Prediction of Metacarpophalangeal joint angles and Classification of Hand configurations based on Ultrasound Imaging of the Forearm

TL;DR

This paper presents a deep learning approach using CNNs and SVC to predict MCP joint angles and classify hand configurations from forearm ultrasound images, enabling real-time hand motion recognition for interactive systems.

Contribution

It introduces a CNN-based pipeline for MCP joint angle prediction and an SVC classifier for hand configuration recognition from ultrasound data, with real-time implementation.

Findings

Average root mean square error of 7.35 degrees in MCP angle prediction

Successful classification of hand configurations relevant to daily activities

Real-time processing at 6.25 - 9.1 Hz for interactive applications

Abstract

With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for interacting with digital systems, AR/VR interfaces, and physical robotic systems. Hand movement recognition is widely used to enable this interaction. Hand configuration classification and Metacarpophalangeal (MCP) joint angle detection are important for a comprehensive reconstruction of the hand motion. Surface electromyography and other technologies have been used for the detection of hand motions. Ultrasound images of the forearm offer a way to visualize the internal physiology of the hand from a musculoskeletal perspective. Recent work has shown that these images can be classified using machine learning to predict various hand configurations. In this paper, we propose a Convolutional Neural Network (CNN) based deep learning pipeline for predicting the MCP joint angles. We supplement our results by using a Support Vector Classifier (SVC) to classify the ultrasound information into several predefined hand configurations based on activities of daily living (ADL). Ultrasound data from the forearm was obtained from 6 subjects who were instructed to move their hands according to predefined hand configurations relevant to ADLs. Motion capture data was acquired as the ground truth for hand movements at different speeds (0.5 Hz, 1 Hz, & 2 Hz) for the index, middle, ring, and pinky fingers. We were able to get promising SVC classification results on a subset of our collected data set. We demonstrated a correspondence between the predicted MCP joint angles and the actual MCP joint angles for the fingers, with an average root mean square error of 7.35 degrees. We implemented a low latency (6.25 - 9.1 Hz) pipeline for the prediction of both MCP joint angles and hand configuration estimation aimed at real-time control of digital devices, AR/VR interfaces, and physical robots.