Kinetic Sensing, Signal Generation, Feature Extraction, And Pattern Recognition For Control Of Autonomous Wearable Leg Devices
20220257389 · 2022-08-18
Inventors
- Hugh M. Herr (Concord, NH, US)
- Roman Stolyarov (Britsol, RI, US)
- Luke M. Mooney (Westford, MA, US)
- Cameron Taylor (Wayzata, MN, US)
- Matthew Carney (Somerville, MA, US)
Cpc classification
A61F2002/7635
HUMAN NECESSITIES
A61F2002/763
HUMAN NECESSITIES
A61F2002/7685
HUMAN NECESSITIES
A61F5/0102
HUMAN NECESSITIES
B25J9/0006
PERFORMING OPERATIONS; TRANSPORTING
A61H3/00
HUMAN NECESSITIES
A61B5/7275
HUMAN NECESSITIES
International classification
A61B5/00
HUMAN NECESSITIES
A61B5/11
HUMAN NECESSITIES
A61F5/01
HUMAN NECESSITIES
A61H3/00
HUMAN NECESSITIES
Abstract
An autonomous wearable leg device employs an array of sensors embedded along a support area, whereby a controller can generate a controlling command and send a controlling command to a prosthetic, orthotic, exoskeletal or wearable component to thereby control the prosthetic, orthotic, exoskeletal or wearable component. A method for controlling autonomous wearable device collects kinetic signals from an array of sensors embedded in a prosthetic, orthotic or exoskeletal component, wherein all values are extracted from at least one feature of the collected kinetic signals, which are applied to a controller that generates a controlling command that is sent to the prosthetic, orthotic exoskeletal component to thereby control the prosthetic, orthotic or exoskeletal component during a portion of a gait cycle.
Claims
1.-22. (canceled)
23. An autonomous wearable leg device, comprising: a) an ankle frame; b) a pair of actuators, each actuator being mounted on the frame and connectable to a power source, and control signal, wherein the actuators are independently controllable; c) an eccentric crank-arm linkage connecting the actuator to the foot interface; d) a foot interface connected to the actuators, whereby actuation of either of the actuators transmits force to the foot interface; and e) at least one hinge at the frame linking the ankle frame to the foot interface, whereby synchronous movement of the actuators causes plantarflexion or dorsiflexion of the foot interface component, and differential movement of the linkages causes eversion or inversion of the foot interface.
24. The autonomous device of claim 23, wherein the hinge includes a ball and socket assembly.
25. The autonomous device of claim 23, wherein the hinge includes a gimbal assembly.
26. The autonomous device of claim 24, wherein the gimbal assembly defines two axes of rotation, whereby one of the two axes defines an axis of rotation for dorsiflexion and plantarflexion, and the other of the two axes defines an axis of rotation for eversion and inversion of the first foot interface.
27. The autonomous device of claim 25, wherein the axes of rotation intersect.
28. The autonomous device of claim 26, wherein the axes of rotation intersect orthogonally.
29. The autonomous device of claim 26, wherein the axes of rotation intersect at an angle that is not orthogonal.
30. The autonomous device of claim 25, wherein the axes of rotation do not intersect.
31. The autonomous device of claim 29, wherein the axes of rotation are oriented orthogonally.
32. The autonomous device of claim 29, wherein the axes of rotation are oriented askew.
33. The autonomous device of claim 23, wherein the hinges include an ankle joint and subtalar joint linked to the ankle joint, wherein the ankle joint includes one degree of freedom, and the subtalar joint includes the other degree of freedom.
34. The autonomous device of claim 23, wherein the actuators are mirrored across a sagittal plane of the prosthesis.
35. The autonomous device of claim 23, wherein the actuators are each connected to the foot frame by way of an eccentric crank-arm linkage.
36. The autonomous device of claim 23, wherein the eccentric crank-arm linkage assembly of each actuator includes a gear reduction component having a serially-connected multi-stage timing belt drive-train and an output timing pulley linking the timing belt drive train with the respective linkage.
