SYSTEMS AND METHODS FOR SENSING BALANCED-ACTION FOR IMPROVING MAMMAL WORK-TRACK EFFICIENCY
An example system includes one or more sleeves, each configured for attachment to a leg and comprising a pressure sensor, an accelerometer and a magnetometer. A processor processes sensor signals from the pressure sensor, the accelerometer and the magnetometer to estimate action (A) and work (W) using event detections of peak stance and valley swing events associated with leg movement, for optimizing energy efficiency for maintaining a balance of the body leaning forward in a gravity pull-down force, with the periodic upward force of the sequential foot-thrusts with the toes.
This application is a continuation of application Ser. No. 15/296,766, filed Oct. 18, 2016, which is a continuation of application Ser. No. 14/073,826, filed Nov. 6, 2013, now U.S. Pat. No. 9,470,763, which claims the benefit of provisional application No. 61/723,132, filed on Nov. 6, 2012.
Application Ser. No. 14/073,826 is also a continuation-in-part of application Ser. No. 13/570,152, filed Aug. 8, 2012, which is a continuation-in-part of application Ser. No. 12/805,496, filed on Aug. 3, 2010, which claims the benefit of provisional application No. 61/344,260, filed on Jun. 21, 2010; of provisional application No. 61/344,026, filed on May 10, 2010; and of provisional application No. 61/282,527, filed Feb. 25, 2010.
Application Ser. No. 13/570,152 claims the benefit of provisional application No. 61/521,278, filed on Aug. 8, 2011; of provisional application No. 61/556,365 filed on Nov. 7, 2011; and of provisional application No. 61/617,424 filed on Mar. 29, 2012.
The contents of each of these applications are incorporated herein in their entirety.
BACKGROUND AND SUMMARYUnlike the typical motion analysis for external observation of body movement using video cameras and force plate measurements, i.e., as currently used in gait analysis for clinical human locomotion research, the plethysleeve technology (PST) as generally described in U.S. Pat. No. 7,610,166 and U.S. Patent Publication No. 2011/0208444 (the contents of each of which are incorporated herein in their entirety) measures instead, instinctually driven internal leg-forces, using two strap-on bands around the lower body limb muscles. Note that ‘plethysleeve’ refers to, for example, technology generally described in the '166 patent and '444 publication, and not compressional fabrics currently in use by sports runners.
As further described herein, the instrumentation of recreational runners is a newer product technology involving a simplified type of gait analysis, primarily using arrays of sensors on the feet and upper body parts to locate relative motion for extracting gait parameters. But, since the muscle force measured by PST is generated from cognitive awareness, it is similar to what might be derived from human sensing of perceived force differences, as cues in dynamic motion that efficiently moves one forward on a path. PST is modeled as a foot step placement in making a TRACK, and ‘falling-forward’ with gravity's pull to the next step, while maintaining stability in an upright posture by efficient appendage motion (e.g., non-translational motion or BALANCE). PST incorporates Micro Electro Mechanical System (MEMS) sensors with RF intra-connectivity and onboard processing to automatically provide locomotion efficiency information. This force sensing ‘perception’ is measured in real-time and is efficiently distilled into accurate parameters automatically.
As described below by way of example and without limitation, one aspect of PST measurements is continuously monitoring important muscle activity with pressure sensors in the sleeve band, such as during the swing phase, when typical gait analysis with Ground Reaction Force measurements are absent. PST is like the internal view of driving a car, by turning the wheels and pushing the gas pedal, vs. watching the wheels turn from outside with a video camera used in gait analysis. Here, PST provides a unique ‘signal’ of the full body dynamic, useful for medical diagnosis of deviations from normality in body function to avoid physiological failures, in mental control disruptions to prevent injury, and in deviations from normality in the elderly due to hidden disease. The technology is self-powered, using smart, inexpensive RF-networked sensor-components, being economically feasible and useful for group activities. PST scales across many event and trend time periods beyond a stride cycle, being useful to many applications, by automatically providing simple, situational assessments products for trainer/therapists. Uses range from reducing recreational injuries, improving health care for the elderly, and improving sport performance prediction and improvement using assessment feedback. This automated locomotion information extraction can be provided directly to the individual user as performance and health feedback from audio-earbud/visual-wristwatch. Or, it can be provided to a trainer's field laptop, assessing teams of instrumented players, and also as an uploaded information stream to network reporting for remote assessments, and then finally being warehoused for database mining. This further improves the locally specific cueing of information for the individual as it relates to a more global population. PST is also useful for realtime, mission reporting of military combatants, for health-assessment as Balance distortion in gait, and with potential in GPS-denied navigation, by using Track placement as location changes to augment inertial measurements.
