A method for detecting a user's intent in an adaptive lower limb device includes providing the adaptive lower limb device. The lower limb device includes a device control unit. The device control unit includes activity controllers and at least one accelerometer. The acceleration features are measured via the at least one accelerometer. The measured acceleration features are determined whether they correspond to a tapping movement initiated by a user with an intent to switch from a first one of the plurality of activity controllers to a second one of the plurality of activity controllers. If the measured accelerating features correspond to the tapping movement, the control unit of the adaptive lower limb device is switched from the first one of the activity controllers to the second one of the activity controllers.

Routines and methods disclosed herein can increase a power efficiency of a prosthetic hand without drastically reducing the speed at which it operates. A prosthesis can implement an acceleration profile, which can reduce an energy consumption of a motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor transitions from an idle state to a non-idle state. A prosthesis can implement a deceleration profile, which can reduce the energy consumption of the motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor transitions from a non-idle state to an idle state.

Routines and methods disclosed herein can increase a power efficiency of a prosthetic hand without drastically reducing the speed at which it operates. A prosthesis can implement an acceleration profile, which can reduce an energy consumption of a motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor transitions from an idle state to a non-idle state. A prosthesis can implement a deceleration profile, which can reduce the energy consumption of the motor, or an amount of electrical and/or mechanical noise produced by a motor, as the motor transitions from a non-idle state to an idle state.

A method of continuous decoding of motion for a direct neural interface. The method of decoding estimates a motion variable from an observation variable obtained by a time-frequency transformation of the neural signals. The observation variable is modelled using a HMM model whose hidden states include at least an active state and an idle state. The motion variable is estimated using a Markov mixture of experts where each expert is associated with a state of the model. For a sequence of observation vectors, the probability that the model is in a given state is estimated, and from this a weighting coefficient is deduced for the prediction generated by the expert associated with this state. The motion variable is then estimated by combination of the estimates of the different experts with these weighting coefficients.

A system and method for controlling a device, such as a virtual reality (VR) and/or a prosthetic limb are provided. A biomimetic controller of the system comprises a signal processor and a musculoskeletal model. The signal processor processes M biological signals received from a residual limb to transform the M biological signals into N activation signals, where M and N are integers and M is less than N. The musculoskeletal model transforms the N activation signals into intended motion signals. A prosthesis controller transforms the intended motion signals into three or more control signals that are outputted from an output port of the prosthesis controller. A controlled device receives the control signals and performs one or more tasks in accordance with the control signals.

A system and method for controlling a device, such as a virtual reality (VR) and/or a prosthetic limb are provided. A biomimetic controller of the system comprises a signal processor and a musculoskeletal model. The signal processor processes M biological signals received from a residual limb to transform the M biological signals into N activation signals, where M and N are integers and M is less than N. The musculoskeletal model transforms the N activation signals into intended motion signals. A prosthesis controller transforms the intended motion signals into three or more control signals that are outputted from an output port of the prosthesis controller. A controlled device receives the control signals and performs one or more tasks in accordance with the control signals.

A prosthesis and control approach using electromyography (EMG) data and motion data. EMG sensors and a motion sensor provide inputs to generate control signals. The EMG sensor detects EMG signals from the user's body. The motion sensor may be one or more inertial measurement sensors (IMS) and/or a magnetic field sensor. The EMG and motion data is analyzed according to various techniques to provide control of one or more actuatable prosthetic joints of an upper limb prosthesis, such as a prosthetic elbow, wrist, hand, and/or digits.

A prosthesis and control approach using electromyography (EMG) data and motion data. EMG sensors and a motion sensor provide inputs to generate control signals. The EMG sensor detects EMG signals from the user's body. The motion sensor may be one or more inertial measurement sensors (IMS) and/or a magnetic field sensor. The EMG and motion data is analyzed according to various techniques to provide control of one or more actuatable prosthetic joints of an upper limb prosthesis, such as a prosthetic elbow, wrist, hand, and/or digits.

A method for configuring a myoelectrically controlled prosthetic system with a prosthesis socket and several lead electrodes for recording electric muscle activities, featuring the steps: placement of a surface electrode arrangement comprising several surface electrodes around the circumference of a residual limb, recording of electric muscle activity in muscles of the residual limb as electromyograhic signals, the activity being recorded by the surface electrodes, evaluation of the myoelectric signals with regards to the distinctness of the signals, selection of the control procedure that is to be used to control the prosthesis system, based on the evaluation of the distinctness of the signals, and fixing of the lead electrodes to the prosthesis socket.

A method for configuring a myoelectrically controlled prosthetic system with a prosthesis socket and several lead electrodes for recording electric muscle activities, featuring the steps: placement of a surface electrode arrangement comprising several surface electrodes around the circumference of a residual limb, recording of electric muscle activity in muscles of the residual limb as electromyograhic signals, the activity being recorded by the surface electrodes, evaluation of the myoelectric signals with regards to the distinctness of the signals, selection of the control procedure that is to be used to control the prosthesis system, based on the evaluation of the distinctness of the signals, and fixing of the lead electrodes to the prosthesis socket.