IP Library › Granted Patent US 12,611,592
Granted Patent B2
US 12,611,592 · App. 18/370,431 · Granted Apr 28, 2026

Hand training method with force feedback and hand training device

Inventors: Fong-Chin Su (Tainan, TW); Li-Chieh Kuo (Tainan, TW); Hsiao-Feng Chieh (Tainan, TW); Chien-Ju Lin (Tainan, TW); Hsiu-Yun Hsu (Tainan, TW)
Assignee: National Cheng Kung University
A63F13/24A63B21/4019A63B21/4035A63B23/16A63F13/218A63F13/22A63F13/285G06F3/016
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,611,592
App. No.
18/370,431
Granted
Apr 28, 2026
Kind
B2
Abstract

The present invention discloses a force feedback hand training method including: providing a training game content and adjusting the training game content based on predetermined parameters; displaying the training game content on a display; determining whether an input button position of at least one input signal from a hand training device matches a predetermined input button position; and storing a determination result in a storage module.

Claims (46)

1 . A force feedback hand training method, comprising:

providing a training game content and adjusting the training game content based on predetermined parameters;

displaying the training game content on a display;

determining whether an input button position of at least one input signal from a hand training device matches a predetermined input button position; and

storing a determination result in a storage module, wherein:

the hand training device, comprising:

a bottom surface;

a top surface connecting to the bottom surface;

a plurality of buttons, wherein the plurality of buttons are configured on one side of a connecting portion of the top surface and the bottom surface, and are spaced apart from each other, wherein a moving direction of the button is parallel to the bottom surface;

a plurality of pressure sensors, wherein each pressure sensor respectively corresponds to the button;

a signal transmitter configured to transmit signals generated by the pressure sensors;

a fixing portion having a ring structure with a hole, wherein the fixing portion is configured on a side of the button opposite to the pressure sensor;

an angle sensor, wherein the angle sensor is configured on a side of the button same to the pressure sensor; and

a pivot, wherein the pivot connects the fixing portion and the angle sensor, when the fixing portion moves along a direction parallel to the bottom surface, the pivot rotates along a direction normal to the bottom surface.

2 . The force feedback hand training method of claim 1 , further comprising:

determining whether a value of the at least one input signal falls within a predetermined parameter threshold range.

3 . The force feedback hand training method of claim 1 , further comprising:

determining whether the determination result matches a predetermined error pattern; and

adjusting the parameters according to a parameter setting corresponding to the error pattern.

4 . A hand training device, comprising:

a bottom surface;

a top surface connecting to the bottom surface;

a plurality of buttons, wherein the plurality of buttons are configured on one side of a connecting portion of the top surface and the bottom surface, and are spaced apart from each other, wherein a moving direction of the button is parallel to the bottom surface;

a plurality of pressure sensors, wherein each pressure sensor respectively corresponds to the button;

a signal transmitter configured to transmit signals generated by the pressure sensors;

a fixing portion having a ring structure with a hole, wherein the fixing portion is configured on a side of the button opposite to the pressure sensor;

an angle sensor, wherein the angle sensor is configured on a side of the button same to the pressure sensor; and

a pivot, wherein the pivot connects the fixing portion and the angle sensor, when the fixing portion moves along a direction parallel to the bottom surface, the pivot rotates along a direction normal to the bottom surface.

5 . The hand training device of claim 4 , wherein the top surface protrudes in an arc shape along a normal direction of the bottom surface.

6 . The hand training device of claim 4 , wherein the hand training device further comprises a plurality of guiding portions configured on the connecting portion of the bottom surface and the top surface, and the guiding portions are spaced apart from the buttons.

7 . The hand training device of claim 6 , wherein the guiding portions are arc-shaped and extend in a direction opposite to the bottom surface.

8 . The hand training device of claim 7 , wherein a distance between a top of the guiding portion and the bottom surface is greater than a distance between a top of the button and the bottom surface.

9 . The hand training device of claim 7 , wherein the two guiding portions connect to each other, and a connection part of the guiding portions is arc-shaped, with a normal vector direction opposite to a normal vector direction of the guiding portions.

10 . The hand training device of claim 4 , wherein the button further comprises a button extension portion connecting the pressure sensor, when the button moves along a direction parallel to the bottom surface, the button extension portion triggers the pressure sensor.

11 . The hand training device of claim 4 , wherein the hand training device further comprises a displacement sensor configured to sense displacement of the hand training device.

12 . The hand training device of claim 4 , wherein the hand training device further comprises a vibrator.

