Aiming at the trajectory planning problem considering kinematic and dynamic manipulability optimizations, this article proposes an acceleration level manipulability maximization (ALMM) scheme to solve the problem in acceleration level for the first time. The manipulability index with nonlinear characteristics is reconstructed in acceleration level by a novel multilevel simultaneous processing strategy. The minimum joint velocity index is introduced to ensure the convergence of the system and maintain the velocity at a low level. Subsequently, the ALMM scheme, which includes pose maintenance, manipulability optimization, joint velocity minimization, and joint physical limit avoidance, is constructed and further formulated as a unified quadratic program. Then, a recurrent neural network with rigorously provable convergence is designed for the ALMM method. Simulations and physical experiments illustrate that the ALMM scheme can accomplish the manipulability optimization task excellently. Comparisons further verify the superiority of this scheme.

Although smartwatches are gaining popularity among mainstream consumers, the input space is limited due to their small form factor. The goal of this work is to explore how to design non-touchscreen gestures to extend the input space of smartwatches. We conducted an elicitation study eliciting gestures for 31 smartwatch tasks. From this study, we demonstrate that a consensus exists among the participants on the mapping of gesture to command and use this consensus to specify a user-defined gesture set. Using gestures collected during our study, we define a taxonomy describing the mapping and physical characteristics of the gestures. Lastly, we provide insights to inform the design of non-touchscreen gestures for smartwatch interaction.