# US 10,869,804 B2 · Method and system for using haptic device and brain-computer interface for rehabilitation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,869,804 B2 - Document ID: US10869804B2 - Status: Granted - Kind: B2 - Application number: 15/328,010 - Filed: 2015-07-23 - Published: 2017-06-29 - Granted: 2020-12-22 - Earliest claimed: 2014-07-23 - Assignees: Agency for Science, Technology and Research - Applicants: Agency for Science, Technology and Research - Inventors: Kai Keng Ang; Cuntai Guan; Kok Soon Phua; Longjiang Zhou; Chuan Chu Wang - Web page: https://patentgrep.com/patent/US10869804B2 ## CPC - A61H 1/0274: {for the upper limbs} - A61B 5/1121: {Determining geometric values, e.g. centre of rotation or angular range of movement} - A61B 5/1126: {using a particular sensing technique} - A61B 5/225: {of the fingers, e.g. by monitoring hand-grip force} - A61B 5/372: Analysis of electroencephalograms - A61B 5/375: using biofeedback - A61H 1/02: Stretching or bending {or torsioning} apparatus for exercising - A61H 1/0285: {Hand} - A61H 1/0288: {Fingers} - G06F 3/014: {Hand-worn input/output arrangements, e.g. data gloves} - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - G06F 3/016: {Input arrangements with force or tactile feedback as computer generated output to the user} - G09B 5/06: with both visual and audible presentation of the material to be studied - A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick} - A61B 2560/0223: of calibration, e.g. protocols for calibrating sensors - A61H 2001/0203: {Rotation of a body part around its longitudinal axis} - A61H 2201/1635: Hand or arm, e.g. handle - A61H 2201/1671: rotational - A61H 2201/5043: Displays - A61H 2201/5048: Audio interfaces, e.g. voice or music controlled - A61H 2201/5061: Force sensors - A61H 2201/5064: Position sensors - A61H 2230/105: used as a control parameter for the apparatus - A61H: PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY (electrotherapy, magnetotherapy, radiation therapy or ultrasound therapy A61N) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) ## Abstract A method for calibrating and executing a rehabilitation exercise for a stroke-affected limb of a stroke patient is disclosed, the method comprising the steps of providing a haptic device for an able limb of the stroke patient to manipulate to perform a calibration action to result in a first position of the haptic device, and providing the haptic device for the stroke-affected limb to manipulate to perform the calibration action to result in a second position of the haptic device. The method further comprises the steps of moving the haptic device coupled with the stroke-affected limb from the second position towards the first position until a predetermined counterforce is detected, indicating an extreme position for the stroke-affected limb using the haptic device, and calibrating the haptic device with the extreme position such that during the rehabilitation exercise, the haptic device is prevented from moving beyond the extreme position. ## Claims ### Claim 1 A method for calibrating and executing a rehabilitation exercise for a stroke-affected limb of a stroke patient, the stroke patient having an able limb, the method comprising: providing a haptic device for the able limb to manipulate; providing with a screen, automated visual/audio instructions to guide the stroke patient in using the able limb to manipulate the haptic device to perform at least one calibration action; determining a first position of the haptic device, the first position resultant from the manipulation of the haptic device by the able limb in completing the at least one calibration action; providing the haptic device for the stroke-affected limb to manipulate; providing with the screen, automated visual/audio instructions to guide the stroke patient in using the stroke-affected limb to manipulate the haptic device to perform the at least one calibration action; determining a second position of the haptic device, the second position resultant from the manipulation of the haptic device by the stroke-affected limb in completing the at least one calibration action; moving the haptic device coupled with the stroke-affected limb from the second position towards the first position until a predetermined counterforce emanating from the stroke-affected limb is detected, indicating an extreme position for the stroke-affected limb using the haptic device; and calibrating the haptic device with the extreme position such that during the rehabilitation exercise for the stroke-affected limb, the haptic device is prevented from moving beyond the extreme position, wherein a force applied by the haptic device is a function of a motor imagery score of the stroke patient, a limb strength of the stroke-affected limb and a maximum limb strength of the able limb, and wherein the force applied by the haptic device is represented by an equation α = - tanh ⁡ ( 5 h max ⁢ ( h - h max 2 ) ) ⁢ tanh ⁡ ( 5 ⁢ ⁢ m m max ) , wherein α is the force applied by the haptic device, h is the limb strength of the stroke-affected limb, hmax is the maximum limb strength of the able limb, m is the motor imagery score of the stroke patient, and mmax is a maximum motor imagery score. ### Claim 2 (depends on 1) The method of claim 1 wherein the rehabilitation exercise has the same sequence of movements as the at least one calibration action. ### Claim 3 (depends on 1) The method of claim 1 wherein the at least one calibration action is any one of, or any combination of the following actions: finger flexion, finger extension, forearm pronation and forearm supination. ### Claim 4 (depends on 1) The method of claim 1 further comprising: providing with the screen, automated visual/audio