Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, 440010, Nagpur, Maharashtra, India.
Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, Tamilnadu, 602104, India.
Sci Rep. 2023 Oct 4;13(1):16718. doi: 10.1038/s41598-023-43645-2.
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.
在典型的手动搬运过程中，行走模式的变化由各种因素决定，例如搬运的物品、搬运的频率、行走表面等。传统的步态分析协议通常在与特定活动或病理学相关的特定范围内评估个体因素。然而，现有文献强调了多个因素对步态的共同影响。本研究确定了五个关键因素——行走速度、表面坡度、搬运物品的重量、搬运方法和鞋类——作为步态改变的因素。为了应对手动搬运活动中的风险因素，我们提出了一种基于实验设计的多任务步态分析的独特方法。揭示手动搬运属性与人体步态之间的关系对于制定有效的干预策略至关重要。我们通过采用面心中央复合设计实验，对 15 名健康男性参与者的五个输入因素进行了优化。共测试了 29 种输入因素组合，生成了一个包含 18 个运动学步态参数（如步频、步长等，使用惯性测量系统测量）的综合数据集，包括因素的孤立影响以及两因素相互作用及其相应响应。结果说明了提高个体步态表现的最佳输入因素场景——包括穿着合适的鞋子、使用背包搬运物品，以及在中等坡度上以最小的负载保持适度的行走速度。研究发现行走速度和负载大小是步态力学的主要影响因素，其次是选择的搬运方法。因此，所获得的见解主张根据结果细化手动搬运任务，通过建议进行姿势矫正的干预措施，有效降低肌肉骨骼疾病的风险。
