Interpretable Locomotion Prediction in Construction Using a Memory-Driven LLM Agent With Chain-of-Thought Reasoning

Construction workers face significant risks of work-related musculoskeletal disorders (WMSDs), driven by repetitive tasks, heavy load handling, and non-neutral postures in dynamic, unpredictable environments [1, 10]. In the U.S., construction workers experience an 11% higher WMSD rate than the average across industries, with the back and shoulders most affected [10]. While exoskeletons show promise in reducing physical strain--passive designs lowering back muscle activity by 10-40% and active ones achieving up to 80% reductions across multiple regions [5]--their practical deployment remains limited by discomfort and poor alignment with human movements, particularly in construction settings [6]. Central to these limitations is the challenge of accurately recognizing user intent across varied tasks, a gap that restricts effective collaboration [3, 34]. This misalignment heightens safety risks, as powered exoskeletons may generate destructive forces if their controlled output deviates from the user's intent [34]. Addressing this locomotion intent recognition challenge is pivotal to unlocking effective exoskeleton assistance in construction, particularly for diverse, safety-critical tasks like ladder climbing and obstacle navigation. Traditional evaluation of assistive technologies like lower-limb exoskeletons has focused narrowly on routine tasks such as straight walking [27], neglecting these critical locomotion modes and requiring a shift beyond conventional control paradigms that lack flexibility for dynamic contexts. Construction tasks are highly variable, requiring workers to adapt to shifting demands, irregular workflows, and unstructured environments where movement patterns are unpredictable [10]. This variability complicates the implementation of assistive technologies, as rigid control approaches struggle to accommodate rapid task transitions and environmental uncertainty.