13 . A hand training system, comprising:

the hand training device of claim 4 , configured to collect input signals;

a human-machine interface connecting to the hand training device, wherein the human-machine interface comprises:

a gaming module including a display, a training content processor and a memory wherein the training content processor is configured to read a training game content stored in the memory, adjust the training game content according to predetermined parameters and displays the training game content on the display;

a determination module connecting to the gaming module, wherein the determination module is configured to determine whether an input button position of the input signal matches a predetermined position and determine whether a value of the input signal falls within a parameter threshold range; and

a storage module configured to store a determination result.

14 . The hand training system of claim 13 , wherein the training game content comprises at least one control object and at least one target object, and a distance between the control object and the target object is adjusted based on the predetermined parameters.

15 . The hand training system of claim 13 , wherein the hand training system further comprises an augmented reality module, and the augmented reality module comprises a camera configured to capture a user's hand image and an image processor configured to combine the user's hand image with the training game content in an augmented reality manner.

16 . The hand training system of claim 13 , wherein the hand training device further comprises a displacement sensor configured to sense displacement of the hand training device.

17 . The hand training system of claim 13 , wherein the hand training device further comprises a vibrator.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 20, 2023
From: SU, FONG-CHIN; KUO, LI-CHIEH; CHIEH, HSIAO-FENG; LIN, CHIEN-JU; HSU, HSIU-YUN
To: NATIONAL CHENG KUNG UNIVERSITY
Reel/Frame 064963/0076 →
Continuity (2)
Provisional Application 63376441 · Sep 21, 2022
Related Publication 20240091635A1 · Mar 21, 2024
References Cited (36)
US 4791408A · Heusinkveld · 1988 [cited by examiner]
US 5841425A · Zenz, Sr. · 1998 [cited by examiner]
US 6545667B1 · Lilenfield · 2003 [cited by examiner]
US 6765502B2 · Boldy · 2004 [cited by examiner]
US 7006075B1 · Olson · 2006 [cited by examiner]
US 7113171B2 · Vayda · 2006 [cited by examiner]
US 10475352B2 · Cramer · 2019 [cited by examiner]
US 10599233B1 · Amalou · 2020 [cited by examiner]
US D922384S · Amalou · 2021 [cited by examiner]
US 20010008848A1 · Armstrong · 2001 [cited by examiner]
US 20020006827A1 · Ogata · 2002 [cited by examiner]
US 20030083131A1 · Armstrong · 2003 [cited by examiner]
US 20050083297A1 · Duncan · 2005 [cited by examiner]
US 20050092083A1 · Baratz · 2005 [cited by examiner]
US 20060274044A1 · Gikandi · 2006 [cited by examiner]
US 20090318269A1 · D'Addario · 2009 [cited by examiner]
US 20130143718A1 · Pani · 2013 [cited by examiner]
US 20130196825A1 · Silagy · 2013 [cited by examiner]
US 20130288777A1 · Short et al. · 2013 [cited by applicant]
US 20150190675A1 · Silagy · 2015 [cited by examiner]
US 20160038075A1 · Burdea et al. · 2016 [cited by applicant]
US 20170069223A1 · Cramer · 2017 [cited by examiner]
US 20210050163A1 · Guerrero, Jr. · 2021 [cited by examiner]
US 20210110591A1 · Yokokawa · 2021 [cited by applicant]
US 20210126634A1 · Lee · 2021 [cited by examiner]
US 20210162259A1 · D'Addario · 2021 [cited by examiner]
US 20210357042A1 · Borodin · 2021 [cited by examiner]
US 20230041782A1 · Nishioka · 2023 [cited by examiner]
US 20230094151A1 · Seymour · 2023 [cited by examiner]
US 20230113699A1 · Feuerstein · 2023 [cited by examiner]
US 20240082698A1 · Duncan · 2024 [cited by examiner]
US 20240169853A1 · Nath · 2024 [cited by examiner]
US 20240207668A1 · Neely · 2024 [cited by examiner]
US 20240299795A1 · Bluman · 2024 [cited by examiner]
Cong Peng et al., A Visuo-Haptic Attention Training Game With Dynamic Adjustment of Difficulty, Special Section On Smart Health Sensing and Computational Intelligence: From Big Data To Big Impacts, 2019 IEEE Translation… [cited by applicant]
Jia Yu et al., Mobile VR Game Design for Stroke Rehabilitation, Springer International Publishing AG, part of Springer Nature 2018, P.-L. P. Rau (Ed.): CCD 2018, LNCS 10912, pp. 95-116, 2018. [cited by applicant]