An apparatus for efficient targeting or removal of weeds or other plants. The apparatus may include a vehicle having a frame, a motor and a plurality of ground engaging members adapted to propel the vehicle over a surface. It may also include a robotic arm comprising a distal portion and a proximal portion coupled to the frame, and an implement, such as a tool or hoe connected to the distal portion of the robotic arm. The implement can be raised and lowered, and also moved relative to the surface by the robotic arm by pivoting or rotating the robotic arm at or near the proximal portion.

A system, method, and apparatus for a robot system that manipulates the surface of an object effect programmed manipulation goals such as reaching specific locations on the surface of the object, displacing the surface of the object, applying a predetermined force and torque to the surface of the object, dynamically changing the contact point between the robot and the object, and applying force to structures below the surface of the object. The system and method determine the state of the object through a sensing method that includes, without limitation: torque and force measurement, visible light sensors, range and depth sensors, ultrasound sensors, thermographic sensors, and worktable force measurement.

Apparatus and associated methods relate to a connected pill dispenser configured to treat a patient's illness with a robotic arm adapted to deliver a prescribed medication dose to a patient in their home, analyze the patient's response to the dose based on various sensor inputs, automatically determine whether the patient took the medicine and send the provider the result of providing the medication dose to the patient. In an illustrative example, the patient may be a chronic, acute, or terminal illness patient. The medication dose may be, for example, a medication prescribed by a doctor to be taken in a specific amount at specific times. In some embodiments, the connected pill dispenser may automatically notify the patient when a medication dose is due. Various embodiments may determine if the patient consumed a medication dose based on machine vision, audio, or other sensor data, permitting caregiver notification of patient medication adherence, medication compliance, or non-adherence and sensor inputs as data on potential effectiveness and/or side effects.

Provided are display screens for electronic devices and methods for displaying content on electronic devices, that are particularly suitable for users who are suffering from or recovering from a concussion or mild traumatic brain injury (MTBI), migraine, or computer vision syndrome. Embodiments comprise a component that modifies at least one parameter of content that is displayed on the display screen, wherein the modified displayed content reduces, minimizes, or eliminates negative side effects of users. Embodiments may include computer executable programmed instructions stored on a non-transitory computer readable storage medium that direct a processor to modify at least one parameter of content that is displayed on a display screen of an electronic device, and/or to implement a simplified graphical user interface. One embodiment is implemented using an e-paper display screen.

A data processing method for a care-giving robot and an apparatus comprises receiving data from a target object comprising a capability parameter of the target object, generating a growing model capability parameter matrix of the target object that includes the capability parameter, a capability parameter adjustment value, and a comprehensive capability parameter that is calculated based on the capability parameter; adjusting the capability parameter adjustment value in the growing model capability parameter matrix, to determine an adjusted capability parameter adjustment value; determining whether the adjusted capability parameter adjustment value exceeds a preset threshold; and sending the adjusted capability parameter adjustment value to a machine learning engine when the adjusted capability parameter adjustment value is within a range of the preset threshold.

Disclosed is a rehabilitation robot training system for monitoring and suppressing the compensatory movement of a hemiplegic upper limb. The system includes an upper computer control center, an interaction display screen, a force feedback glove, a position tracker, upper limb rehabilitation robot tail end connectors, an upper limb rehabilitation robot, a base and a pressure cushion. One upper limb rehabilitation robot tail end connector is mounted on a tail end of each of two robotic arms of the upper limb rehabilitation robot, and the upper limb rehabilitation robot tail end connectors are respectively worn on an upper arm and a forearm of a patient to drive an arm to move; the upper computer control center stores and processes data, collected by the position tracker, the force feedback glove and the pressure cushion in real time, of the patient, and monitors and analyses whether the patient does a compensatory gesture.

A method for controlling a limb motion intention recognizing and rehabilitation training robot based on force sense information includes: acquiring data of a three-dimensional force and a three-dimensional moment by a six-dimensional force sensor held in a hand; calculating forces and moments produced by a palm, a forearm and an upper arm of a human body according to a constructed human arm model to achieve recognition of limb motion intention; fixing the six-dimensional force sensor on a rocker at an end of the three-degree-of-freedom upper limb rehabilitation training robot, acquiring motion intention of an arm of the human body according to the motion intention recognition method, and controlling the rehabilitation training robot to achieve auxiliary active training under a weak active force.

A method for controlling a limb motion intention recognizing and rehabilitation training robot based on force sense information includes: acquiring data of a three-dimensional force and a three-dimensional moment by a six-dimensional force sensor held in a hand; calculating forces and moments produced by a palm, a forearm and an upper arm of a human body according to a constructed human arm model to achieve recognition of limb motion intention; fixing the six-dimensional force sensor on a rocker at an end of the three-degree-of-freedom upper limb rehabilitation training robot, acquiring motion intention of an arm of the human body according to the motion intention recognition method, and controlling the rehabilitation training robot to achieve auxiliary active training under a weak active force.

Apparatus and associated methods relate to a connected pill dispenser configured to treat a patient's illness with a robotic arm adapted to deliver a prescribed medication dose to a patient in their home, analyze the patient's response to the dose based on various sensor inputs, automatically determine whether the patient took the medicine and send the provider the result of providing the medication dose to the patient. In an illustrative example, the patient may be a chronic, acute, or terminal illness patient. The medication dose may be, for example, a medication prescribed by a doctor to be taken in a specific amount at specific times. In some embodiments, the connected pill dispenser may automatically notify the patient when a medication dose is due. Various embodiments may determine if the patient consumed a medication dose based on machine vision, audio, or other sensor data, permitting caregiver notification of patient medication adherence, medication compliance, or non-adherence and sensor inputs as data on potential effectiveness and/or side effects.

A rehabilitation system for rehabilitation of a subject including at least one end-effector for interacting with the subject, the end-effector having at least two degrees of freedom of motion, at least one actuator for actuating the at least one end-effector, at least one sensor for measuring at least the position and the speed of the at least one end-effector; at least one sensor for measuring the interaction force between the subject and the end-effector; a memory including at least two initial coefficients and a session including at least one exercise including at least one reference trajectory to be carried out by the subject through actuation of the end effector; and an actuator controlling unit. The memory delivers the initial coefficients and the session, the sensors deliver measurement signals to the controlling unit, and the controlling unit provides a force-controlled feedback based on the initial coefficients.