37. The autonomous device of claim 32, further including a foot component connected to the foot interface.
38. The autonomous device of claim 33, wherein the motors are split phase sector motors.
39. The autonomous device of claim 34, wherein the foot component includes at least one powered metatarsophalangeal joint.
40. The autonomous device of claim 23, further including a knee component connected to the ankle frame, having a single degree of freedom.
41. The autonomous device of claim 38, wherein the knee component includes a knee joint and a powered knee actuation system linked to the knee joint, wherein the knee joint has the degree of freedom of the knee component.
42. The autonomous device of claim 39, wherein the powered knee actuation system includes a clutched series static actuator.
43. The autonomous device of claim 40, wherein ankle component includes an ankle joint and a subtalar joint linked to the ankle joint, wherein the ankle joint includes one degree of freedom, and the subtalar joint includes the other degree of freedom of the ankle component.
44. The autonomous device of claim 41, wherein the foot component includes a metatarsophalangeal joint having the degree of freedom of the foot component.
45. The autonomous device of claim 42, wherein the knee component further includes a series elastic actuator.
46. The autonomous device of claim 43, wherein the series elastic actuator is a clutchable series elastic actuator.
47.-166. (canceled)
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The foregoing will be apparent from the following more particular description of example embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments.
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DETAILED DESCRIPTION OF THE INVENTION
[0047] The inventions described here are directed toward both the control methods and mechanical design of autonomous wearable leg devices (AWLDs) including autonomous transtibial or transfemoral prostheses, exoskeletons, and orthotics.
Control Methods
[0048] These inventions include methods of actuating AWLDs by modulating force or torque, power, position, velocity, work, or other control variables or provide feedback to the user in the form of mechanical or electrical stimuli. The methods of the invention are performed in real time and employ integrated, autonomous sensing, microprocessing, and actuation systems.
[0049] In one embodiment, the invention is a method for controlling an AWLD that incorporates integrated, real-time, kinetic sensing. Kinetic sensors are embedded either on an area for ground support belonging to the AWLD or as one or a plurality of load cells within a prosthetic thigh or shank. A controller is in communication with the sensors, whereby the sensors can collectively sense and transmit spatially-dependent pressure signals or localized forces and moments to the controller, and whereby the controller can generate a controlling command and send the controlling command to AWLD, and thereby control the AWLD.
[0050] In one embodiment, the controller of the AWLD uses the spatially varying pressure signals to estimate ground reaction force (GRF) and center of pressure (COP), and extracts features on these signals within the time window when the support area is in the ground, and inputs these features into a machine learning classifier to predict gait activity, gait event, or terrain of the next step. In this embodiment, the vertical component F.sub.Z of the GRF is estimated as:
[0051] where p.sub.i is the strain at each sensor i and Nis then total number of sensels. Furthermore, COP components G and G can be estimated as:
where x.sub.i and y.sub.i are the positions of each sensor i. Additionally, signals from individual sensors can also be used as input into the controller, whether for actuation in a particular gait state or for detecting and triggering state, task, or mode transitions.
[0052] In the case of a transtibial load cell within a prosthesis, a static model can be used to estimate the GRF vector and location of the COP using three-dimensional moments and forces measured by such a load cell. The estimation contemplates only single stance support and the coordinate system employed for the model is Cartesian. The sign convention for the ground reaction force is positive upward and forward. The reference frames, shown in
[0053] Here the left superscripts represent the reference frame and the right subscripts represent the coordinate direction. The values employed are defined in the following way: .sup.SM.sub.YS, .sup.SF.sub.YS are the moment and force, respectively, around the Ys axis of frame {S}; .sup.SM.sub.XS, .sup.SF.sub.XS are the moment and force, respectively, around the X.sub.S axis of frame {S}; .sup.SF.sub.ZS is the force in the Z.sub.S direction of frame {S}; .sup.rT.sub.ZS is the distance in Z.sub.S to the COP. For an active prosthesis system this value can be computed as Z.sub.0 cos γ where Z.sub.0 is the load cell height at zero degrees of ankle flexion and γ is the shank angle in {G}. This assumes only rotation in the sagittal plane with no adduction or abduction in the joint. Assuming negligible inversion-eversion of the foot-ankle joint system, we can assume that the rotation matrix that relates frames {S} relative to {P} can be represented by this single sagittal angle γ. Given the COP location, the forces that interact with the ground, relative to the COP frame {P}, can then be expressed as:
.sup.PF.sub.X.sub.
