The effect of school trolley load on spatiotemporal gait parameters of children

The impact of varying trolley case usage modes and weights on body posture

OBJECTIVE

To study the body posture characteristics when walking with trolley case, and to explore the effects of different usage methods and weights of trolley case on body posture characteristics.

METHODS

Fifteen subjects pushed and pulled(Condition 1 and 2) the case with three load weights of 10 %, 20 % and 30 % of their own body weight with 0 % no load as baseline for both conditions. The basic gait parameters, kinematic and kinetic data were collected using the VICON infrared motion capture system and a 3D force platform. Two repeated measures factor (condition×weight) analysis of variance was used for statistical analysis of the gait temporal and spatial parameters, as well as trunk angle, kinetic ground reaction force, shoulder joint force, and trunk moment.

RESULTS

Significant condition*weight interactions were detected in DLST (Double Limb Stance Time) (F=5.341，P = 0.006), GRF (Ground Reaction Force) in frontal plane (F=10.507, p < 0.001) and vertical plane (F=3.751, p = 0.021), shoulder joint force in sagittal plane (F=21.129, p < 0.001), and flexion-extension angle of the trunk in the sagittal plane (F=4.888, p < 0.010). Significant main effects were detected in walking speed (F=35.842, p < 0.001), right support time (F=12.156, p < 0.001), left swing time (F=8.506, p < 0.001), left support time (F=1.122, p < 0.001), right step length (F=33.900, p < 0.001), and left step length (F=14.960, p < 0.001) under different weights. A significant main effect was detected in sagittal GRF (F=11.77, p < 0.001), trunk rotation angle (F=4.124, p = 0.016), amplitude of COM (F=2.993, p = 0.046), under different weights.

CONCLUSION

When the weight of the case exceeds 20 % of the body weight, from the perspective of energy efficiency, the push method is more advantageous than the pull method. When walking with luggage, people tend to maintain the stability of their trunk posture by adjusting the force on their arms more often.

Biomechanical investigation of tasks concerning manual materials handling using response surface methodology

In typical manual material handling, the variations in walking pattern are decided by various factors, such as load being handled, frequency of handling, walking surface, etc. Traditional gait analysis protocols commonly evaluate individual factor within specified ranges associated with particular activities or pathologies. However, existing literature underscores the concurrent impact of multiple factors on gait. This study identifies five pivotal factors-walking speed, surface slope, load carried, carrying method, and footwear-as contributors to gait alterations. To address risk factors in manual material handling activities, we propose a unique design-of-experiment-based approach for multi-task gait analysis. Unraveling the relationship between manual handling attributes and human gait holds paramount importance in formulating effective intervention strategies. We optimized the five input factors across a cohort of 15 healthy male participants by employing a face-centered central composite design experimentation. A total of 29 input factor combinations were tested, yielding a comprehensive dataset encompassing 18 kinematic gait parameters (such as cadence, step length etc., measured using inertial measurement system), the isolated impacts of factors, and the interplay of two-factor interactions with corresponding responses. The results illuminate the optimal scenarios of input factors that enhance individual gait performance-these include wearing appropriate footwear, employing a backpack for load carriage, and maintaining a moderate walking pace on a medium slope with minimal load. The study identifies walking speed and load magnitude as primary influencers of gait mechanics, followed by the chosen carrying method. In consequence, the insights gained advocate for the refinement of manual material handling tasks based on the outcomes, effectively mitigating the risk of musculoskeletal disorders by suggesting the interventions for posture correction.

Multi-class classification of biomechanical data: A functional LDA approach based on multi-class penalized functional PLS

A functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed. Kinematic data, usually collected in linear acceleration or angular rotation format, can be identified with functions in a continuous domain (time, percentage of gait cycle, etc.). Since kinematic curves are measured in the same sample of individuals performing different activities, they are a clear example of functional data with repeated measures. On the other hand, the sample curves are observed with noise. Then, a roughness penalty might be necessary in order to provide a smooth estimation of the discriminant functions, which would make them more interpretable. Moreover, because of the infinite dimension of functional data, a reduction dimension technique should be considered. To solve these problems, we propose a multi-class approach for penalized functional partial least squares (FPLS) regression. Then linear discriminant analysis (LDA) will be performed on the estimated FPLS components. This methodology is motivated by two case studies. The first study considers the linear acceleration recorded every two seconds in 30 subjects, related to three different activities (walking, climbing stairs and down stairs). The second study works with the triaxial angular rotation, for each joint, in 51 children when they completed a cycle walking under three conditions (walking, carrying a backpack and pulling a trolley). A simulation study is also developed for comparing the performance of the proposed functional LDA with respect to the corresponding multivariate and non-penalized approaches.

