English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Many people suffer from the loss of a limb. Learning to get by without an armor hand can be very challenging, and existing prostheses do not yet fulfil theneeds of individuals with amputations. One promising solution is to providegreater communication between a prosthesis and its user. Towards this end, wepresent a simple machine learning interface to supplement the control of arobotic limb with feedback to the user about what the limb will be experiencingin the near future. A real-time prediction learner was implemented to predictimpact-related electrical load experienced by a robot limb; the learningsystem's predictions were then communicated to the device's user to aid intheir interactions with a workspace. We tested this system with fiveable-bodied subjects. Each subject manipulated the robot arm while receivingdifferent forms of vibrotactile feedback regarding the arm's contact with itsworkspace. Our trials showed that communicable predictions could be learnedquickly during human control of the robot arm. Using these predictions as abasis for feedback led to a statistically significant improvement in taskperformance when compared to purely reactive feedback from the device. Ourstudy therefore contributes initial evidence that prediction learning andmachine intelligence can benefit not just control, but also feedback from anartificial limb. We expect that a greater level of acceptance and ownership canbe achieved if the prosthesis itself takes an active role in transmittinglearned knowledge about its state and its situation of use.

Using Learned Predictions as Feedback to Improve Control and Communication with an Artificial Limb: Preliminary Findings