PST—Plethysleeve Technology
MEMS—Micro Electro Mechanical Systems
RF— Radio Frequency
GRF—Ground Reaction Force
R— Right
L—Left
ACL—Anterior Cruciate Ligament
IC— Initial Contact, e.g., heel strike
TO—Toe Off
EMG—Electromyographic (EMG) potentials
COG—Center of Gravity; also Center of Mass (CM)
ACM—About CM dynamics being motion around the body centered CM point
M—Total ‘point mass’ replacement location for CM force modeling
G—Earth's gravitational field vector
g—Earth's G field as acceleration
CP— Gait cycle time period
PE—Potential Energy, e.g., Mgh
KE—Kinetic Energy, e.g., ½M|v|2 and ½I|ω|2
h—Relative vector height in PE for g (scalar distance from the ground)
t1, t2, t3, t4, t5—Sequential time markers in a gait cycle time periods for a stride
x1, x2, x3—Sequential positions in a gait cycle as space positions for a stride
I— Moment of inertia for the angular motion of the upper body
v—CM vector motion velocity for KE computations
ω—About CM angular motion velocity vector
Hz—Hertz units for frequency
SRV— Stride-to-stride variability
IR— Infrared
EOM—Equation of Motion
B— Earth's magnetic field vector
A—Foot step force vector onto ground, as measured with GRF
P— PST sleeve pressure sensor voltage measurement (ith indexing, as Pi)
COP— Center of Pressure, a pointing vector (A) to the CM location from the ground contact
L—Lagrangian Energy defined as L=KE—PE
a—General vector notation for an accelerating force vector (F) on a mass m; F=ma
Ab—Absorption
Gen—Generation
St—Stance
Sw—Swing
PCB— Printed Circuit Board
MAG—Magnetic sensors
GRAV—Accelerometer sensors of gravity
PRES—Pressure sensors
Hg— Mercury; in earlier patents for measuring pressure as a loop of Hg-filled, rubber tubing
2D—Two dimensional geometry
3D—Three dimensional geometry
Bx, By—Magnetic field components measured in a 2D XY plane
Gx, Gy—Gravitational field components measured in a 2D XY plane
FFT—Fast Fourier Transform for spectral analysis of time series data
τ—Scalar representing a time lag used in the time delay of a correlation calculation
τ—Vector of torque, pointing usually from the intersection of two other vectors in contact
A—Action as the time integration of L
W— Work as the vector dot product path integration of a force vector with path distance vector dx
+L, −L—Notation for action and reaction in minimizing the Lagrangian time integrations
RTC—Real Time Clock
LF—Low Frequency
HF— High Frequency
B&T—Balance and Track
A&W— Action and Work
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTSThe movement dynamics of mammals is a complex process of multiple limbs and muscles exerting forces to create forward locomotion. Much of the lower human leg motion is described in the dynamics of the gait cycle with stance and swing phases, as sketched in
The gait cycle is modeled for a person walking, shown in
-
- 1) initial contact (IC) beginning the stance phase with the ground creating a Ground Reaction Force (GRF) to the body from this supporting limb, involving specific pelvis, hip, knee, and ankle joint positions; while these body locomotion positions change, the following, three sequential components occur after IC, for
- 2) loading response (from the downward body response pressure from the ground, as a spring compression from the ‘falling’ energy absorption),
- 3) mid stance from single limb support, and
- 4) terminal stance, of heel-off, and then moving on into the next component of
- 5) toe off(TO) that leads into pre-swing, for the next three swing phase components of
- 6) initial swing,
- 7) mid swing, and finally,
- 8) terminal swing, just before the cycle begins again, to complete the 8 gait periods.
