Walking is an essential part of almost all activities of daily living. We use different gait patterns in different situations, e.g., moving around the house, performing various sports, or when compensating for an injury. However, how humans perform this gait tailoring remains a partially unknown process. To this end, the influence of various performance metrics on the optimality and diversity of gait patterns can provide us with more insight. To analyse gait in terms of pattern diversity and performance metrics related to physical aspects, such as joint torque, fatigue, and manipulability, we propose a multi-metric gait analysis framework that simultaneously accounts for these parameters. We used a recorded set of versatile gait patterns that are already dynamically stable and physiologically feasible. To that end, 45 gait variations-varying in stride length, step height, and walking speed-were recorded in a motion capture experiment. Results of analysis using the recorded dataset are presented for a baseline case (with all optimisation weights set to one), which serves as the first step for future research, in particular giving insights into specific aspects of the gait, e.g., joint loading, long-term performance, and capacity to sustain ground reaction forces.@en

In this work, we present a novel control approach to human-robot collaboration that takes into account ergonomic aspects of the human co-worker during power tool operations. The method is primarily based on estimating and reducing the overloading torques in the human joints that are induced by the manipulated external load. The human overloading joint torques are estimated and monitored using a whole-body dynamic state model. The appropriate robot motion that brings the human into the suitable ergonomic working configuration is obtained by an optimisation method that minimises the overloading joint torques. The proposed optimisation process includes several constraints, such as the human arm muscular manipulability and safety of the collaborative task, to achieve a task-relevant optimised configuration. We validated the proposed method by a user study that involved a human-robot collaboration task, where the subjects operated a polishing machine on a part that was brought to them by the collaborative robot. A statistical analysis of ten subjects as an experimental evaluation of the proposed control framework is provided to demonstrate the potential of the proposed control framework in enabling ergonomic and task-optimised human-robot collaboration.

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