.sup.PF.sub.Y.sub.
.sup.PF.sub.Z.sub.
[0054] In the case of a transfemoral load cell, estimation of COP and GRF can be performed using knowledge of knee state and spatial transformations similar to those used for the transtibial load cell.
[0055] In another embodiment, a machine learning classifier can be used on the kinetic signals to predict upcoming gait events or terrains based on features extracted from these signals in real time. The classifier could include a heuristic method, a Naive Bayesian classifier; a decision tree classifier; a linear or quadratic discriminant classifier; a neural network classifier; and a logistic regression classifier.
[0056] In an embodiment of the invention, a method includes application of kinetic sensing and control to obtain biomimetic control strategies for AWLDs to define a hierarchical control architecture. One level of such a hierarchy is mode, which includes such general activities as walking, running, sitting, or standing. Another level is task, the categories of which vary depending on activity. For example, walking tasks include walking on different terrains such as flat ground, stairs, or inclines. Finally, each task can be composed of a next hierarchical level, namely sequential states which are, typically, periodic. For example, walking on flat ground can be divided into swing phase, controlled plantarflexion, controlled dorsiflexion, and powered plantarflexion at the end of stance. Various modes, tasks, and states along with relevant transitions are visualized in
[0057] In one specific version of this device, an array of kinetic sensors can be embedded along the support area of a standard prosthetic foot cover and used to transmit spatially dependent pressure signals to the prosthesis controller. An example of such a configuration is exhibited in
[0058] Alternatively, an array of such sensors can be embedded within the support area of the prosthetic foot itself, as illustrated in
[0059] In yet another embodiment (not shown), an array of sensors is embedded within a transtibial or transfemoral prosthetic socket, or embedded within the sole of a shoe, or embedded within a sock.
[0060] In still another embodiment, the invention includes real-time provision of mechanical or electrical feedback to the user of an AWLD. Kinetic state variables, as described herein can be employed to provide feedback to the AWLD user in the form of mechanical or electrical stimuli. Mechanical stimuli can include vibration, application of normal pressure to the skin, skin pinching or strain application, or skin surface temperature variation. Electrical stimuli include electrical stimulation of muscles or nerves.
[0061] In another embodiment of the invention, one or more contact-free sensors can be employed within the foot covering of either a biological foot or prosthetic foot, either in addition or as an alternative to contact or pressure sensors. Examples of suitable contact-free sensors include cameras, distance-measuring sensors, and laser scanners. Such sensors, when employed, can be placed in communication with an AWLD controller that uses signals from these sensors to predict and respond to gait events and terrain changes by actuating AWLD in real time. The contact-free sensors can be aligned in any orientation or position either interior or exterior to an associated sock, foot cover, or shoe. With respect to powered exoskeletons and orthosis controllers, contact-free sensors can be aligned in any orientation or position either exterior or interior to an associated exoskeleton or orthosis.
[0062] In one embodiment, at least one non-contact sensor is positioned in a forward orientation at a toe area of a shoe, whereby it is used to predict that the user will ascend stairs or clear an obstacle, as visualized in
[0063] Another embodiment of the invention includes real-time statistical or diagnostic monitoring in AWLDs using kinetic or non-contact sensors. Signals generated using these sensors can be monitored to provide statistical or diagnostic information about the AWLD. Statistical information can include, for example, statistics, step counts, information about power or force output, time spent, and electrical or metabolic work done in various modes, tasks, or states. This can be used to collect information from one user or across many users. Diagnostic information concerns any data regarding intended operation of the device or deviation from intended operation.
