Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles. Sensors capture human eye movements, hearing, hand grip and contact area on steering wheel, the positions of accelerator and brake pedals from the wall behind them as well as from the foot. Outside event signatures corresponding to human reactions and actions are then extracted form these sensors and correlated to events, status and situations acquired using vehicle and outside environment sensors. These outside event signatures are then used to train vehicles to improve their autonomous capabilities.

Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles are disclosed. Responses of subjects to non-events and outside events when operating vehicles under various conditions are scored partly based on vehicle location and an associated map. Scoring for non-events is based on performance and mental functioning on selected categories, while scoring for outside events is based on success and failure and mental functioning when responding to these events. Subjects are ranked and expert drivers identified depending on their raw or scaled scores, or a combination score, or a threshold score. Responses of expert drivers are then used as vehicle training data to enable or improve vehicle autonomy. Response data can include data from vehicle sensors, outside environment sensors and human sensors.

Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles. Sensors capture human eye movements, hearing, hand grip and contact area on steering wheel, the positions of accelerator and brake pedals from the wall behind them as well as from the foot. Outside event signatures corresponding to human reactions and actions are then extracted form these sensors and correlated to events, status and situations acquired using vehicle and outside environment sensors. These outside event signatures are then used to train vehicles to improve their autonomous capabilities.

Systems and methods to improve performance, reliability and learning to enhance autonomy of vehicles are disclosed. Responses of subjects to non-events and outside events when operating vehicles under various conditions are scored partly based on vehicle location and an associated map. Scoring for non-events is based on performance and mental functioning on selected categories, while scoring for outside events is based on success and failure and mental functioning when responding to these events. Subjects are ranked and expert drivers identified depending on their raw or scaled scores, or a combination score, or a threshold score. Responses of expert drivers are then used as vehicle training data to enable or improve vehicle autonomy. Response data can include data from vehicle sensors, outside environment sensors and human sensors.

Disclosed are a system and method of detection of an interaction-error. The interaction-error is derived from an incorrect decision and is directed to interacting with a machine. During human-machine interaction, command related data values are obtained. Command related data values characterize any one of an interacting-command and an interacting-action. The command related data values are compared with command related reference data values, and an interaction-error is identified if a difference between the command related data values and the command related reference data values complies with a predefined criterion.

Generator systems and methods are provided for generating a neuromuscular-to-motion decoder from a healthy limb. The generator system is configured to receive neuromuscular signals from neuromuscular sensors associated to predefined muscle/nerve locations of at least one pair of agonist and antagonist muscles/nerves of the healthy limb, obtained during performance by the person of a predefined exercise (defined by predefined exercise data) with the healthy limb; to receive motion signals from motion sensors associated to predefined positions of the healthy limb, during performance by the person of the predefined exercise with the healthy limb; and to generate the neuromuscular-to-motion decoder by mapping the neuromuscular signals to the motion signals over time using a mapping method. Rehabilitation systems are also provided for rehabilitating a paretic limb by using a neuromuscular-to-motion decoder produced by a generator system.

The present invention relates to a new method for quantifying key components of manual dexterity. The present invention also provides methods for diagnosing impaired upper limb and/or hand function in patients based on how these components are affected.

A system for estimating friction, the system including a contact surface including a plurality of protrusions extending from a base surface, the contact surface further including a first contact surface region and a second contact surface region, the first contact surface region being configured to resist slip less than the second contact surface region, and a sensor arrangement configured for detecting displacement of at least three of the plurality of protrusions in three different dimensions to measure a three-dimensional force applied to the at least three plurality of protrusions at a moment of slippage of the first contact surface region.

Methods and systems for assessing a stroke rehabilitation outcome of a subject include a home-based brain-controlled interface (BCI) apparatus and a computer processor in communication with the BCI apparatus. The BCI apparatus has i) a portable brain signal acquisition headset that acquires a brain signal from a subject; ii) an orthosis device having a body part interface configured to be coupled to a body part of the subject and a plurality of sensors that generate force data and movement data; and iii) a BCI component that receives the brain signal from the brain signal acquisition headset. The BCI component is capable of controlling the orthosis device. The computer processor performs instructions to process input data to output a rehabilitation outcome prediction for the subject, where the input data includes the brain signal, the force data, the movement data, and background information about the subject.

A grip gauge is configured to measure grip force from a single hand of a user. The grip gauge comprises a shell and a force sensor housed within the shell. The force sensor is configured to measure grip forces applied to the shell. The grip gauge also includes a control unit housed within the shell and communicatively connected to the force sensor, and a wireless transmitter communicatively connected to the control unit and configured to transmit measured grip forces to one or more external devices.