3D-printed programmable bistable mechanisms for customized wearable devices in tremor attenuation.

This research proposes a computational framework for designing a compliant bistable mechanism and fabricating it using 3D printing for customized medical applications. The proposed method reduces upper limb tremors, taking advantage of the nonlinear mechanical properties of flexible structures. The model's development and execution on a single platform streamlines integrated inverse design and simulation, simplifying the customization process. A synthetic human arm model, built to imitate a human wrist, was scanned with a light detection and ranging (LiDAR) sensor to customize the 3D model of the bistable structure. Afterwards, the arm model was used to test the bistable mechanism. Automating the inverse design process with a deep neural network (DNN) and evolutionary optimization decides the optimal bistable mechanism configurations for stiffness and vibration attenuation. The pseudo-rigid-body model (PRBM) of the bistable mechanism was developed to train the machine learning (ML) model in the inverse design, making it computationally affordable to find the optimal parameters of bistable structure for a specific mechanical response based on tremor characteristics. Experimental results showing up to 87.11 % reduction in tremor power while weighing only 27 g to reduce vibrations in various situations suggest its use in 4D printing of wearable orthotic devices for Parkinsonian tremors and related diseases.