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.

There are two quite different kinds of approaches to the generation of motions for a humanoid robot. The first one optimizes for the robot's pose, i.e. the movement should look similar to human motions (Zordan and Hodgins, 1999), (Dasgupta and Nakamura, 1999), (Riley et al., 2003), (Kuffner et al., 2003), (Erol et al., 2003), and (Ijspeert et al., 2002). The second one attempts to make the robot perform a given task and is thus focused on the accurate movements of the robot's end effectors (Asfour et al., 2000) and (Yigit et al., 2003). In this work, we present a completely new scheme for the motion generation of a humanoid robot called context oriented motion generation. This scheme incorporates the pose oriented approach and the task oriented approach. It is based on a classical trajectory generator and a new context specific motion classifier developed by our group. The trajectory generator creates a set of trajectories and thereby ensures that the given motion task is accomplished by all trajectories. Afterwards, the motion classifier evaluates this set of motions with respect to the given context and selects the optimal trajectory