The online adaptation of exoskeleton control based on muscle activity sensing offers a promising approach to personalizing exoskeleton behavior based on the user's biosignals. While electromyography (EMG)-based methods have demonstrated improvements in joint torque estimation, EMG sensors require direct skin contact and extensive post-processing. In contrast, force myography (FMG) measures normal forces resulting from changes in muscle volume due to muscle activity. We propose an FMG-based method to estimate knee and ankle joint torques by integrating joint angles and velocities with muscle activity data. We learn a model for joint torque estimation using Gaussian process regression (GPR). The effectiveness of the proposed FMG-based method is validated on isokinetic motions performed by ten participants. The model is compared to a baseline model that uses only joint angle and velocity, as well as a model augmented by EMG data. The results indicate that incorporating FMG into exoskeleton control can improve the estimation of joint torque for the ankle and knee joints in novel task characteristics within a single participant. Although the findings suggest that this approach may not improve the generalizability of estimates between multiple participants, they highlight the need for further research into its potential applications in exoskeleton control.