The lower part of
In summary, walking locomotion is modeled as an eight time period sequence for each leg spending 40% in the swing phase of one limb (synched with the other in single, along with double limb stance totaling 60%), and then moves into stance phase while the previous limb moves into swing. These percentages change to an increased swing with running.
Thus, lower body dynamics require considerable balance to maintain effective locomotion in making a track as sequenced footprints placed on the ground. Walking can be described as an evolving falling down process, while balanced on one leg, with a recovery by quickly moving the other leg forward to catch the fall in the swing phase. The GRF in
Considerable research in gait analysis using force plates that measure GRF, as a three dimensional loading vector with friction components, is used to understand the body dynamics, and the dynamics of this force determine the three dimensional momentum of the foot as well. The body vertical force exhibits the double peaked curve during the stance phase of the gait cycle shown in
The gait cycle is shown more specifically in Side and Top Views in
In
The model for locomotion is that of the cognitive brain process commanding specific direction to engage groups of muscles in a synchronized completion of locomotion actions. It will be shown in the example embodiments of the PST that the muscle groups appear to operate in a self-synchronizing manner, particularly in the running phase. In an examination of the neurophysiological basis of adaptive behavior through EEG measurements, Freeman has shown a mass action model for collections of neural “masses,” with time-space behavior in a feedback loop control, which includes limit or terminal cycles, from impulse driven oscillations having characteristic frequencies from a periodic driven nature, or an aperiodic behavior at the sub-system levels. On a global scale, these brain-commanded sequences are brain wave frequencies of alpha (8-12 Hz), theta (3-7 Hz), beta (13-30 Hz), and gamma (30-100 Hz), which are steady state, self-sustaining activities, but show a very short spectral resolution, as an inverse square frequency roll-off for temporal correlation. Freeman proposes the aperiodic activity as stochastic chaos, which is a “ringing” of limit cycle attracters. One can extend this model to dynamic locomotion muscle actions as being impulse driven, aperiodic behavior at the local level, which is globally maintained in a more periodic control function based on the cognitive intentions of the brain. Such behavior might arise from training as ‘muscle memory.’
Hence, in gait analysis, one can see stride-to-stride rate variability (SRV), representing human walking locomotion as an interaction of the central nervous system in the neural functions of the brain, and the intraspinal nervous system with the mechanical periphery at the bones and muscle levels, as a biomechanical model. This is aproprioception sense of locomotion, because there is a feedback from the limb tendons, muscles, and articular joints. However, kinesthesia is distinguished from locomotion by excluding the sense of balance. Proprioception is considered a feed-backward perception by making post-action adjustments with 100 msec delays; however the feed-forward component for balance is also postulated in proprioception, where it is used for more rapid actions based on a pre-action knowledge of the limb locations, such as used in placing the fingers on the nose during a sobriety test to be within 20 mm. Various training mechanisms can improve this balance sensing, such as juggling or standing on a wobble board, which is enhanced with the eyes closed. Thus, locomotion is a combination of footfall placement knowledge after steps occur, and a sense of balance is used for the next footfall placement, creating a track motion. Gait analysis using IR stroboscopic photometry has shown that elderly subjects had up to 20% reduction in velocity and length of stride (with stooped posture, faster cadence, and increased double limb stance) over young adults, and which also included reductions in toe-floor clearance, arm swing, and hip and knee rotations. This is a combined reduction of cadence and stride that normally reduces the expenditure of energy, under the criteria of energy conservation. While this reduced action can be considered that of a change in the neurological health of the elderly, this is why the combined determination of track and balance, when studying the conservation of energy in gait analysis, is critical to avoid artificial effects from stiff joints or absence of breath in the elderly (i.e., requiring a normalization within a variety of studied gaits).
