In this paper, special attention is given to the hand. The literature provides solutions allowing the hand gestures recognition and/or object recognition for virtual reality, robotic applications, and so on. These solutions rely mainly on computer vision and data gloves. From this finding, we decided to develop our data glove prototype. The data glove is exploited to recognize common objects in the kitchen that the person can hold (e.g., hold a fork) while he/she performs basic daily activities such as drink a glass of water. The proposed approach is straightforward, cheap ($\sim$260 $ in USD) and efficient ($\sim$100%). Moreover, the designed data glove gives easy and direct access to the raw data provided by sensors. Besides, a comparison between classical machine learning algorithms (e.g., CART, Random Forest) and a deep neural network is given. Finally, the proposed prototype is described in a way that researchers can reproduce it for any applications involving the object recognition with the hand.

在本文中，要特别注意手。文献提供了允许针对虚拟现实，机器人应用等的手势识别和/或对象识别的解决方案。这些解决方案主要依靠计算机视觉和数据手套。根据这一发现，我们决定开发数据手套原型。数据手套被用来识别人在执行诸如喝一杯水之类的基本日常活动时可以握住（例如，握住叉子）的厨房中的常见物体。所提出的方法简单明了，便宜（$〜$260 $ in USD）and高效（$〜$100％）。此外，设计的数据手套可轻松直接访问传感器提供的原始数据。此外，还比较了经典机器学习算法（例如CART，随机森林）和深度神经网络。最后，对所提出的原型进行了描述，以使研究人员可以将其复制到涉及用手进行物体识别的任何应用中。