An artificial hand, or prosthetic hand, works primarily through two main methods: motor-powered or myoelectric-powered systems.
Motor-Powered Artificial Hands
These simpler prosthetics often utilize a switch mechanism, typically located on the wrist or another easily accessible part of the arm. When activated, the switch triggers a motor within the hand to perform a pre-programmed action, such as gripping.
- Mechanism: A switch activates a motor.
- Action: The motor controls the hand's movements (e.g., gripping).
- Control: Limited to pre-defined actions.
Myoelectric-Powered Artificial Hands
These more advanced prosthetics use the body's own electrical signals to control the hand.
- Signal Acquisition: Electrodes placed on the skin of the residual limb pick up the faint electrical signals generated by muscle contractions. These signals are similar to those recorded during an electromyography (EMG) test used in medical diagnostics.
- Signal Processing: The electrical signals are then sent to a small computer processor within the prosthetic hand. This processor interprets the patterns of muscle activity.
- Motion Execution: Based on the interpreted signals, the processor instructs motors within the hand to move specific fingers or joints, allowing for a variety of grips and movements. The intensity of the muscle contraction often corresponds to the strength of the grip.
Here's a breakdown in a table format:
| Feature | Motor-Powered Hand | Myoelectric-Powered Hand |
|---|---|---|
| Power Source | External switch triggering a motor | Electrical signals from muscle contractions |
| Control Method | Simple on/off switch | Complex signal interpretation and processing |
| Movement | Pre-defined actions (e.g., gripping) | Variable grips and movements based on muscle control |
| Complexity | Less complex | More complex |
Myoelectric hands offer a more intuitive and natural control experience compared to motor-powered prosthetics, allowing users to perform a wider range of tasks with greater precision. The advancements in machine learning also allow for even more sophisticated control, where the hand can learn and adapt to the user's specific needs and movements.
