Identifying convergence of sensor data from first and second sensors within an augmented reality wearable device
Examples of wearable systems and methods can use multiple inputs (e.g., gesture, head pose, eye gaze, voice, totem, and/or environmental factors (e.g., location)) to determine a command that should be executed and objects in the three-dimensional (3D) environment that should be operated on. The wearable system can detect when different inputs converge together, such as when a user seeks to select a virtual object using multiple inputs such as eye gaze, head pose, hand gesture, and totem input. Upon detecting an input convergence, the wearable system can perform a transmodal filtering scheme that leverages the converged inputs to assist in properly interpreting what command the user is providing or what object the user is targeting.
1. A method, comprising:
under control of a hardware processor of a wearable system:
accessing sensor data from a plurality of sensors of different modalities;
identifying convergence events of sensor data from a first sensor of the plurality of sensors and sensor data from a second sensor of the plurality of sensors; and
during the convergence events, selectively applying a noise filter to the sensor data from the first sensor, wherein the selectively applying the filter to the sensor data from the first sensor during the convergence events includes:
detecting a convergence of the sensor data from the first and second sensors, based on the convergence of the sensor data from the first and second sensors;
applying the filter to the sensor data from the first sensor;
detecting a divergence of the sensor data of the first sensor from the sensor data of the second sensor; and
based on the divergence, disabling application of the filter to the sensor data from the first sensor.
2. The method of claim 1 , wherein the filter comprises a low-pass filter having an adaptive cutoff frequency.
3. The method of claim 1 , further comprising:
utilizing first sensor data from the first sensor and second sensor data from the second sensor to target an object in a three-dimensional (3D) environment around the wearable system.
4. The method of claim 1 , wherein the first sensor includes an electromyogram (EMG) sensor that, in operation, senses hand motions, wherein the second sensor includes a camera-based hand gesture sensor, and wherein identifying the convergence events of sensor data includes determining, with the EMG sensor, that muscles of a user are flexed in a manner consistent with a nonverbal symbol and determining, with the camera-based hand gesture sensor, that at least a portion of a hand of the user is positioned in a manner consistent with the nonverbal symbol.
5. A wearable system comprising:
a plurality of sensors of different modalities; and
a hardware processor programmed to:
access sensor data from the plurality of sensors;
identify convergence events of sensor data from a first sensor of the plurality of sensors and sensor data from a second sensor of the plurality of sensors; and
during the convergence events, selectively apply a noise filter to the sensor data from the first sensor, wherein, to selectively apply the filter to the sensor data from the first sensor during the convergence events, the hardware processor is programmed to:
detect a convergence of the sensor data from the first and second sensors, based on the convergence of the sensor data from the first and second sensors;
apply the filter to the sensor data from the first sensor;
detect a divergence of the sensor data of the first sensor from the sensor data of the second sensor; and
based on the divergence, disable application of the filter to the sensor data from the first sensor.
6. The wearable system of claim 5 , wherein the filter comprises a low-pass filter having an adaptive cutoff frequency.
7. The wearable system of claim 5 , wherein the hardware processor is programmed to:
utilize first sensor data from the first sensor and second sensor data from the second sensor to target an object in a three-dimensional (3D) environment around the wearable system.
8. The wearable system of claim 5 ,
wherein the first sensor includes an electromyogram (EMG) sensor that, in operation, senses hand motions;
wherein the second sensor includes a camera-based hand gesture sensor; and
wherein to identify the convergence events of sensor data the hardware processor is programmed to:
determine, with the EMG sensor, that muscles of a user are flexed in a manner consistent with a nonverbal symbol; and
determine, with the camera-based hand gesture sensor, that at least a portion of a hand of the user is positioned in a manner consistent with the nonverbal symbol.