In this work, a surface skin electromyography(sEMG)-based control solution for elbow wearable assistive devices during load lifting tasks is presented. The goal of the controller consists in limiting the user's muscle activity during the task execution, in such a way that the assistive device can partially compensate the load-related biceps muscle effort. Since sEMG-driven control strategies based on the estimation of the joint torques generally requires complex task- and subject-dependent training sessions for tuning the control algorithms, here a more direct control approach is proposed, based on a muscle activity error related proportional-integral action together with an double-threshold activation logic. The controller's parameters are easily set by means of a fast, online and automatic subject calibration procedure, ensuring a simple adjustability to different users. An experimental phase has been conducted in order to evaluate the sEMG-based control performance involving four healthy subjects, using as wearable assistive device a twisted string action module, which is particularly suitable for assistive applications because of its lightness and compactness. Results show that the control strategy is able to successfully limit the EMG activity of the subjects during the lifting tasks, providing preliminary outcomes and promising possibilities for the use of twisted string-based technologies to assist human joints and muscles.