Mechanical Design
[0064] Another embodiment of the invention includes an autonomous multi-degree of freedom lower limb prosthesis, orthotic, exoskeleton, or wearable system that relies on signals, features, and pattern recognition on kinetic signals for control, such as an autonomous knee-ankle-foot, transfemoral prosthesis system with four powered/actuated degrees of freedom (DOFs) or powered axes of motion. The embodiment of the four DOF system shown in
[0065] In this embodiment, an open-source FlexSEA bionic control architecture is utilized. This electronics architecture integrates high powered electronics 292 with flexible, high-fidelity sensing. In one embodiment, each actuated degree of freedom include power electronics 292 and high fidelity sensing electronics (referred to as “Execute boards”) controlled by Cypress Semiconductor PSOC microprocessors. Digital and analog input-output (I/O) functionality on the Execute board include native inertial measurement units, strain gage amplifiers, and expandable generic I/O. Generally, all DOFs are simultaneously connected and controlled with a single high level controller (Management board) 293, based on the STMicrosystems STM32 microprocessor, that fuses all sensors and state information. In this embodiment, the Management board 293 performs the high-level control that includes mode, task, state identification and operations as shown in
[0066] In a specific embodiment, the invention is an ankle-foot prosthesis, having two DOFs. A passive foot prosthesis is shown in
[0067] As that term is understood herein, the term “linkage” means a mechanical component that transmits bidirectional force with a push-pull action such as push-rod or flexure, a unidirectional tensile component such as a belt, cable, or chain, or a torsional component transmitting rotary motion directly through a rotary element, or a combination of elements such as a roller-cam element.
[0068] In detail, in this embodiment one actuator as shown in
[0069] In the embodiment shown, high-torque, brushless, and direct current (BLDC) split phase sector motors (also referred to as “outrunner” motors) 291, for example, can be utilized as torque generators. In this embodiment, each actuator consists of one motor controlled by an Execute electronic control board 292, their synchronization controlled by a Manage microprocessor based high-level controller 293. Due to their torque-density, these split phase sector motors enable reduced transmission ratios from those of typically-available prosthetics. The lower reflected inertia and frictional losses of the reduced transmission ratio results in a higher bandwidth, more dynamic, efficient and quiet system, and also enables a more accurate observation of output torque by way of current sensing at the motor power electronics, reducing the need for additional load-cells, specific series compliance and displacement measurement systems.
[0070] In this embodiment, homodirectional or synchronous motion between the left and right push-rods 288 affect ankle rotation for dorsiflexion and plantarflexion (plantarflexion is shown in
[0071] For this embodiment,
[0072] The embodiment of the gimbal as described is more clearly visible in
[0073] In still another embodiment, the invention includes a single DOF foot device (
[0074] In yet another embodiment, in
[0075] In
[0076] One embodiment of the invention is an autonomous four DOF knee-ankle-foot prosthesis system comprising a knee joint with a single actuated DOF, ankle-foot-prosthesis with two actuated DOFs, and prosthetic foot with single actuated DOF. Another embodiment includes: an autonomous three DOF knee-ankle-foot prosthesis system comprising a knee with a single actuated DOF; an ankle-foot prosthesis with two actuated DOFs; and a passive prosthetic foot. Still another embodiment of the invention is an autonomous single DOF knee-ankle-foot prosthesis that includes: a knee with a single actuated DOF; a passive ankle-foot prosthesis; and a passive prosthetic foot. Yet another embodiment of the invention is an autonomous three DOF knee-ankle-foot prosthesis that includes: a passive knee joint; an ankle-foot prosthesis with two actuated DOFs; and a prosthetic foot with one actuated DOF. Still another embodiment of the invention is an autonomous two DOF knee-ankle-foot prosthesis that includes: a passive knee joint; an ankle-foot prosthesis with two actuated DOFs; and a passive prosthetic foot. In another embodiment, the invention is an autonomous three DOF ankle-foot prosthesis that includes: an ankle-foot prosthesis with two actuated DOFs; and a prosthetic foot with one actuated DOF. Another embodiment is an autonomous two DOF ankle-foot prosthesis that includes: an ankle-foot prosthesis with two actuated DOFs and a passive prosthetic foot.
