What it does
This mouse solves the problem of some Parkinson's patients who are unable to accurately control mouse movements and click operations due to hand tremors. It provides a new way to control the mouse for Parkinson's patients
Your inspiration
Through my observation on social platforms, I found that the vast majority of Parkinson's patients have extremely high costs of using computers, that is, they need to fight hand tremors to control the mouse, so I decided to design a mouse to solve the difficult situation of Parkinson's patients when using a mouse. At the beginning, I looked through the history of the mouse and found that there is a type of mouse called a trackball mouse, which controls the movement of the mouse by rolling the ball on the mouse, and can also be used in a vibration environment, so I looked through the literature.
How it works
The mouse is divided into two parts, the first half is for the wrist and palm rest, so that the palm can be more comfortable to lean on the mouse, the two buckles and the handguard can provide higher stability for the hand, in order to avoid Parkinson's patients using their fingers for mouse trajectory control and click operations, we put the sensor on the back half of the mouse, that is, Parkinson's patients can keep their wrists and palms still, and only move their arms to control the mouse trajectory. In order to adapt to different operating habits, they are small arm flow and large arm flow, that is, some users are used to leaning their elbows on the table to operate; Some users hover their elbows in the air to operate the mouse, so we placed the sensor close to the wrist, which is the part of the arm where the pulse can be clearly perceived, so that the user can adapt to either of the above two operating habits.
Design process
By using modeling clay and combining it with ergonomics, we first created a ship-shaped clay model. We found that a too-short base would stick users' arms to the desktop, causing skin to feel hot and sweaty. We hoped that users would use their elbows as a pivot point or their shoulders as a pivot point to operate the mouse. Therefore, we decided to thicken the base to 6 millimeters, so that the part of the user's elbow that contacts the mouse can be at a certain height from the desktop and does not need to be forcefully suspended. This can make using the mouse more effortless. We've solved the problem of mouse movement, but we still need to figure out how this mouse can perform a click function. Our initial idea was to design a gravity-sensing ring that users could wear on their index and middle fingers. By moving the base of their fingers up and down, they could perform a click function. But obviously, this method is not feasible. By studying the symptoms of Parkinson's patients, we found that they also experience tremors when their hands are at rest, which can affect the input of click operations. It's impossible to recognize whether it's the user's input or a click operation caused by pathological reasons. Moreover, this design should avoid the hand and return to the arm.
How it is different
Traditional assistive mice (such as those with tremor filtering and large button designs) still rely on the fine movements of fingers, while this design completely eliminates the need to control the finger muscle groups. By placing the trajectory control sensor at the pulse point of the arm (radial/ulnar artery area), users can control the cursor simply by the macroscopic movement of the forearm or upper arm, using the stability of the large muscle groups of the arm to counteract finger tremors. This design directly bypasses the finger joints that Parkinson's patients are most prone to lose control of, and fundamentally solves the core pain point of "trajectory deviation caused by hand tremors". The sensor is positioned at the proximal end of the wrist, and it uses the biomechanical characteristics of forearm rotation to map pronation/supination movements into cursor XY axis movement.
Future plans
Core technology iteration direction: Integrate electromyography sensors and inertial measurement units (IMUs), distinguish intentional movements from pathological tremors in real time through computation, develop a tremor feature learning model based on the Transformer architecture, achieve a 0.1ms-level motion compensation response, introduce flexible electronic skin technology, develop a game-specific mode, and combine eye tracking to realize "gaze point locking + micro-motion gesture control" composite operation. Product form innovation: Develop replaceable upper cover components
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