# Novel near E-Field Topography Sensor for Human–Machine Interfacing in Robotic Applications

## TL;DR

A new non-contact sensor using near E-field sensing is developed to detect muscle activity for robotic human-machine interfaces, showing promising results in tracking hand and finger movements.

## Contribution

A novel near E-field topography sensor is introduced for HMI applications, offering low-cost and low-noise muscle activity sensing.

## Key findings

- The sensor's performance was validated through accuracy, hysteresis, and resolution evaluations.
- The sensor's raw output correlates with hand and finger movements, indicating muscle activation.
- A CNN achieved joint angle prediction with RMSE under six degrees for thumb and wrist movements.

## Abstract

This work investigates a new sensing technology for use in robotic human–machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human–machine interfaces.