[0077] The following is a description of a demonstration of select embodiments of the invention, and is not intended to be limiting in any way.
EXEMPLIFICATION
[0078] We performed a preliminary study in which we asked six subjects with unilateral transtibial amputations (ages ranged between 28 and 66, heights 1.68 m and 1.90 m, and weights between 130 and 229 lbs, all K4 ambulators) to traverse various terrains while signals were collected from an array of kinetic sensors embedded in an insole on the side of the prosthetic device.
[0079] Data for each subject was collected in several trials each involving between eight and twelve circuits. In each trial, subjects were asked to undergo several circuits involving transitions to and from a staircase and flat ground while wearing a powered transtibial prosthesis aligned to a custom fitted socket via a pylon of appropriate length. Each circuit comprised one complete ascent, turn-around maneuver, descent, and subsequent turn-around, allowing for transitions to and from the terrain in either direction. Stairs terminated in a platform allowing for approximately one complete gait cycle completion after exiting the staircase and before turning around.
[0080] Each trial was conducted such that the subject was visible by at least four motion capture cameras at all times, which were set to a capture rate of 100 Hz. Data were collected from the prosthesis sensors (including an inertial measurement unit and motor and ankle joint encoders) and from resistive pressure sensing insoles developed by Tekscan. All subjects wore a rubber cosmesis over their carbon fiber foot, with pressure sensing insoles positioned between the cosmesis and shoe insole and taped to the latter. Pressure sensors were cut by hand to match the size of the shoe's insole.
[0081] Subjects were asked to begin each trial with a quiet period of static, bilateral stance to establish a reference pressure distribution on the sensor. Additionally, sensors were calibrated using a proprietary method provided by Tekscan software that involved collecting a short trial of unilateral (one-legged) stance on the instrumented leg. All subjects were physically labeled by a full lower body marker set (described in a further section).
[0082] Subjects used the BiOM ankle-foot prosthesis (
[0083] Pressure sensing insoles were part of the F-Scan In-Shoe Analysis System developed by Tekscan. The sensors were originally size 14 (US) and trimmed in accordance with the foot size of each subject. Sensor technology was resistive, with 0.15 mm thickness, 25 sensel per in2 resolution, and 862 KPa pressure range. An example is displayed in
[0084] For each trial, various time varying signals were extracted from the frame data including the centers of pressures, integrated pressures across the ball, heel, and whole foot, and derivatives of these signals. All integrated pressure signals were normalized by the mean maximum value achieved across all flat ground to flat ground steps. Next, all stance phase periods within the trial were identified using a threshold on total integrated force, and the boundaries of each stance window were used to identify foot contact and foot off, respectively. For each stance period, a terrain (either flat ground, upstairs, or downstairs) was defined using motion capture data, which included data about subject and staircase position.
[0085] We then extracted various features for each signal from only the stance phase, including mean, maximum, minimum, range, standard deviation, and initial and final values. Additionally, the initial length of stance was used as a feature. All features were then standardized to zero mean and unit variance across the entire dataset.
[0086] Finally, we employed pattern recognition on the features of each step to predict the labeled terrain of the next step correctly. We were able to attain an accuracy of approximately 86% using a 20-fold cross-validated linear discriminant analysis classifier with empirical prior probabilities on data containing transitions among flat ground, upstairs, and downstairs steps. Complete data for all feature subsets using empirical priors are presented in the
REFERENCES
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[0124] The relevant teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety.