Prosthetic limb monitoring system

• US 9,072,463 B2
• Filed: 01/27/2010
• Issued: 07/07/2015
• Est. Priority Date: 01/27/2009
• Status: Expired due to Fees

• Current Assignee: University of Washington
• Original Assignee: University of Washington
• Inventors: Sanders, Joan, Myers, Timothy, Hafner, Brian
• Primary Examiner(s): EISEMAN, ADAM JARED
• Application Number:
  US12/694,409
• Publication Number:
  US 20100191153A1
• Time in Patent Office: 1,987 Days
• Field of Search: 600/587, 600/592, 600/595
• US Class Current: 1/1
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/4585 Evaluating the knee

Abstract

Parameters related to use of a prosthesis by a patient with a limb amputation are monitored using a tool that includes one or more piezoelectric force sensors. The resulting data are processed for use both in short and long term management of amputee patients. The sensor is a small modular unit that fits within or between traditional prosthetic components, e.g., below a prosthesis socket. The data produced by the tool are collected, processed, and stored. Optionally, the data are periodically communicated to a remote site via a network, e.g., over the Internet. The device and associated software used to process the data can be used to characterize activities conducted by a prosthesis user, to determine pistoning or threatening interface stress distributions between the limb and socket, mal-alignment of the socket, use of improper components, and other possibly undesired conditions that the amputee patient using the prosthesis may be experiencing.

Claims

1. A method for collecting, processing, and employing data indicative of kinetic parameters experienced by a prosthesis while a user fitted with the prosthesis engages in activities, comprising the steps of:

(a) attaching one or more pre-loaded piezoelectric sensors to the prosthesis to monitor forces experienced by the prosthesis as a result of the user wearing the prosthesis while engaging in the activities, each of the one or more pre-loaded piezoelectric sensor developing a charge indicative of a kinetic parameter experienced by prosthesis;

(b) amplifying the charge produced by each of the one or more pre-loaded piezoelectric sensors, producing a corresponding output signal indicative of the parameter experienced by the prosthesis, the output signal from each of the one or more pre-loaded piezoelectric sensors being used to produce the data indicating the kinetic parameters; and

(c) processing the data to determine the kinetic parameters experienced by the prosthesis and for evaluating conditions related to use of the prosthesis by the user, wherein the step of processing the data includes the steps of;

(i) segmenting the data into equal duration data segments by dividing the data by a predetermined interval of time, wherein a duration of the predetermined interval of time is selected to match features of interest needed to carry out a desired analysis of the data;

(ii) determining an applied force or moment for each of the data segments over the duration of the data segment;

(iii) characterizing each of the data segments into groups based on a difference between a maximum and a minimum of the applied force or moment of the data segment in comparison to the applied force or moment measured by the one or more pre-loaded piezoelectric sensors.

2. The method of claim 1, wherein the step of processing comprises the step of characterizing the activities that are carried out by the user while wearing the prosthesis.

3. The method of claim 1, wherein the data comprise two or more parameters selected from a group of parameters consisting of:

(a) a component of force directed along one of a plurality of orthogonal axes;

(b) a moment relative to at least one of the plurality of orthogonal axes;

(c) an indication of the prosthesis bending;

(d) an indication of at least one parameter on a foot portion of the prosthesis, where said at least one parameter is selected from a group of parameters consisting of;

(i) a resultant force position;

(ii) a direction that a force is applied; and

(iii) a center of pressure;

(e) an indication of a position where a shear force is applied to the prosthesis;

(f) a temperature of a portion of the prosthesis;

(g) a velocity of the prosthesis as the user moves about;

(h) an angle of the prosthesis; and

(i) a force inside a socket of the prosthesis.

4. The method of claim 1, further comprising the step of supplying electrical energy to energize the one or more piezoelectric force sensor and other components used for collecting the data by harvesting energy as a result of the activities in which the user engages while wearing the prosthesis.

