Object recognition in performed basic daily activities with a handcrafted data glove prototype.

• Performing object recognition with a new prototype of data glove. • Creation of a unique dataset containing 14 grasped objects. • Experiments (basic daily activities) conducted by 19 participants. • Comparing classical machine learning algorithms and one deep learning model. • Results of the object recognition is superior to 95%. 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 (∼ 260 $ in USD) and efficient (∼ 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. [ABSTRACT FROM AUTHOR]