There are five basic temporal patterns in locomotion conditions, and when studied with four walking conditions (normal, kicking a ball, stepping over an obstacle, and stooping right and left while grasping an object), using EMG muscle recordings from between 16 to 31 ipsilateral limb and trunk muscles in a set of 8 subjects, results showed that muscle activation associated with voluntary tasks was either synchronized with the locomotion, or had additional activations supporting a superposition model of compound movements. This complexity can be modeled with nonlinear mathematics shown in multifractal and chaotic Equations of Motion (EOM), and exhibit periodic and aperiodic behavior, which also exhibits irregular SRV, leading to falls in young children.
Unsteady locomotion is a sign of poor integration of muscle function with whole body dynamics and neuromuscular voluntary control, where fast-motion (e.g., running) depends more on local control that can be best modeled with spring-mass dynamics, which creates stabilization during unsteady running from changes in terrain, lateral impulsive perturbations, and changes in substrate stiffness. These stabilization modes might be based on initial conditions, as seen in chaotic models, where the conditions arise from proximo-digital (i.e., length of the humerus) differences in limb muscle architecture, function, and control strategy. Nonlinear fractal exponent modeling for the data has supported correlation with forced pace gait conditions (i.e., metronome pace) having similar fractal exponent values to Parkinson's disease.
There is also a feedback that compensates for length dependent neural control, using ground contact sensing from GRF, which cause a redistribution of energy by the distal muscles through their tendons. The optimization, of this energy use in locomotion, can allow mammals to achieve stability under a variety of conditions. Comparisons between GRF and kinematic (ultrasound) gait measurements of heel-strike and toe-off identification show high correlation, with slight differences with gait speed. This basic locomotion biomechanics is a vaulting over stiff legs in walking and compliant legs in running, but further analysis of these models with data requires a compliant leg for both, and shows that gait is but one of many legged motion solutions accessed by energy and speed, and is useful in stable animal and robotic locomotion. Another element of stability is in the use of a retraction of the swing leg through rotation, just prior to contact with the ground, changing the spring-mass angle-of-attack in responses to disturbances of stance-limb stiffness and forward speed. Robotic studies of four-legged locomotion in simulated and real environments are optimized to minimize energy use in gait locomotion.
Locomotion Upper/Lower Body DynamicsThis gait cycle locomotion action by the lower body can be modeled as an action of the body CM movement in the earth's gravitational field, G, while exerting angular momentum from the upper body motion through the pelvis, about the body CM, as an about center of mass (ACM) motion. The ACM angular changes were measured with respect to the Earth's magnetic field vector (B). Finally, the GRF of the foot thrusts, made during the gait stance as a transfer of CM weight between the two feet, and also as a balance of one foot, while the other foot was in swing, creates a reactive force vector (A) in response to the Earth's force G. This is a vertical pressure component, and two lateral shear components (shown in
However, in
Sleeve Information from Correlation Metrics
The metrics derived from the Balance and Track PST measurements are detailed in ('444 publication, '166 patent), citing figure numbers from '444 publication (3-FIG. notation), are made relative to previous biomechanical models and measurements (3-
Notice in the top part of the
The two orthogonal sensor measurements of
A simulation model was developed that recreates the rounded up pressure of the stance, and the downward, narrowed, “valley” pressure of the swing, as shown in
The feature of the muscles in synchronization, as shown in the modeling of the ‘1-sec gait cycle’ in the simulation and data example of
The metrics of balance and track are based on the application of the foot force vector A, created from the pressure measurements of the sleeve, P, and the B vector location, as shown in
On the other hand, the Track metric can be estimated by the uniformity of the foot path placement estimated from the calf rotation swing component when the gravitational vector angle is aligned with the shank angle at maximum pressure during the TO part of the gait cycle. Together with balance, and the temporal identification of the eight-component, time periods of the gait cycle, a continuous estimate of Track and Balance can be made, based on synchronized MEMS sensor data estimates from the sleeve pair. However, the efficiency of Balance and Track can be estimated using the Lagrangian energy and force measurements for each sleeve, based on the space-time changes in the relative two interaction force vectors (G, A) detailed in Equation L3 for the Lagrangian energy (L), and the EOM for the torque vector (t). The definition of L is given in
A more detailed description in the force diagram is shown in
