Controlling a multi-degree of freedom upper limb prosthesis using foot controls: user experience

Abstract

Multifunction upper limb prostheses require many user control inputs to operate. Foot controls offer additional control input options for such advanced devices, yet have had minimal study. This study found that foot controls were a viable option for controlling multifunction upper limb prostheses. Most of the 36 subjects in this study were willing to adopt foot controls to control the multiple degrees of freedom of the DEKA Arm. With training and practice, all users were able to develop the psychomotor skills needed to successfully operate food controls. Some had initial difficulty, but acclimated over time.

Cortical neuroprosthetics from a clinical perspective

“…Importantly, current non-BMI approaches (e.g. using mechanical switches or joysticks) can have limitations when scaling up to control a prosthetic device with a large number of DOF (Resnik et al, 2014; Resnik et al, 2012). In contrast, direct neural control using intracortical approaches aims to allow intuitive and naturalistic control of the required DOF to achieve complex functional restoration.…”

Training protocol for a powered shoulder prosthesis

“…Within the first 6 mo, DEKA introduced the first prototype of end-point control, and the training protocol for Gen 2 SC users was modified accordingly. The training protocol was also modified over the course of the 3 yr Optimization Study as key features of the DEKA Arm evolved, such as foot controls [10], end-point control [9], and hardware [9].…”

“…Features of the SC DEKA Arm with end-point control and foot controls were described in detail in three articles [9][10][11]. The SC Arm is shown in figure 1.…”

“…The user's control setup and training time were discussed [11]. A separate article reported Optimization Study participants' reactions to and experiences with learning foot controls [10].…”

Reciprocal Kinematic Control: using human-robot dual adaptation to control upper limb assistive devices

“…For that purpose, the most widespread solutions to obtain user's motor intention use physiological signals (electromyograms or electroencephalograms e.g.) [1,2], distal functional joints (for instance, head or foot motions control the endeffector position and/or orientation) [3,4] or inter-joint synergies models [5]. Despite interesting results, they all still have important limitations: the first two are neither natural nor intuitive and suffer from robustness issues, the third one does not allow very versatile devices.…”