more suitable for recognizing the walk patterns of overweight/obese population than those of normal-weight individuals. The findings from the study will potentially inform healthcare applications, including artificial intelligence-based fall assessment systems for minimizing the risk of fall-related incidents among overweight and obese individuals.