Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensorcontrolled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single-and multi-subject based parameter transfer on LSTM models trained for "sensor-tojoint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single-and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.
I. INTRODUCTIONWearable physiological sensors may support decisionmaking, training, and device control across a variety of clinical, health monitoring, fitness, and wearable robotics tasks [1], [2], [3], [4]. The ability to generalize physiological sensing models between individuals is often a significant hurdle in model development for more widespread adoption in these domains. Among these use cases, wearable robotics (e.g. exoskeletons) have stricter requirements for temporal granularity and specificity of state sensing than other types of activity monitoring, given the need for real-time detection of user movement and intent [5]. For this work, we focus specifically on the requirements for wearable sensing models for the wearable robotics and other real-time joint dynamics estimation use cases.