URHAND: Hand prosthesis for identifying objects for activities of daily living

Ramos, Orion; Casas, Diego; Cifuentes, Carlos A.; Jimenez, Mario F.

Abstract

This work introduces URHAND, an innovative prosthetic hand designed to succeed in the identification of objects used in daily life activities, addressing a critical gap in the field of hand prosthetics and artificial intelligence. By leveraging advanced 3-D printing technologies, URHAND enhances functionality and adaptability with ten degrees of freedom (DoFs) and a unique underactuated mechanism. Dynamixel MX-106 motors provide precise finger control, while force-sensitive sensors enable the implementation of machine learning (ML) algorithms. The primary objective of this study is to create a comprehensive dataset derived from standardized objects associated with activities of daily living (ADLs) and standardized protocols, a necessary step to advance the state of the art. The dataset, including motor positions, loads, currents, and force sensing resistor (FSR) values, supports four classification problems as follows: 1) using all measured variables to identify objects; 2) using only motor positions; 3) using FSR sensor data; and 4) identifying grip types with FSR data. ML training, conducted using the PyCaret library, reveals that CatBoost, extra tree classifier, and random forest are the top-performing algorithms for object and grip-type identification. The results underscore the importance of FSR data in achieving high precision, demonstrating a novel contribution to optimizing object handling in daily activities. This work represents a significant advancement in the application of artificial intelligence and prosthetics, providing essential information for future developments in the field.

• Journal Article Type: Article
• Acceptance Date: Aug 24, 2024
• Online Publication Date: Oct 1, 2024
• Publication Date: Oct 1, 2024
• Deposit Date: Oct 31, 2024
• Publicly Available Date: Nov 1, 2024
• Journal: IEEE Transactions on Instrumentation and Measurement
• Print ISSN: 0018-9456
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 73
• DOI: https://doi.org/10.1109/tim.2024.3470013
• Public URL: https://uwe-repository.worktribe.com/output/13283320