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However, existing literature underscores the concurrent impact of multiple factors on gait. This study identifies five pivotal factors\u2014walking speed, surface slope, load carried, carrying method, and footwear\u2014as 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\u2014these 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.<\/jats:p>","DOI":"10.1038\/s41598-023-43645-2","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T11:01:44Z","timestamp":1696417304000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Biomechanical investigation of tasks concerning manual materials handling using response surface methodology"],"prefix":"10.1038","volume":"13","author":[{"given":"Amit M.","family":"Adhaye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhananjay A.","family":"Jolhe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay R.","family":"Loyte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuvarajan","family":"Devarajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subash","family":"Thanappan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"issue":"1","key":"43645_CR1","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/scientificamerican0198-48","volume":"278","author":"DE Ingber","year":"1998","unstructured":"Ingber, D. 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