Strength training improves overall health, well-being, physical appearance, and sports performance. There are four major factors that affect training efficacy in a training session: exercise type, number of repetitions, movement velocity, and workload. Prior research has used wearable sensors to detect exercise type, number of repetitions, and movement velocity while training. However, detecting workload still requires instrumentation of exercise equipment such as exercise machines, or free weights. This paper presents MuscleSense, an approach that detects training weight through wearable devices. In particular, MuscleSense uses various regressors to predicting weight using signals from wearable sEMG sensors mounted on user’s arm or forearm. We evaluated the effects of sensor placement and collected training data from 20 participants. The results from our user study show that MuscleSense achieves Root Mean Square Error(RMSE) of 0.683kg in sensing workload through sensors data from both forearm and arm using Support Vector Regressor of linear kernel.