Abstract: A cue processor uses one or more sensors to obtain motion data for a user performing a physical task in an environment. A cueing law is based on a model determined from the motion data, for example a movement and skill model where the collected motion data are parsed into one or more movement units used to accomplish a range of outcomes. The cue processor generates a movement phase estimation to predict a movement phase and associated movement feature, and applies the cueing law to generate a cue signal. The cue signal is communicated to the user as a visual, audio or haptic stimulus, selected to target the feature for the user to achieve or improve a desired outcome.

Abstract: A cue processor uses one or more sensors to obtain motion data for a user performing a physical task in an environment. A cueing law is based on a model determined from the motion data, for example a movement and skill model where the collected motion data are parsed into one or more movement units used to accomplish a range of outcomes. The cue processor generates a movement phase estimation to predict a movement phase and associated movement feature, and applies the cueing law to generate a cue signal. The cue signal is communicated to the user as a visual, audio or haptic stimulus, selected to target the feature for the user to achieve or improve a desired outcome.

Abstract: A cueing device or system includes one or more sensors configured to take measurement data for one or more participants in an activity. A processor in communication with the sensors analyzes the measurement data to detect events associated with the environment and participants, extracts attributes associated with the events from the measurement data, predicts a movement outcome associated with one of the events based on the attributes, and generates one or more instructions based on the predicted movement outcome. A cue administrator generates one or more anticipatory cues based on the instructions generated by the processor. The cues are administered as cue stimuli encoding timing and magnitude information selected to reduce response time for recognition of the event.

Abstract: A cueing device or system includes one or more sensors configured to take measurement data for one or more participants in an activity. A processor in communication with the sensors analyzes the measurement data to detect events associated with the environment and participants, extracts attributes associated with the events from the measurement data, predicts a movement outcome associated with one of the events based on the attributes, and generates one or more instructions based on the predicted movement outcome. A cue administrator generates one or more anticipatory cues based on the instructions generated by the processor. The cues are administered as cue stimuli encoding timing and magnitude information selected to reduce response time for recognition of the event.

Abstract: A system adapted to augment movement behavior of participants in an open motor task or activity includes one or more movement sensors configured to generate output characterizing movements of participants, including relevant interactions with elements and features of the environment and task or activity objects within the environment. A processor is configured to extract and segment a sequence of movement behavior elements from the output, register the movement elements with respect to operating environment, including the task or activity objects, recognize activity state, and determine cues to enhance performance and/or learning. Augmentations include verbal, visual, or haptic or audible signal-based cues that are designed to target critical aspects of movement skills in open motor tasks, including planning sequence of movements toward task goals; coordinating and executing movement elements in the sequence in relationship to relevant task activity events and elements.

Abstract: A cue processor uses one or more sensors to obtain motion data for a user performing a physical task in an environment. A cueing law is based on a model determined from the motion data, for example a movement and skill model where the collected motion data are parsed into one or more movement units used to accomplish a range of outcomes. The cue processor generates a movement phase estimation to predict a movement phase and associated movement feature, and applies the cueing law to generate a cue signal. The cue signal is communicated to the user as a visual, audio or haptic stimulus, selected to target the feature for the user to achieve or improve a desired outcome.

Abstract: A cue processor uses one or more sensors to obtain motion data for a user performing a physical task in an environment. A cueing law is based on a model determined from the motion data, for example a movement and skill model where the collected motion data are parsed into one or more movement units used to accomplish a range of outcomes. The cue processor generates a movement phase estimation to predict a movement phase and associated movement feature, and applies the cueing law to generate a cue signal. The cue signal is communicated to the user as a visual, audio or haptic stimulus, selected to target the feature for the user to achieve or improve a desired outcome.

Abstract: A cue processor uses one or more sensors to obtain motion data for a user performing a physical task in an environment. A cueing law is based on a model determined from the motion data, for example a movement and skill model where the collected motion data are parsed into one or more movement units used to accomplish a range of outcomes. The cue processor generates a movement phase estimation to predict a movement phase and associated movement feature, and applies the cueing law to generate a cue signal. The cue signal is communicated to the user as a visual, audio or haptic stimulus, selected to target the feature for the user to achieve or improve a desired outcome.

Abstract: A cueing device or system includes one or more sensors configured to take measurement data for one or more participants in an activity. A processor in communication with the sensors analyzes the measurement data to detect events associated with the environment and participants, extracts attributes associated with the events from the measurement data, predicts a movement outcome associated with one of the events based on the attributes, and generates one or more instructions based on the predicted movement outcome. A cue administrator generates one or more anticipatory cues based on the instructions generated by the processor. The cues are administered as cue stimuli encoding timing and magnitude information selected to reduce response time for recognition of the event.

Abstract: A codification and cueing system for adversarial or cooperative interactions between actors engaged in sports and other activities uses measurements from motion sensors to identify events in the activity and participant's behavior to provide real-time cues to help the actors perform as well as train. The codification describes the interactions based on behavioral events and movements. The cueing method can be implemented using different subsets of such events. Following detection of these events, a signal is transmitted to generate auditory, visual and/or haptic stimuli. These stimuli encode information and hence the cues help the participants select, prepare for and execute relevant aspects of the performance.

Abstract: A data-driven movement skill training system is disclosed. The system uses movement skill assessment and diagnostics at distinct levels of the human movement system hierarchy to specify training goals for a user. The system may provide various augmentations that are synthesized to help the user pursue the training goals. The system may include features to track and/or manage training or learning processes.

Abstract: The invention consists of a codification and cueing system for the optimization of adversarial or cooperative interactions between human actors engaged in sports and other activities. The system uses measurements from motion sensors to identify key events in the activity and participant's behavior to provide real-time cues to help the actors perform as well as train. The system is illustrated for a tennis application. The codification describes the players' interactions based on court events, such as the ball's trajectory relative to the court, and player behavioral events, such as the stroke phases and court movements. The cueing method can be implemented using different subsets of such events. In its simplest implementation, timing cues are obtained from the stroke backswing initiation, forward swing initiation and impact of the ball on the racket. Following the detection of these events, a signal is transmitted to the adversary where it is used to generate auditory, visual and/or haptic stimuli.

Abstract: A movement augmentation system that provides various forms of feedback, including real-time and post-performance for training and rehabilitation is disclosed. The systems, devices and methods provide augmentation to human movement behavior in order to accelerate acquisition of complex movement skills by a user and to mitigate injuries from poor movement technique (e.g., joint damage or muscle fatigue). An environment is provided that enables systematic and comprehensive capabilities through integration of its components.