Multi-class classification of biomechanical data: A functional LDA approach based on multi-class penalized functional PLS

A functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed. Kinematic data, usually collected in linear acceleration or angular rotation format, can be identified with functions in a continuous domain (time, percentage of gait cycle, etc.). Since kinematic curves are measured in the same sample of individuals performing different activities, they are a clear example of functional data with repeated measures. On the other hand, the sample curves are observed with noise. Then, a roughness penalty might be necessary in order to provide a smooth estimation of the discriminant functions, which would make them more interpretable. Moreover, because of the infinite dimension of functional data, a reduction dimension technique should be considered. To solve these problems, we propose a multi-class approach for penalized functional partial least squares (FPLS) regression. Then linear discriminant analysis (LDA) will be performed on the estimated FPLS components. This methodology is motivated by two case studies. The first study considers the linear acceleration recorded every two seconds in 30 subjects, related to three different activities (walking, climbing stairs and down stairs). The second study works with the triaxial angular rotation, for each joint, in 51 children when they completed a cycle walking under three conditions (walking, carrying a backpack and pulling a trolley). A simulation study is also developed for comparing the performance of the proposed functional LDA with respect to the corresponding multivariate and non-penalized approaches.

Gait asymmetry and rating of perceived exertion: How are they influenced by carrying a backpack and pulling a trolley?

BACKGROUND

Carrying a backpack and pulling a school trolley have been previously related to changes in spatiotemporal gait parameters.

OBJECTIVE

Analyze gait asymmetry and rating of perceived exertion carrying a backpack and pulling a trolley with different loads.

METHODS

Fifteen students from an elementary school (aged 10.1 ± 1.7 years) participated in this study. The participants walked with no bag, and carrying a backpack or pulling a trolley, both with 10%, 15% and 20% of the participant's body weight (BW). A 3D motion capture system was used to analyze the spatiotemporal gait variables. Then, the ratios, symmetry angles and gait asymmetry of the step length, swing time, and stance time were analyzed. Furthermore, the rating of perceived exertion was recorded.

RESULTS

None of the asymmetry parameters were significantly different between those who carried a backpack and those who pulled a trolley. In the backpack condition, the ratings of perceived exertion were higher among participants in the 20% BW (2.07 ± 2.09, p = 0.003) and 10% BW (0.27 ± 0.59, p = 0.004) conditions compared to those in the control condition (0.07 + 0.26). Pulling a trolley did not significantly increase the perceived exertion ratings.

CONCLUSIONS

Carrying a backpack and pulling a trolley from 10% - 20% BW did not induce gait asymmetry in children. The use of the school trolley required less subjective effort than carrying a backpack with the same loads.

Pulling a school trolley: A good kinematic option for children

This study analyzed the kinematic gait parameters associated with pulling a school trolley with different loads and the effects of the type of packing device user (backpack vs. trolley) and body side (loadedunloaded). Methods Fifty-three elementary subjects walked at a selfselected speed under four experimental conditions: without a trolley and pulling a trolley with 10%, 15% and 20% of the subject́s body weight (BW). Averages and standard deviations of spatiotemporal gait parameters and 3D kinematics of the lower limbs and thorax were obtained for the loaded and unloaded sides of the body. Results Spatiotemporal gait parameters were affected by pulling a trolley with a load of 20% BW, although the changes were not important (decrease of 0.02 units in velocity and stride length, decrease of 0.32% in single support and increase of 0.31% in double support). In the 3D kinematics analysis, the main effects of trolley load were observed in the thorax, with increased flexion as the load increased, and in the pelvis between baseline and 10%-15% BW. No interaction was found between kinematic parameters and the type of packing device user (trolley or backpack). Considering the loaded and unloaded sides of the body, the transverse plane of the thorax was the main site affected by the asymmetrical task. Conclusion Although some of the analyzed kinematic parameters were influenced by the use of a school trolley, the adaptations were minimal, and trolleys could be considered a good option for use in the transportation of school supplies.

Children require less gait kinematic adaptations to pull a trolley than to carry a backpack

To transport school materials, trolleys have been proposed for children as an alternative to carrying a backpack. However, there is limited evidence comparing the adaptations associated with carrying school trolley versus backpack. This study compared the effects of carrying a backpack and pulling a trolley on gait kinematics in children. Fifty-three students were evaluated. Children walked at self-selected speeds across a walkway with no bag (control), carrying a backpack with the 15% of child́s body weight (%BW) and pulling a trolley with the same load. Spatiotemporal gait parameters and 3D kinematics of lower extremities and thorax were computed. No significant differences were obtained in spatiotemporal parameters between pulling a trolley and control. Carrying a backpack resulted in larger kinematics gait alterations than pulling the trolley compared to control. In conclusion, pulling a school trolley (15%BW) was more similar to not carrying a bag than carrying a backpack of the same load during level walking.