About This Project

Current prosthetics are often rigid, expensive and require users to adapt to the tools' limitations. This research hypothesizes and tests whether halide perovskite-based neuromorphic memristors, can support be the foundation of a non-invasive, self-learning interface that adapts to the wearer. By utilizing triboelectric sensors to capture individual muscle twitches, the hardware mimics specific biological behaviors, thus enabling the desired movement without external assistance.

Ask the Scientists

What is the context of this research?

Traditional prosthetics lack the biological plasticity needed to integrate seamlessly with a wearer’s unique muscle signals. To bridge this gap, my research utilizes halide perovskite based memristors and triboelectric sensors for stable neuromorphic hardware. Halide perovskites are ideal for mimicking neural synapses due to their high ionic mobility, enabling hardware that absorbs and adapts to the body’s specific twitches. I hypothesize that a neuromorphic architecture using halide perovskite memristors will achieve cheaper, faster, more accurate adaptation to non linear muscle signatures than standard digital controllers, significantly improving flexibility, functionality and user acceptance of non invasive prosthetics. This is a project driven by personal ethics and pride. My previous organization shifted heavily to military projects, due to personal monetary incentives of the decision makers.

What is the significance of this project?

Human-machine relations will change by a large margin when this project finalizes results. By utilizing halide perovskite-based memristors, my goal is to provide empirical data on how synthetic synapses can process stochastic biological signals with high efficiency and low power. Moreover, the solution based synthesis is cheaper and doesn't require an extensive array of lab equipment and heavily controlled conditions. This is significant because it allows a prosthetic to learn a wearer's unique musculoskeletal signature without the need for preprogrammed algorithms or invasive and expensive surgical implants. The data generated will validate triboelectric sensors as a viable alternative for high fidelity signal acquisition. These technical benchmarks relating to signal response latency and pattern recognition accuracy on hardware level, can provide a reproducible framework for developing low cost, high performance medical devices.

What are the goals of the project?

The project has a 24 month timeline. The end goal is is to develop a functional, wearable prosthetic limb prototype that achieves personalized mechanical adaptation. The first phase focuses on the development of at least 10 stable halide perovskite memristors integrated with on a flexible substrate where triboelectric input for the wearer's skin is used as the input of the neuromorphic hardware. During the second year, I will fit the array in a prosthetic and start the realization of the prototype prosthetic. The assembly and validation of the prototype, the measuring of its power efficiency and adaptive speed against standard Complementary Metal-Oxide-Semiconductor (CMOS) based digital controllers will be the defining factor of this project's success. By maintaining independence this development process, I will ensure the resulting technology remains dedicated to humanitarian applications and not used for exploitation.