Smart Prosthetic Limbs using AI prediction capacity to perform delicate and comp

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  • #664
    SenseHub Admin
    Keymaster

    Completely different approach from brain-controlled prosthetics

    The goal of this project is to address the issue, using an economical and easily reproducible setup as open source tools.

    Evaluating, as Open Source recommends, a variety of issues including ethical concerns by contributors representing a wide range of viewpoints.

    In general, conforming to commercially available tech, specification, standards and current regulatory guidance.

    Deep Learning Training and Inference to Identify the pattern of activity to perform an action (control a mechanical limb to perform a complex motor task) without our really being aware of it. For example, consider the delicate and complex tasks hands can perform, such as writing in calligraphy or playing the violin.
    AI and machine learning algorithms are becoming increasingly good at predicting next actions in videos.

    Smart Prosthetic limbs, where equipment, and sensors are all connected to a network, allowing software control programs to autonomously run entire processes and self-optimize performance by integrating data and adapting to conditions in real time. The combination of multiple sensor technologies provides more information to make applications more intelligent and more efficient (e.g. LIDAR, smartglass camera, wearable computer).

    Motor Preparation during Error-Driven Learning using Feedback Adaptation with voice and gesture control. Over time, with continual practice, actions as complicated as riding a bike, knitting, or even playing a tune on a musical instrument, can be performed almost automatically and without thought. In a manner analogous to motor memory consolidates –that is, with the formation of a memory that progresses over time from a fragile state, which is susceptible to interference or a conflicting motor task, to a stabilized state, which is resistant to such interference.

    Biomimicry Design Inspired By Nature:

    The majority of neurons in an octopus are found in the arms, which can independently taste and touch and also control basic motions without input from the brain.

    We are confident that you will be there to support us.

    Await your positive response.

    #678
    SenseHub Admin
    Keymaster

    Empathy is the connecting factor between developers.

    The source code to be used, modified and distributed commercially or non-commercially by anyone under the terms of
    GNU General Public License.

    #679
    SenseHub Admin
    Keymaster

    The project is inspired by the revolutionary aspects of Linux that are social, not technical.

    From the beginning, with the aim of being casually modified by volunteers coordinating only through the Internet.

    The quality maintained by the simple strategy of obtaining feedback and insights from users, creating a kind of gradual and participatory evolution.

    Freely redistributable, anyone can create a distribution.

    #683
    SenseHub Admin
    Keymaster
    #690
    SenseHub Admin
    Keymaster

    HAPTIX is a DARPA program designed to “develop new science and technology to achieve closed-loop control of dexterous mechatronic prostheses that will provide amputees with prosthetic limb systems that feel and function like natural limbs”. OSRF, the organization maintaining Gazebo, has been tasked with extending Gazebo to simulate prosthetic hands and test environments, and develop both graphical and programmatic interfaces to the hands.

    http://gazebosim.org/haptix

    #705
    SenseHub Admin
    Keymaster

    Google Assistant comes to gaze-powered accessible devices

    Google Assistant comes to gaze-powered accessible devices

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