Among our expansive list of goals, we hope to expand the range of motion and precision of the prosthetic hand by allowing the other digits to mechanically contract. The thumb is responsible for 40% of overall manual dexterity, but future iterations will allow for more nuanced finger control (Lin et al, 2012). By adding more EMG channels with more advanced signal processing methods, we can unlock a higher degree of control with the prosthetic hand. Lastly, gesture recognition and EMG training may allow users to perform more complex hand motions greater precision. This prosthetic could evolve from a tool into an extension of the user's body without any surgical intervention.
Figure 2. Self-healing polymers.
In future versions, we plan to explore self-healing polymers that provide a more durable, impact-resistant material that can endure in a range of different environmental conditions. Our current rendition allows for low-cost, rapid prototyping, but we hope to explore elastomeric joints with variable stiffness components for increased utility and user comfort. Sweat-resistant coatings will be investigated along with antimicrobial linings to improve hygienic practices. Lastly, sustainable practices will be explored by integrating recycled filament to ensure the responsible production of our prosthetic devices.
Figure 3. Prosthetic hand.
No two people are the same, so why should prosthetics be? Our long-term goal is to make our device adaptive: learning from the user's unique muscle activations, behaviors, and daily routines. With machine learning algorithms, future prosthetics could calibrate control settings, adjust grip strength, and recognize frequently used hand motions. These prosthetics can evolve over time, not just in software but in structure as well. Our modular design allows a user to swap out parts depending on their needs. This allows individuals to modify their own device in rural environments or regions of military conflict without needing to replace the entire device.
The connection between a prosthetic hand and an individuals neural network.
Prosthetic control is largely dependent upon successful nervous system integration. Using technologies such as targeted muscle reinnervation (TMR), users could regain the ability to feel sensations such as pressure, temperature, and motion. Our vision is to create a closed-loop system where the prosthetic can receive the user's muscle signals while also sending sensory information back into the body. This can be achieved with electrical stimulation pads or even direct nerve interfaces.
Image sources: OpenAI