# US 2026/0244272 A1 · User Interface Control of Responsive Devices

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- Number: US 2026/0244272 A1
- Document ID: US20260244272A1
- Status: Published application
- Kind: A1
- Application number: 19/646,090
- Filed: 2026-04-13
- Published: 2026-08-20
- Earliest claimed: 2018-08-05
- Assignees: Pison Technology, Inc.
- Applicants: Pison Technology, Inc.
- Inventors: Dexter W. Ang; David O. Cipoletta
- Web page: https://patentgrep.com/patent/US20260244272A1

## CPC

- A61B 5/681: {Wristwatch-type devices}
- G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection}
- A61B 5/389: Electromyography [EMG]
- G02B 27/0172: {characterised by optical features}
- G06F 1/163: {Wearable computers, e.g. on a belt}
- G06F 3/016: {Input arrangements with force or tactile feedback as computer generated output to the user}
- G06F 3/017: {Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883)}
- G06F 3/0346: with detection of the device orientation or free movement in a three-dimensional [3D] space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
- G06F 3/0484: for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F 3/167: {Audio in a user interface, e.g. using voice commands for navigating, audio feedback}
- H04W 4/80: Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/14542: {for measuring blood gases (A61B5/14551 takes precedence)}
- A61B 5/7267: {involving training the classification device}
- G02B 2027/0178: {Eyeglass type (eyeglass details G02C)}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G02B: OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

A system and method for detecting and interpreting tissue electrical signals from a user. The system comprises a user interface device configured to be worn at a wrist of a user, the user interface device comprising biopotential sensors configured to detect tissue electrical signals from the user, the biopotential sensors providing at least two channels of tissue electrical signals. The system includes a processor and is configured to receive the tissue electrical signals from the at least two channels, analyze the tissue electrical signals, and detect that a signal from at least one channel exceeds a threshold. Based on detecting that the signal exceeds the threshold, the system determines that a gesture has been performed by the user and generates an output corresponding to the determined gesture for a responsive device. The method includes receiving tissue electrical signals from the biopotential sensors, analyzing the signals, detecting threshold exceedance, determining that a gesture has been performed, and generating an output for a responsive device. The threshold is adjusted based on a calibration routine performed by the user, the calibration routine including having the user perform one or more actions multiple times.

## Claims

### Claim 1

A system, comprising:
a user interface device configured to be worn at a wrist of a user, the user interface device comprising biopotential sensors configured to detect tissue electrical signals from the user, the biopotential sensors providing at least two channels of tissue electrical signals;
a processor;
the system configured to:
receive the tissue electrical signals from the at least two channels;
analyze the tissue electrical signals from the at least two channels;
detect that a signal from at least one of the at least two channels exceeds a threshold;
based on the detection that the signal exceeds the threshold, determine that a gesture has been performed by the user; and
generate an output corresponding to the determined gesture for a responsive device;
wherein the threshold is adjusted based on a calibration routine performed by the user, and wherein the calibration routine includes having the user perform one or more actions multiple times.

### Claim 2 (depends on 1)

The system of claim 1, wherein the threshold is determined based on an average voltage amplitude of the tissue electrical signals detected during the calibration routine.

### Claim 3 (depends on 1)

The system of claim 1, wherein the calibration routine is performed prior to actual use of the user interface device.

### Claim 4 (depends on 1)

The system of claim 1, wherein the processor is further configured to personalize a classifier in real time as the user operates the user interface device.

### Claim 5 (depends on 1)

The system of claim 1, wherein the at least two channels comprise a first channel from a radial compartment of the wrist and a second channel from a flexor digitorum profundus region of the wrist.

### Claim 6 (depends on 1)

The system of claim 1, wherein the biopotential sensors are positioned to face an anterior side of the wrist.

### Claim 7 (depends on 1)

The system of claim 1, wherein the processor is configured to apply the tissue electrical signals to a classifier, and wherein the classifier is trained based on data collected during the calibration routine.

### Claim 8 (depends on 7)

The system of claim 7, wherein the classifier is customized for the user based on outputs from the calibration routine.

### Claim 9 (depends on 1)

The system of claim 1, wherein the gesture comprises at least one of an intended finger flick, a palm rotation, or a fist formation.

### Claim 10

A method, comprising:
receiving, from a user interface device configured to be worn at a wrist of a user, tissue electrical signals detected by biopotential sensors of the user interface device, the biopotential sensors providing at least two channels of tissue electrical signals;
analyzing the tissue electrical signals from the at least two channels;
detecting that a signal from at least one of the at least two channels exceeds a threshold;
based on the detection that the signal exceeds the threshold, determining that a gesture has been performed by the user;
generating an output corresponding to the determined gesture for a responsive device; and
adjusting the threshold based on a calibration routine performed by the user, wherein the calibration routine includes having the user perform one or more actions multiple times.

### Claim 11 (depends on 10)

The method of claim 10, wherein the threshold is determined based on an average voltage amplitude of the tissue electrical signals detected during the calibration routine.

### Claim 12 (depends on 10)

The method of claim 10, wherein the calibration routine is performed prior to actual use of the user interface device.

### Claim 13 (depends on 10)

The method of claim 10, further comprising personalizing a classifier in real time as the user operates the user interface device.

### Claim 14 (depends on 10)

The method of claim 10, wherein the at least two channels comprise a first channel from a radial compartment of the wrist and a second channel from a flexor digitorum profundus region of the wrist.

### Claim 15 (depends on 10)

The method of claim 10, wherein the biopotential sensors are positioned to face an anterior side of the wrist.

### Claim 16 (depends on 10)

The method of claim 10, further comprising applying the tissue electrical signals to a classifier, wherein the classifier is trained based on data collected during the calibration routine.

### Claim 17 (depends on 16)

The method of claim 16, wherein the classifier is customized for the user based on outputs from the calibration routine.

### Claim 18 (depends on 10)

The method of claim 10, wherein the gesture comprises at least one of an intended finger flick, a palm rotation, or a fist formation.