instructions to guide the stroke patient in using the able limb to apply maximum strength when the haptic device is stationary; and determining the maximum limb strength of the able limb by measuring the maximum strength applied by the able limb. ### Claim 5 (depends on 4) The method of claim 4 wherein the predetermined counterforce is greater than one quarter of the maximum limb strength of the able limb. ### Claim 6 (depends on 4) The method of claim 4 further comprising the operation of determining a limb strength of the stroke-affected limb by measuring the driving motor current necessary to maintain the same servo motor position during the rehabilitation exercise for the stroke-affected limb. ### Claim 7 (depends on 6) The method of claim 6 further comprising the operation of using a brain computer interface (BCI) system to obtain electroencephalogram (EEG) data from the brain of the stroke patient, and determining from the EEG data, a motor imagery score of the stroke patient. ### Claim 8 (depends on 7) The method of claim 7 further comprising the operation of executing the rehabilitation exercise for the stroke-affected limb by applying the force with the haptic device. ### Claim 9 (depends on 8) The method of claim 8 wherein the force applied by the haptic device is an assistive force or a resistive force depending on the limb strength of the stroke-affected limb. ### Claim 10 (depends on 7) The method of claim 7 further comprising the operation of using the EEG data to compute a Temporal Spectral-dependent Brain Index (TSBI), and then plotting the TSBI against Fugi-Meyer Score Improvement in a graph so as to predict the progress of stroke rehabilitation. ### Claim 11 (depends on 10) The method of claim 10 wherein the TSBI is calculated using the equation TSBI ⁡ ( t ) = 1 n k ⁢ ∑ n = k 1 k 2 ⁢  R n * ⁡ ( t ) - L n * ⁡ ( t ) R n * ⁡ ( t ) + L n * ⁡ ( t )  . ### Claim 12 A system for calibrating and executing a rehabilitation exercise for a stroke-affected limb of a stroke patient, the stroke patient having an able limb, the system comprising a haptic device, a screen, at least one sensor and a processor; wherein the haptic device is capable of: being manipulated by the able limb, in response to automated visual/audio instructions provided by the screen to guide the stroke patient in using the able limb to manipulate the haptic device to perform at least one calibration action, wherein the at least one sensor is configured to determine a first position of the haptic device, the first position resultant from the manipulation of the haptic device by the able limb in completing the at least one calibration action; being manipulated by the stroke-affected limb, in response to automated visual/audio instructions provided by the screen to guide the stroke patient in using the stroke-affected limb to manipulate the haptic device to perform the at least one calibration action; wherein the at least one sensor is configured to determine a second position of the haptic device, the second position resultant from the manipulation of the haptic device by the stroke-affected limb in completing the at least one calibration action; and moving, coupled with the stroke-affected limb, from the second position towards the first position until a predetermined counterforce emanating from the stroke-affected limb is detected by the least one sensor, indicating an extreme position for the stroke-affected limb using the haptic device; wherein the processor is configured to calibrate the haptic device with the extreme position such that during the rehabilitation exercise for the stroke-affected limb, the haptic device is prevented from moving beyond the extreme position, wherein a force applied by the haptic device is a function of a motor imagery score of the stroke patient, a limb strength of the stroke-affected limb and a maximum limb strength of the able limb, and wherein the force applied by the haptic device is represented by an equation α = - tanh ⁡ ( 5 h max ⁢ ( h - h max 2 ) ) ⁢ tanh ⁡ ( 5 ⁢ ⁢ m m max ) , wherein α is the force applied by the haptic device, h is the limb strength of the stroke-affected limb, hmax is the maximum limb strength of the able limb, m is the motor imagery score of the stroke patient, and mmax is a maximum motor imagery score. ### Claim 13 (depends on 12) The system of claim 12 wherein the haptic device is a glove that fits onto a hand of the stroke patient. ### Claim 14 (depends on 12) The system of claim 12 wherein the at least one sensor comprises position encoders for providing position and orientation data of the haptic device. ### Claim 15 (depends on 12) The system of claim 12 further comprising a brain computer interface (BCI) system, the BCI system configured to obtain electroencephalogram (EEG) data from a brain of the stroke patient, and determine from the EEG data the motor imagery score of the stroke patient. ### Claim 16 (depends on 15) The system of claim 15 wherein the haptic device is configured to execute the rehabilitation exercise by applying the force during the rehabilitation exercise for the stroke-affected limb, wherein the force is a function of the motor imagery score of the stroke patient, the limb strength of the stroke-affected limb and the maximum limb strength of the able limb. ### Claim 17 (depends on 16) The system of claim 16 wherein the force applied by the haptic device is an assistive force or a resistive force depending on the limb strength of the stroke-affected limb. ### Claim 18 (depends on 15) The system of claim 15 wherein the EEG data is used to compute a Temporal Spectral-dependent Brain Index (TSBI), and the TSBI is then plotted against Fugi-Meyer Score Improvement in a graph so as to predict progress of stroke rehabilitation.