Sarah Griffin successfully defended her dissertation entitled “A mechanistic model for predicting ladder fall risk: from biomechanics laboratory to wearable technologies”!

Committee Chair was Kurt Beschorner and Committee Members included April Chambers, Eni Halilaj, and Mark Redfern.

See below for an abstract of her work.

Falls from ladders are a leading cause of injury and death in the workplace, yet there is insufficient knowledge on what causes them to occur. This dissertation performed biomechanical analyses on over a hundred people climbing ladders to identify factors associated with fall risk, with the long-term goal of reducing the frequency of falls. Aim 1 identified biomechanical factors associated with the required friction (RCOF) of ladder climbing. Notably, the foot angle and body angle are associated with the RCOF, indicating that foot and body orientation during ladder climbing may be associated with slip risk. Additionally, a new metric called the foot-body coupling angle was developed to quantify coordination between the foot and body angles. This novel metric was a strong predictor of RCOF, indicating that minimizing foot-body coupling angle may be the safest climbing strategy. Aim 2 correlated individual factors (anthropometrics, age, sex) with metrics of fall risk. Four destabilizing and compensatory events were identified: kicking a rung, foot readjustments, skipping rungs, and double stepping. The occurrence of these events, RCOF, and foot placement location were compared to range of individual factors. Leg length, waist-to-height ratio, strength, age, and sex were associated with double stepping, skipping rungs, and foot placement location but not other fall risk metrics. These findings indicate that individual factors only partially influence fall risk but substantially influence a person’s climbing strategy. Aim 3 developed a predictive model using accelerometer-based data to predict an individual’s risk of experiencing a destabilizing event. This was the first application of acceleration-based metrics commonly used to measure stability and smoothness in gait to a ladder use task. The models were highly predictive, especially for rung kicking. Thus, a person’s risk of experiencing a destabilizing event may be associated with their climbing mechanics. This model can be deployed into the field to empower workers with more knowledge on their personal safety and to improve ladder safety training methods. Overall, this dissertation identified relationships between biomechanics, individual factors, and ladder fall risk. These findings can be used to improve ladder safety training and inform changes in ladder design to reduce fall risk.