The influence of walking surface types on gait parameters, kinematics and worker perceptions in relation to slips, trips, and fall incidents in the construction industry

Slips, trips, and falls (STFs) are serious hazards to workers in the construction industry who work every day in demanding and challenging environments. Even with the continuously high reported number of incidents and high direct and indirect costs associated with STF related injuries, there is still a lack of research to help safety professionals and company executives in decision-making regarding STF prevention on construction sites. Previous research related to STFs has come from the medical industry and has been mostly focused on degenerative health conditions and concerns related to the elderly population. With the use of the newest technology of wearable devices, gait analysis is more easily studied in real time and has led to new research opportunities in occupational studies that were not practical in the past.This mixed methods study utilized a laboratory-based approach where participants walked on five common types of surfaces found on construction sites including steel decking, floor, gravel, mud, and plywood. In a series of different sessions and trials, the participants wore two wearable technology sensor systems (Xsens and McRoberts) to record their gait and joints' posture characteristics on each surface. The gait characteristics that were collected were gait duration (to study gait speed), step length, gait symmetry, and joint angles (ankle, hip, and knee). The data was compared between the different surfaces and with load versus without load to see if there were any significant differences in the participants' gait characteristics and joint angles. Quantitative data from the Xsens was used to produce line graphs to be interpreted by observation and quantitative data from the McRoberts system was analyzed by Repeated Measures ANOVA statistical analysis using IBM SPSS statistics software version 29.In addition to the laboratory data, the researcher also collected qualitative data based on the perceptions of laboratory participants as well as construction workers. Participants that completed the walking trials within the laboratory were provided questionnaires following their final session. The questions focused on the participant's perception of STFs regarding the walking surfaces and their decision making during their walking trials. And were reviewed for reoccurring themes and common responses. A sample of construction workers were interviewed based on their perception of STF hazards in their work environment. The construction workers' responses were analyzed using concept clusters.The data analysis on joint angles showed little difference in the hip joint between different surfaces but indicated that on more complex surfaces, the ankle joint was the most inconsistent and experienced a smaller range of motion especially in the plantar-flexion direction. On these surfaces, the knee joint was most likely to have had less range of motion as well. Overall, gait speed was not significantly affected by the type of walking surface, although participants did walk slower on the steel decking and plywood. Participants also took shorter steps on the steel decking and plywood surfaces compared to all other surfaces although the only significant differences were that participants took significantly shorter steps on the steel decking surface compared to the floor, gravel, and mud surfaces. In all planes of movement, gait symmetry was more symmetrical on complex surfaces (notably steel decking and gravel) than most others.Construction walking surfaces create an elevated risk of falling. This study found that gait characteristics were consciously altered to adjust for the walking surface based on the participants' perception of the fall risk, which participants provided on their post-experiment questionnaires. Participants altered their gait according to the complexity of the walking surface to maintain balance and prevent slips, trips, and falls. To interpret the data results, both the laboratory quantitative data, the interviews, and questionnaire qualitative data must be considered collectively. The p tential interactions between these data sets are relevant to the conclusions.