Powered, multi-functional limb movement auxiliary device, particularly prosthesis and movement-assisting orthosis, with combined estimation regimes

• US 10,448,857 B2
• Filed: 06/03/2016
• Issued: 10/22/2019
• Est. Priority Date: 06/19/2015
• Status: Active Grant

• Current Assignee: Georg-August-Universitat Gottingen Stiftung Offenlichen Rechts (Georg-August-Universität Göttingen), Otto Bock HealthCare GmbH (OTTO BOCK Holding GmbH & Company KG)
• Original Assignee: Georg-August-Universitat Gottingen Stiftung Offenlichen Rechts (Georg-August-Universität Göttingen)
• Inventors: Graimann, Bernhard, Amsuss, Sebastian, Farina, Dario
• Primary Examiner(s): Sweet, Thomas
• Assistant Examiner(s): Bahena, Christie L
• Application Number:
  US15/737,635
• Publication Number:
  US 20180168477A1
• Time in Patent Office: 1,236 Days
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61F 2/54 Artificial arms or hands or parts thereof
• CPC Class Codes: A61F 2/583 Hands; Wrist joints
• CPC Class Codes: A61F 2/60 Artificial legs or feet or parts thereof
• CPC Class Codes: A61F 2/72 Bioelectric control, e.g. myoelectric
• CPC Class Codes: A61F 2/80 Sockets, e.g. of suction type
• CPC Class Codes: A61F 2002/701 operated by electrically controlled means, e.g. solenoids or torque motors
• CPC Class Codes: A61F 2002/704 computer-controlled, e.g. robotic control
• CPC Class Codes: A61F 2002/7615 Measuring means for implanting artificial joints A61F2/4657
• CPC Class Codes: A61F 2002/7635 for measuring force, pressure or mechanical tension
• CPC Class Codes: A61F 5/01 Orthopaedic devices, e.g. splints, casts or braces

Abstract

A method of operating a multi-functional limb movement auxiliary device comprising a plurality of actuators configured to move, upon activation, limb movement auxiliary device members in at least two independent degrees of freedom, a bio-signal sensing unit that is configured to acquire bio-signals indicative of motor activity, a control unit configured to receive and evaluate the acquired bio-signals, the method comprising steps of

• • acquiring bio-signals from the user of the limb movement auxiliary device,
  • applying a novelty detection method (ND) for comparing digitally converted, acquired bio-signals with calibration bio-signals, and, based on a result of the comparison, for assigning a similarity measure to the acquired, digitally converted bio-signals,
  • applying at least one sequential estimator (SEQ-E) to the acquired, digitally converted bio-signals, if the assigned similarity measure is equal to or larger than a pre-determined threshold value,
  • applying at least one simultaneous estimator (SIM-E) to the acquired, digitally converted bio-signals, if the assigned similarity measure is less than the pre-determined threshold value, and
  • activating at least one actuator out of the plurality of actuators, based on a result of applying one of the estimators (SIM-E, SEQ-E) to the acquired, digitally converted bio-signals; and
    a multi-functional limb movement auxiliary device including a control unit configured for carrying out such method.

Claims

1. A multi-functional limb movement auxiliary device, comprising

a base member,

a socket member that is configured to at least partially receive a portion or remaining portion of a limb,

a plurality of limb movement auxiliary device members, each limb movement auxiliary device member being connected in an articulated way to at least one of the base member or a different limb movement auxiliary device member,

a plurality of actuators that are connectable to an electric power source, wherein each actuator is configured to move, upon activation, at least one limb movement auxiliary device member out of the plurality of limb movement auxiliary device members in an articulated way, and wherein the plurality of actuators is configured to enable articulated movements of limb movement auxiliary device members in at least two independent degrees of freedom,

a bio-signal sensing unit that is configured to acquire bio-signals indicative of motor activity from the portion or remaining portion of the limb,

a control unit, including at least one processor unit and at least one digital data memory unit to which the at least one processor unit has data access, the control unit being configured to

receive and evaluate the acquired bio-signals to estimate an intended movement, by carrying out steps comprising

digitally converting the acquired bio-signals,

applying a novelty detection method for comparing the digitally converted, acquired bio-signals with data that reside in the at least one digital

data memory unit and that represent bio-signals in response to a set of movements, and, based on a result of the comparison,

assigning a similarity measure to the digitally converted, acquired bio-signals,

applying at least one sequential estimator (SEQ-E) to the digitally converted bio-signals, if the assigned similarity measure is equal to or larger than a pre-determined threshold value,

applying at least one simultaneous estimator (SIM-E) to the digitally converted bio-signals, if the assigned similarity measure is less than the pre-determined threshold value,

and the control unit is further configured to activate at least one actuator out of the plurality of actuators, based on a result of applying the at least one sequential estimator (SEQ-E) or the at least one simultaneous estimator (SIM-E) to the digitally converted bio-signals.