The description of the Lagrangian in
Within this frame work of balanced Action and Work, one can compute the locomotion efficiency of the Action being minimized under the Principle of Least Action. In the example of walking and running shown in
This review describes how computational modeling can be combined with noninvasive gait measurements to describe and explain muscle and joint function in human locomotion. Five muscles—the gluteus maximus, gluteus medius, vasti, soleus, and gastrocnemius—have been indicated to contribute most significantly to the accelerations of the center of mass in the vertical, fore-aft, and medio-lateral directions when humans walk and run at their preferred speeds. Humans choose to switch from a walk to a run at speeds near 2 m/sec to enhance the biomechanical performance of the ankle plantar flexors and to improve coordination of the knee and ankle muscles during stance. Muscles that do not span a joint can contribute to the contact force transmitted by that joint and therefore affect its stability. In walking, for example, uniarticular muscles that cross the hip and ankle act to create the adduction moment at the knee, thereby contributing to the contact force present in the medial compartment. Many of these muscles are sensed within the placement of PST sleeves on the limbs.
The example systems and methods described in this application relate to the automation of the general field of determining mammal locomotion metrics, from a simple viewpoint when muscular-driven support members propel the body, being that of linear momentum relative to the ground or other surfaces, defined as Track-movement, and being that of angular-momentum relative to the body, defined as Balance-movement. This is uniquely different from gait analysis because these measurements are made by totally self-contained, strap-on-bands that can be worn in any type of locomotion activity including sports, and also by other mammals, such as horses, and does not require human analysis of any collected data. The example systems and methods incorporate band sensors worn on body limbs with networked RF connectivity to compute, using related sensor data and fundamental physical models, muscular motion across multiple band links and within a group of interacting sports players or racing mammals.
The particular sensing described in these measurements relate to the efficiency-of-retaining a Balanced-action of the upper-body angular momentum during Track-movement, which switches between the two lower body limbs, where previously A is defined as the temporally integrated, expressed Lagrangian energy, and also in the efficiency-of-moving the limbs forward during the placement of the foot, as a work Track-force. Here, W is defined as the actual force being integrated, over the spatial transition-distance of the limb, being moved between the forces of gravity and muscular applied thrusting and extending forces (A), as measured by the combined band sensors worn on the body limbs, being applied for the next periodic track foot-step. Because this real-time measurement and monitoring is being made with a very high fidelity, and is made outside the laboratory in the world of more natural activities, the Track and Balance motion viewpoint allows the measured information to be used in physical and mental health assessment. The metrics are in a database format for easy long-term trend analysis and population demographic characterization. Examples include use in sports training, in therapeutic injury-recovery monitoring (e.g., from either a predicted potential-injury diagnosis, or form post-disorders and post-injury repair assessment), and in general health care and treatment of the elderly. This discussion follows, with a focus on the unique viewpoint of Balance and Track, within the previous discussion of typical Gait Analysis.
Gait Analysis—Placing Feet on a TrackThe mammal process of upright locomotion has been characterized for decades with gait analysis using measurements from feet striking force plates during video recordings, being made in simplistic dynamics, such as walking on a treadmill. The physical modeling of forward locomotion is part of biomechanics engineering, using complicated muscle and bone structure anatomy with Newtonian force interaction representations, to characterize the changes from stand-still, to walking, running, and sprinting (at maximum speed), by creating a lower body activity, step-sequence of right (R) and left (L) foot placements used to make a Track. As is well known from early horse racing pictures, running is defined as having periods where all feet are off of the ground. The motion is of the body mass center, rising and falling in a periodic cadence between the R-foot on the Track in the stance phase, and then the Balance of the upper body, to transfer the body mass weight to the placement of the L-foot on the Track ahead of the first step. A final transfer of weight back to the R-foot with a second step completes the two-step gait cycle in time, as a stride of stride-length, at a speed, defined by this length and time, within a two legged, spatiotemporal correlation. These descriptions of Balance and Track use an analytic representation in Lagrangian and Newtonian representations for the physical modeling.
