Co-adaptation interfaces, developed through user-machine collaboration, have the capacity to transform surface electromyography (sEMG) into control signals, thereby enabling external devices to facilitate or augment the sensory-motor capabilities of individuals with physical disabilities.However, the efficacy and reliability of myoelectric interfaces in untrained environments les benjamins ta?l? sweatshirt over extensive spatial range have not been thoroughly explored.We propose a user-machine closed-loop co-adaptation strategy, which consists of a multimodal progressive domain adversarial neural network (MPDANN), an augmented reality (AR) system and a scenario-based dynamic asymmetric training scheme.MPDANN employs both sEMG and Inertial Measurement Unit (IMU) data using dual-domain adversarial training, with the aim of facilitating knowledge transfer and enabling multi-source domain adaptation.The AR system allows users to perform 10 holographic object repositioning tasks in a stereoscopic mixed reality environment using a virtual prosthesis represented as an extension of the residual limb.
The scenario-based dynamic tantalizer lorac asymmetric training scheme, which employs incremental learning in MPDANN and incremental training in the AR system, enables the continuous updating and optimization of the system parameters.A group of non-disable participants and two amputees performed a five-day offline data collection in multiple limb position conditions and a five-day real-time holographic object manipulation task.The average completion rate for subjects utilizing MPDANN reached ${83}.{37}% pm {2}.{50}%$ on the final day, marking a significant improvement compared to the other groups.
These findings provide a novel approach to designing myoelectric interfaces with cross-scene recognition through user-machine closed-loop co-adaptation.