SUMMARY
This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist.