Exploring the Impact of Machine-Learned Predictions on Feedback from an Artificial Limb

Communicative capital: a key resource for human-machine shared agency and collaborative capacity

In this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human-machine collaborations have a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce communicative capital as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.

The Influence of Training with Visual Biofeedback on the Predictability of Myoelectric Control Usability

Studies have shown that closed-loop myoelectric control schemes can lead to changes in user performance and behavior compared to open-loop systems. When users are placed within the control loop, such as during real-time use, they must correct for errors made by the controller and learn what behavior is necessary to produce desired outcomes. Augmented feedback, consequently, has been used to incorporate the user throughout the training process and to facilitate learning. This work explores the effect of visual feedback presented during user training on both the performance and predictability of a myoelectric classification-based control system. Our results suggest that properly designed feedback mechanisms and training tasks can influence the quality of the training data and the ability to predict usability using linear combinations of metrics derived from feature space. Furthermore, our results confirm that the most common in-lab training protocol, screen guided training, may yield training data that are less representative of online use than training protocols that incorporate the user in the loop. These results suggest that training protocols should be designed that better parallel the testing environment to more effectively prepare both the algorithms and users for real-time control.