# Vision Controlled Sensorized Prosthetic Hand

arXiv: https://arxiv.org/abs/2407.12807 · HTML: https://mlsanity.com/paper/2407.12807/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Attentive Contexts for Object Detection](https://mlsanity.com/paper/1603.07415/) (2016)
- [LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems](https://mlsanity.com/paper/1705.05922/) (2017)
- [Real-time Mask Detection on Google Edge TPU](https://mlsanity.com/paper/2010.04427/) (2020)

## Built on by (most cited first, 2 of 2)

- [AI-Powered Camera and Sensors for the Rehabilitation Hand Exoskeleton](https://mlsanity.com/paper/2408.15248/) (2024)
- [A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities](https://mlsanity.com/paper/2504.15654/) (2025)

## Abstract (arXiv)

This paper presents a sensorized vision-enabled prosthetic hand aimed at replicating a natural hand's performance, functionality, appearance, and comfort. The design goal was to create an accessible substitution with a user-friendly interface requiring little to no training. Our mechanical hand uses a camera and embedded processors to perform most of these tasks. The interfaced pressure sensor is used to get pressure feedback and ensure a safe grasp of the object; an accelerometer is used to detect gestures and release the object. Unlike current EMG-based designs, the prototyped hand does not require personalized training. The details of the design, trade-offs, results, and informing the next iteration are presented in this paper.