The foot placement track dynamic shown in
-
- Track is the motion sideways, in position of the paddle and in angle relative to the normal gravitational inclination, and
- Balance is the ball position relative to the center point directly above the paddle.
Gravity is the force applied by the rubber band in pulling the ball down to the paddle, and the foot-thrust to move the body mass to the other foot, is the paddle hitting force that drives the ball back up into the air. The shadow of the runner's feet positioning in
Efficient motion is when the ball stays in one position moving up and down in a linear periodic motion directly above the paddle, using a biomechanical model of an inverted pendulum component during the stance phase, oscillating periodically from the ankle/foot-toe, static position. With the knee also being a recognized joint in this modeled motion, this is called a double inverted pendulum. Finally, because the foot placement of the body weight acts like the absorption of motion momentum in compressing a spring when striking the Track, and the re-generation of this absorbed momentum acts like the release of the compressed spring's energy, the model includes a spring for absorbing and generation phases of momentum under conservation. This action creates a change in the circumferential pressure of the calf, which is measured with the PST sleeves, shown as an inset to
An informal analysis of human locomotion is to compare the differences between a baby crawling (all four limbs making tracks) and a football player running (usually with one or no feet on the ground). Training humans to move more efficiently and to stay healthy has enormous benefits; in the PST, the goal in these two comparisons is to move in an upright stance at a faster, safer pace, where the sleeve leg pressure measurements are translated into how one moves, and for professional athletes, effected information is from measured changes in hundredths of a sec increments. Inefficient movement develops fatigue, creating stepping errors, inviting a poor cadence in stepping that is an unbalanced motion. This can create injuries; hence, the desire to move upright vs. the inevitable action of falling down.
Thus the human cycle of forward motion is about the dynamics in daily life, through exercise and sports, where dynamic errors cause injuries and out of the ordinary changes can be precursors of mental changes too. The locomotion of placing feet on the ground to move forward is the historic “1 sec” gait cycle, measuring pace, cadence, step-length, step-rate, speed, and stride-length, where improper dynamics have an inefficient gait. The PST is making a unique and previously unavailable measurement. An interesting way of understanding these changes is to look at images of humans in activities with zero, one, or two feet touching the ground:
TWO FEET Extended force—When stationary, we stand on two feet, or transfer energy between feet when moving or swinging a club, racket, bat, etc. for applying an extended body force, which in many instances, this applied force is while on one foot, in such sports as tennis, golf, cricket, lacrosse, baseball, hockey, etc.
ONE FOOT Changing mass direction—The Newtonian physical modeling relates the “hitting” force (F) while moving to creating changes in the mass (M) direction, as an acceleration (a), which in turn reacts back as an unbalancing force to the human dynamics; this is where the Balance is perturbed, and thus perturbs the Track when the feet return to the ground.
ONE FOOT Applying pushing force—The return of one foot to the ground must include a landing of the body force, combined with the angular momentum carried through the limb contact, which is usually referred to as a turn, cut, etc., which changes body motion direction as an extended, “pushing” force to keep balance with tracks in a new direction. Here, basketball, football, soccer, rugby, and other contact sports involve extending forces through the body to catch balls in the air, push balls in the air towards a hoop or another player, or change direction to avoid another player.
ZERO FEET Regaining Balance on return to ground Track-Finally, there are body dynamics of being without any ground contact, such as throwing a ball while in the air, aligning the limbs after leaving a ski jump, or maneuvering on a snow board in the air, which all create a change in angular momentum of Balance, which must be transferred in an unknown manner back to the Track upon contact with the ground.