5. The method of claim 4, wherein the step of supplying electrical energy comprises at least one step selected from the group of steps consisting of:

(a) using a force resulting from the user engaging in activity to provide an input to a piezoelectric generator, producing an electrical output current;

(b) using a temperature differential developed between a portion of a body of the user and an environment to generate the electrical output with a Peltier device; and

(c) using a chemical gradient developed due to a chemical change that occurs as a result of the activity of the user within one of a tissue of the body of the user, and a material of the prosthesis.

6. The method of claim 1, further comprising the steps of:

(a) communicating the data to an automatically adjustable component of the prosthesis; and

,

(b) using the data for automatically adjusting the component to more effectively fit the prosthesis to the user, in regard to the activity of the user.

7. The method of claim 1, further comprising the steps of:

(a) automatically activating collection of the data when the user is at a specific predefined location where the user engages in a training session; and

(b) processing the data that were automatically collected to provide at least one of a medical practitioner and the user with information about a progress of the user in the training session.

8. The method of claim 1, wherein the step of processing the data includes the steps of preparing and presenting a report showing information derived from the data, to provide feedback to the user about a condition related to the use of the prosthesis by the user, wherein the information comprising the report are presented to the user on a device that is readily accessible by the user.

9. The method of claim 1, wherein the step of processing the data includes the steps of automatically:

(a) identifying a pattern for an activity being performed by the user;

(b) detecting whether the activity places the user at risk of injury, and if so, carrying out at least one of the steps of;

(i) warning the user that the activity identified is placing the user at risk of injury; and

(ii) automatically adjusting a component of the prosthesis to reduce a risk of injury to the user as a result of the activity recognized.

10. The method of claim 1, wherein the step of processing the data includes the step of processing the data to provide information for enabling a medical practitioner to determine if the user is either overusing or underusing the prosthesis, or is at a risk of an injury, when engaging in one or more activities.

11. The method of claim 1, wherein the step of processing the data includes the step of processing the data to provide information for enabling a medical practitioner to determine if components used in the prosthesis are appropriate to fit the user properly or should be changed to better fit the user in connection with the activities in which the user has been engaging.

12. The method of claim 1, wherein the step of processing the data further includes the step of characterizing the groups of data based on a desired scheme, to represent a plurality of biomechanical events experienced by the prosthesis, in regard to a magnitude of at least one parameter selected from a group of parameters consisting of a force and a moment applied to the prosthesis.

13. The method of claim 12, wherein the step of processing the data further includes the steps of determining:

(a) a location for a center of pressure on a portion of the prosthesis; and

(b) the plurality of moments acting on the prosthesis, based on the force components and the center of pressure.

14. The method of claim 12, wherein the step of processing the data further includes the step of automatically preparing a report for at least one of the user and a medical practitioner, wherein the report presents results from the step of characterizing the groups of data based on the desired scheme, so as to represent the plurality of biomechanical events.

15. The method of claim 1, further comprising the step of using the data with a model to predict at least one of where and when soft tissue breakdown will occur as a result of use of the prosthesis by the user.

16. The method of claim 1, further comprising the step of using the data with a model to assess an alignment of components of the prosthesis and to determine any change that should be made to correct a problem with the alignment.

17. The method of claim 1, further comprising the step of employing results of the step of processing the data to determine if the activities in which the patient engages are cause for changing the prosthesis or a component of the prosthesis used by the patient.

18. The method of claim 1, wherein the one or more pre-loaded piezoelectric sensors are pre-loaded between two plates so as to allow tension and compression measurements along a sensing axis of the piezoelectric sensors, provide more linear performance, and reduce axial force measurement distortions due to sagittal bending moments.

19. The method of claim 18 wherein the one or more pre-loaded piezoelectric sensors are pre-loaded using a bolt which passes through a center of the one or more pre-loaded piezoelectric sensors and couples the two plates together.

20. The method of claim 1, wherein the one or more pre-loaded piezoelectric sensors monitor forces, singly or in combination, in three axes.

21. The method of claim 19, further comprising at least one strain gauge for measuring moments in the transverse plane.

22. The method of claim 1, wherein the data segments are characterized into groups by comparing the difference between the maximum and the minimum of the applied force or moment of the data segment to a predetermined percentage of the applied force or moment measured by the one or more pre-loaded piezoelectric sensors.