2. The multi-functional limb movement auxiliary device as claimed in claim 1, wherein the novelty detection method (ND) is selected out of a group comprising One Class Support Vector Machine (OCSVM), Kernel Null Foley-Sammon Transform (KNFST), Mahalanobis distance (MD) approach, Mahalanobis distance-Individual (MD-IND), Mahalanobis distance lineal discriminant analysis (MD-LDA), k-nearest neighbors (kNN)-based approach, or Common Spatial Patterns Proportional Estimator Likelihood (CSP-PELL).

3. The multi-functional limb movement auxiliary device as claimed in claim 1, wherein the data stored in the at least one digital data memory unit represent bio-signals of a user of the limb movement auxiliary device.

4. The multi-functional limb movement auxiliary device as claimed in claim 1, wherein the data stored in the at least one digital data memory unit represent at least one out of

bio-signals in response to movements in a single degree of freedom, for all of the at least two independent degrees of freedom, or

bio-signals in response to movements combining at least two degrees of freedom.

5. The multi-functional limb movement auxiliary device as claimed in claim 1, wherein the control unit is configured to set a period time to periodically receive and evaluate bio-signals acquired in a time interval of specified duration, wherein the specified duration of the time interval is longer than the period time.

6. The multi-functional limb movement auxiliary device as claimed in claim 1, wherein the limb movement auxiliary device is designed as a human hand prosthesis, and wherein the socket member is configured to partially receive the remaining portion of a human forearm.

7. The multi-functional limb movement auxiliary device as claimed in claim 1, further comprising at least one wireless data link between the bio-signal sensing unit and the control unit for wirelessly transferring the digitally converted, acquired bio-signals.

8. A method of operating a multi-functional limb movement auxiliary device, the limb movement auxiliary device comprising

a base member,

a socket member that is configured to at least partially receive a portion or remaining portion of a limb,

a plurality of limb movement auxiliary device members, each limb movement auxiliary device member being connected in an articulated way to at least one of the base member or a different limb movement auxiliary device member,

a plurality of actuators that are connectable to an electric power source, wherein each actuator is configured to move, upon activation, at least one limb movement auxiliary device member out of the plurality of limb movement auxiliary device members in an articulated way, and wherein the plurality of actuators is configured to enable articulated movements of limb movement auxiliary device members in at least two independent degrees of freedom,

a bio-signal sensing unit that is configured to acquire bio-signals indicative of motor activity from the portion or remaining portion of the limb, and

a control unit, including at least one processor unit and at least one digital data memory unit to which the at least one processor unit has data access, the method comprising steps of

acquiring bio-signals from the user of the limb movement auxiliary device,

digitally converting the acquired bio-signals,

applying a novelty detection method (ND) for comparing the acquired, digitally converted bio-signals with calibration bio-signals that reside in the at least one digital data memory unit,

based on a result of the comparison, assigning a similarity measure to the acquired, digitally converted bio-signals,

applying at least one sequential estimator (SEQ-E) to the acquired, digitally converted bio-signals, if the assigned similarity measure is equal to or larger than a pre-determined threshold value,

applying at least one simultaneous estimator (SIM-E) to the acquired, digitally converted bio-signals, if the assigned similarity measure is smaller than the pre-determined threshold value, and

activating at least one actuator out of the plurality of actuators, based on a result of the step of applying the at least one sequential estimator (SEQ-E) or of applying the at least one simultaneous estimator (SIM-E) to the acquired, digitally converted bio-signals.

9. The method as claimed in claim 8, further comprising steps of

acquiring bio-signals indicative of motor activity from a user of the limb movement auxiliary device while the user is executing a set of movements, and the set of movements includes at least one movement in a single degree of freedom or movements combining at least two degrees of freedom,

digitally converting the acquired bio-signals, and

storing the acquired, digitally converted bio-signals in the at least one digital data memory unit.

10. The method as claimed in claim 8, wherein the novelty detection method (ND) is selected out of a group comprising One Class Support Vector Machine (OCSVM), Kernel Null Foley-Sammon Transform (KNFST), Mahalanobis distance (MD), Mahalanobis distance-Individual (MD-IND), Mahalanobis distance lineal discriminant analysis (MD-LDA), k-nearest neighbors (kNN)-based approach, or Common Spatial Patterns Proportional Estimator Likelihood (CSP-PELL).

11. The method as claimed in claim 8, further comprising a software module for carrying out the method steps, wherein the method steps to be conducted are converted into a program code of the software module, wherein the program code is implementable in a digital data memory unit of the multi-functional limb movement auxiliary device and is executable by a processor unit of the multi-functional limb movement auxiliary device.