Helen
Abstract:Existing robotic lower-limb prostheses use autonomous control to address cyclic, locomotive tasks, but they are inadequate to operate the prosthesis for daily activities that are non-cyclic and unpredictable. To address this challenge, this study aims to design a novel electromyography (EMG)-driven musculoskeletal model for volitional control of a robotic ankle-foot prosthesis. This controller places the user in continuous control of the device, allowing them to freely manipulate the prosthesis behavior at will. The Hill-type muscle model was used to model a dorsiflexor and a plantarflexor, which functioned around a virtual ankle joint. The model parameters were determined by fitting the model prediction to the experimental data collected from an able-bodied subject. EMG signals recorded from ankle agonist and antagonist muscle pair were used to activate the virtual muscle models. This model was validated via offline simulations and real-time prosthesis control. Additionally, the feasibility of the proposed prosthesis control on assisting the user's functional tasks was demonstrated. The present control may further improve the function of robotic prosthesis for supporting versatile activities in individuals with lower-limb amputations.
Abstract:We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. This framework is demonstrated through the design and implementation of a novel privacy-preserving inference information flow where we pass homomorphically encrypted activation signals through a split neural network for inference. We show that splitting the model further up the computation chain significantly reduces the computation time of inference and the payload size of activation signals at the cost of model secrecy. We evaluate our proposed flow with respect to its provision of the core structural transparency principles.