Abstract
This paper describes a portable, prosthetic control system and the first at-home use of a multi-degree-of-freedom, proportionally controlled bionic arm. The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional control. We describe (a) how the system trains motor control algorithms for use with an advanced bionic arm, and (b) the system's ability to record an unprecedented and comprehensive dataset of EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution description of daily use—essential information to determine clinical relevance and improve future research for advanced bionic arms.
Highlights
High temporal resolution of the position and forces applied to the prosthesis is necessary to describe the interactive and refined movements made possible with proportionally controlled prostheses
A portable take-home system designed to research advanced bionic arms should meet several criteria for optimal performance and data collection: (a) the system must accurately and efficiently control the prosthesis; (b) training of the control algorithm must not be too long or burdensome to prevent its daily use—and should include the ability to quickly load a previously trained control algorithm; (c) high-temporal-resolution data should be stored automatically so that researchers can study at-home use without influencing the users with in-person observation; and (d) the system must be easy to use and allow the user to adjust control preferences
We have described a portable, prosthetic control system and the first at-home use of a multi-degree-of-freedom, FIGURE 5 | Two-handed activities of daily living at home using a bypass socket and the portable system: (A) using scissors; (B) donning a sock; and (C) folding a towel
Summary
Electromyography (EMG) from the residual forearm has been used to control commercially available and research-grade prosthetic arms (Kuiken et al, 2016; Hargrove et al, 2017; Ottobock, 2017; Touch Bionics Inc, 2017; Wendelken et al, 2017; George et al, 2018; Page et al, 2018; Perry et al, 2018; Mobius Bionics, 2020). Research has demonstrated proportional control of multiple, independent degrees of freedom (DOFs) (Davis et al, 2016; George et al, 2018; Page et al, 2018), commercially available prostheses still suffer from a variety of limitations (Biddiss and Chau, 2007), including limited number of pre-determined grips (Touch Bionics Inc, 2017), temporal delay due to sequential inputs used to select grips (Ottobock, 2017; Mobius Bionics, 2020), fixed output force (e.g., from traditional classifier algorithms) (Resnik et al, 2018a), extensive training that lasts days to weeks (Resnik et al, 2017, 2018a, 2019), and non-intuitive methods of control [e.g., inertial measurement units (IMUs) on residual limb or feet] (Resnik et al, 2018b; Mobius Bionics, 2020). These data are necessary to describe key aspects of actual prosthesis use: revealing when objects were manipulated; whether movements were performed unilaterally or bilaterally (for bilateral amputees); which grasps were preferred; how often each DOF was used; and when new inter-digit collaborative movements were employed
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