Even video action gamers, jump and move in simulated action environments, and elderly walk and run in low contact environments, with a muscle control being guided under a brain dynamic of requests to engage multiple muscles in creative unity of purpose. These actions benefit from Balance and Track measurements in enhancing the body dynamics to the game feedback, or to monitor the body dynamics for internal mental changes in health.
Gait Analysis—Swinging Feet in BalanceJust as important in the gait cycle of the stance phase, is the other, lower body action, which “magically” moves the back foot off of the Track, and places it ahead of the other foot in the stance phase, just in time before the upright mammal falls over as the transfer of weight in the stance phase begins again. This is the stance compliment phase called the swing phase, which is not periodic, and is referred to as being “aperiodic.” While it is easy to refer to this as meeting a physical argument of conservation of upper body angular momentum, the swing phase is anything but a simple, nonlinear action, and is not only not well modeled, but it is also not well measured in the video gait analysis sequences, because multiple cameras are required to describe the 3D motion of the swing leg as it moves back to the stance phase.
Current wearable devices used in gait measurement and recreational activities produce simple data recordings of external force applications, analyzed along with video by a human, to infer characteristics of orderly body limb movement and symmetry, using extensive biomechanical simulation models, but generally without any internal force sensing. The sleeve described herein is used in pairs that correlates motion of both feet through the entire gait cycle and provides information on Balance efficiency in the use of energy dynamic transfer as Action and in Track placement efficiency using the angular momentum of the upper body Balance as Work in lifting and placing feet. An important element of Balance & Track, is not just the stance phase, but more importantly in the swing phase, used to adjust the momentum to reduce force errors from the GRF in re-establishing the next Track.
Sleeve Information Integrated from Pressure Sensor Measurements
A key point of the developed sleeve is the manner in which the human locomotion utilizes energy in achieving efficient work within the gait cycle. This replaces conventional, external gait metrics of force plate data, video cameras, and biomechanical models, with onboard the body, energy and force information from Action and Work computations. Here the gait cycle is just a model of what really happens, to better categorize what is measured with the sleeve sensors. The points for integrated sensor data measurements to produce informed guidance and monitoring requires a precise segmentation of the data as follows:
-
- Gait dynamic characterization exists between a two-step, L-R-L sequence of three-ICs, as the gait cycle, with units of:
- 1. Gait Speed (time to walk at preferred/quick speed for 20 ft), varying with age (20's to >80's, or frail) and sex from 1.18/1.97-3.57/6.4 (ft/sec) for men and 1.38/1.57-3.47/6.43 for women.
- 2. Stride-length (1.5 m), step-length (L-heel to R-heel), step-rate (120 steps/min, which is an average speed of 1.5 m/sec stride-rate) are various distances and clocked time as measured by calibrated photographic recordings of IC placements for a set of footsteps while walking and running, usually performed on a treadmill.
- 3. Three beat, two-step sequence as a cadence in stride, related in mammal locomotion to oxygen uptake and fatigue, with optimal locomotion efficiency, being most efficient at a moderate walking pace.
- PST mammal dynamic information, with units of:
- 1. Balance symmetry averaged over multiple gait cycles as a continuous information output derived from cross leg-stance asymmetric behavior.
- Two-limb correlation analysis for locomotion information
- Optimum conservation of angular momentum in Balance
- Inefficient and abnormal in Balance is asymmetric, or unBalance
- Track footpath placement errors must be corrected by the swing back to IC, for ‘re-spinning’ the angular momentum wheel, to be in synch with Balance
- 2. Stance and swing as smooth transitions between periodic and aperiodic muscle pressures derived from lower body muscle force exertion and relative L to R muscle correlations.
- Sleeve pressures in calibrated sensors change about a mean value between stance and swing phases of cadence
- Periodic and aperiodic time periods have statistical discrimination, with individual gait cycle corrections for Balance, and efficient foot step placement to maintain efficient Track
- 3. Periodic and aperiodic dynamics of cycle change forces and energy use effecting locomotion efficiency.
