The impact of outdoor walking surfaces on lower-limb coordination and variability during gait in healthy adults

The NACOB multi-surface walking dataset

Walking is a fundamental aspect of human movement, and understanding how irregular surfaces impact gait is crucial. Existing gait research often relies on laboratory settings with ideal surfaces, limiting the applicability of findings to real-world scenarios. While some irregular surface datasets exist, they are often small or lack biomechanical gait data. In this paper, we introduce a new irregular surface dataset with 134 participants walking on surfaces of varying irregularity, equipped with inertial measurement unit (IMU) sensors on the trunk and lower right limb (foot, shank, and thigh). Collected during the North American Congress on Biomechanics conference in 2022, the dataset aims to provide a valuable resource for studying biomechanical adaptations to irregular surfaces. We provide the detailed experimental protocol, as well as a technical validation in which we developed a machine learning model to predict the walking surface. The resulting model achieved an accuracy score of 95.8%, demonstrating the discriminating biomechanical characteristics of the dataset's irregular surface gait data.

Transfemoral amputees adapt their gait during cross-slope walking with specific upper-lower limb coordination

BACKGROUND

Unilateral lower limb amputees have asymmetrical gaits, particularly on irregular surfaces and slopes. It is unclear how coordination between arms and legs can adapt during cross-slope walking.

RESEARCH QUESTION

How do transfemoral amputees (TFAs) adapt their upper-lower limb coordination on cross-slope surfaces?

METHODS

Twenty TFA and 20 healthy adults (Ctrl) performed a three-dimensional gait analysis in 2 walking conditions: level ground and cross-slope with prosthesis uphill. Sagittal joint angles and velocities of hips and shoulders were calculated. Continuous relative phases (CRP) were computed between the shoulder and the hip of the opposite side. The closer to 0 the CRP is, the more coordinated the joints are. Curve analysis were conducted using SPM.

RESULTS

The mean CRP between the downhill shoulder and the uphill hip was higher in TFA compared to Ctrl (p = 0.02), with a walking conditions effect (p = 0.005). TFA showed significant differences about the end of the stance phase (p = 0.01) between level ground and cross-slope, while Ctrl showed a significant difference (p = 0.008) between these walking conditions at the end of the swing phase. In CRP between the uphill shoulder and the downhill hip, SnPM analysis showed intergroup differences during the stance phase (p < 0.05), but not in the comparison between walking conditions in TFA and Ctrl groups.

SIGNIFICANCE

TFA showed an asymmetrical coordination in level ground walking compared to Ctrl. Walking on cross-slope led to upper-lower limb coordination adaptations: this condition impacted the CRP between downhill shoulder and uphill hip in both groups. The management of the prosthetic limb, positioned uphill, induced a reorganization of the coordination with the upper limb of the amputated side. Identifying upper-lower limb coordination adaptations on cross-slope surfaces will help to achieve rehabilitation goals for effective walking in urban environments.

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.

Musculoskeletal adaptation of young and older adults in response to challenging surface conditions

Over 36 million adults over 65 years of age experience accidental falls each year. The underlying neuromechanics (whole-body function) and driving forces behind accidental falls, as well as the effects of aging on the ability of the musculoskeletal system to adapt, are poorly understood. We evaluated differences in kinematics (lower extremity joint angles and range of motion), kinetics (ground reaction force), and electromyography (muscle co-contraction), due to changes in surface conditions during gait in 14 older adults with a history of falling and 14 young adults. We investigated the impact of challenging surfaces on musculoskeletal adaptation and compared the mechanisms of adaptation between age-groups. Older adults displayed greater hip and knee flexion and range of motion during gait, reduced initial vertical loading, and 13 % greater knee muscle co-contraction during early stance compared to young adults. Across age groups, the presence of an uneven challenging surface increased lower-limb flexion compared to an even surface. On a slick surface, older adults displayed 30 % greater ankle muscle co-contraction during early stance as compared to young adults. Older adults respond to challenging surfaces differently than their younger counterparts, employing greater flexion during early stance. This study underscores the need for determining lower-limb musculoskeletal adaptation strategies during gait and assessing how these strategies change with age, risk of accidental falls, and environmental surfaces to reduce the risk of accidental falls.

Generalizability of deep learning models for predicting outdoor irregular walking surfaces

Observations from laboratory-based gait analysis are difficult to extrapolate to real-world environments where gait behavior is modulated in response to complex environmental conditions and surface profiles. Inertial measurement units (IMUs) permit real-world gait analysis; however, automatic detection of surfaces encountered remains largely unexplored. The aims of this study are to quantify for machine learning models the effect of (1) random and subject-wise data splitting and (2) sensor location and count on surface classification performance. Thirty participants walked on nine surface conditions (flat-even, slope-up, slope-down, stairs-up, stairs-down, cobblestone, grass, banked-left, banked-right) wearing IMUs (wrist, trunk, bilateral thighs, bilateral shanks). Data were separated into gait cycles, normalized to 101 samples, and spilt into train and test sets (85 and 15%, respectively). For random splitting, trials were randomly assigned to the train or test set. In subject-wise splitting, all trials from 4 random participants were selected for testing. Linear discriminant analysis extracted features from the IMUs. Features were delivered to a neural network. F1-score evaluated model performance. Models achieved F1 scores of 0.96 and 0.78 using random and subject-wise splitting, respectively. Random splitting performance was mainly invariant to sensor location/count; however, subject-wise splitting showed best performance using lower-limb sensors. In general, stairs and sloped surfaces were easily predicted (F1 > 0.85) while banked surfaces were challenging, especially for subject-wise models (F1 ≈ 0.6). Neural networks can detect surfaces based on subtle changes in walking behavior captured by IMUs. Data splitting approaches and sensor location/count (subject-wise) have a non-negligible effect on model performance.