23. The method of claim 22, wherein the predetermined percentage of the applied force or moment measured by the one or more pre-loaded piezoelectric sensors comprises 10-15% of the applied force or moment measured by the one or more pre-loaded piezoelectric sensors.

24. The method of claim 22, wherein data segments are characterized into a static group when the difference is less than the predetermined percentage of the applied force or moment measured by the one or more pre-loaded piezoelectric sensors and wherein data segments are characterized into a nonstatic group when the difference is greater than the predetermined percentage of the applied force or moment measured by the one or more pre-loaded piezoelectric sensors.

25. The method of claim 24, wherein the data segments of the static group is further separated into subgroups based on a position of the applied force on a bottom of a foot of the prosthesis.

26. The method of claim 25, wherein the data segments of the static group are separated into a forefoot loading subgroup when the position of the applied force on the bottom of the foot of the prosthesis is anterior to an arch of the foot, a hindfoot loading subgroup when the position of the applied force on the bottom of the foot of the prosthesis is posterior to the arch of the foot, and a balanced loading subgroup when the position of the applied force on the bottom of the foot of the prosthesis is in the arch of the foot.

27. The method of claim 26, wherein the data segments of the forefoot loading subgroup, the hindfoot loading subgroup, and the balanced loading subgroup of the static group are each characterized based on a magnitude of the applied force relative to a weight of the user.

28. The method of claim 27, wherein the data segments of each of the forefoot loading subgroup, the hindfoot loading subgroup, and the balanced loading subgroup are characterized into a low subgroup of each of the forefoot loading subgroup, the hindfoot loading subgroup, and the balanced loading subgroup when the magnitude of the applied force is less than 30% of the weight of the user, a medium subgroup of each of the forefoot loading subgroup, the hindfoot loading subgroup, and the balanced loading subgroup when the magnitude of the applied force is between 30% and 70% of the weight of the user, and a high subgroup of each of the forefoot loading subgroup, the hindfoot loading subgroup, and the balanced loading subgroup when the magnitude of the applied force is greater than 70% of the weight of the user.

29. The method of claim 24, wherein the data segments of the nonstatic group are further separated into a cyclic subgroup and an isolated subgroup based on whether the applied force or moment measurements contain a repeated portion.

30. The method of claim 29, wherein the data segments of the cyclic subgroup and isolated subgroup are further separated into a forefoot loading subgroup of each of the cyclic and isolated subgroups when a position of the applied force on a bottom of a foot of the prosthesis is anterior to an arch of the foot, a hindfoot loading subgroup of each of the cyclic and isolated subgroups when the position of the applied force on the bottom of the foot of the prosthesis is posterior to the arch of the foot, and a balanced loading subgroup of each of the cyclic and isolated subgroups when the position of the applied force on the bottom of the foot of the prosthesis is in the arch of the foot.

31. The method of claim 30, wherein the data segments of the forefoot loading subgroup, the hindfoot loading subgroup, and the balanced loading subgroup of the cyclic and isolated subgroups are each characterized based on a magnitude of the applied force relative to a weight of the user.

32. The method of claim 31, wherein the data segments of each of the forefoot loading subgroup, the hindfoot loading subgroup, and the balanced loading subgroup of the cyclic and isolated subgroups are characterized into a low subgroup of each of the forefoot loading, the hindfoot loading, and the balanced loading subgroups of the cyclic and isolated subgroups when the magnitude of the applied force is less than 30% of the weight of the user, a medium subgroup of each of the forefoot loading, the hindfoot loading, and the balanced loading subgroups of the cyclic and isolated subgroups when the magnitude of the applied force is between 30% and 70% of the weight of the user, and a high subgroup of each of the forefoot loading, the hindfoot loading, and the balanced loading subgroups of the cyclic and isolated subgroups when the magnitude of the applied force is greater than 70% of the weight of the user.

33. The method of claim 29, further comprising:

processing the data segments of at least one of the cyclic and isolated subgroups to identify a stance phase in one or more of the data segments;

extracting the stance phase;

conducting a spectral analysis on the stance phase to identify a perturbation associated with unstable coupling of the prosthesis.