- Muscle forces change during moving limbs in gravity over distances producing work (W) as the lower body muscles lift and swing the legs after thrusting (A) during TO leaving the supporting gravitational force (G), and StR/SwR absorption/generation of linear momentum (spring in the inverted pendulum).
- Work integrates the force over spatial distance in moving the feet into producing tracks as a Work-Track from (A-G) changes (misaligned vectors have less work from the reduced, projected aligned component).
- Action (A) integrates the Euler-Lagrangian energy (L), from the difference of the kinetic energy (KE) and potential energy (PE), as L=KE-PE over time, where the KE is derived from the mass center, linear dynamics such as forward momentum, and the other mass center nonlinear, angular dynamics, such as angular rotation of upper body inertial momentum, and the PE is derived from the potential changes in gravitational height of the mass center bouncing up and down through stance phases.
- Minimizing the use of L energy is an optimization goal to have efficient transfer between linear and nonlinear dynamics (Principle of Least Action), while also minimizing the action (+L) and reaction (−L) over multiple gait cycles, where the work is reduced with balancing foot thrust forces as push-up directions, with gravitational forces as pull-down directions.
- The interchange between Balanced-Action making efficient Work-Tracks, is controlled at cognitive and muscle memory levels, but also involves feedback from skin sensing of blood flow and muscle pressure, such as the reduced pressure during the swing phase of the leg. This is an efficient sensing channel not obvious in biomechanical models and is the essence of efficient locomotion.
- 4. The example sleeve described herein incorporates the spatiotemporal analysis of sensor measurements in correlation between paired limbs in the lower body, and/or also in the upper body, but at a minimum it is with the L-Calf and R-Calf sensing, with RF links used to correlate the individual work and the Action computations shown next.
- 1. Balance symmetry averaged over multiple gait cycles as a continuous information output derived from cross leg-stance asymmetric behavior.
- PST Action and Work Correlated Computations, where the KE of the swing and thrust gait components and the PE of the stance double support periods contribute to the Action, and the Work is being measured during the singular support stance while moving the swing in a limb motion against momentum and gravity. Example data is presented in relative units of circumference pressure scale change, after scaling of the raw data. Note the following individual and correlated limb discoveries:
- 1.
FIG. 13 for 10 sec/10 cycles of calf pressure data, being shown for both Right (FIG. 13a red color) and Left (FIG. 13b blue color) calf's sleeves, here with:- Vertical zero-crossing dashed lines delineating the stance phases (rounded sine wave top, pointing up, as rounded peaks more typical of GRF data), and
- In-between swing phases (pointing down, as valleys with more pointed bottoms), as cyclic below the
FIG. 13a data, which also correlate with the Right gait phase delineation. The double-hump in the stance is paired with the pointed valley in swing by the opposite leg. Sometimes this pattern is such a ‘mirror image’ pattern, they seem subnormal, yet this is a similar observance.
- 2. Another example of correlated data is shown in
FIG. 14 , with letter labels on valleys (swing) and peaks (stance), for R-leg in red color, marked with “R's,” L-leg in blue color, marked with “L's,” shown for walking at 1 mph for 10 sec (6 cycles,FIG. 14a ,) and running at 5 mph for 10 sec (8 cycles,FIG. 14b ).- Note the ‘double support’ of both legs in stance by the crossing of the traces above the mean in walking, and
- Note how the faster running has self-synching of the swing, to very precise valley ‘ticks,’ while the peaks are moving relative to each other in time location and amplitude.
- 1.
- Sensor Fidelity Improvements, included changes in electronics, fabrication of sensors and materials, and placement within the sleeve, allowing for an inherent impulse response at <1 msec. Examples include:
- 1. Improvements shown in
FIG. 15 for a 2.3-sec/3-stride example has increased pressure sensor fidelity, with IC events marked with a dashed circle, which varies slightly in position in walking, and a - 2. Re-scaling (red color, upper curve) of this high fidelity data in
FIG. 16 , shows an improved linear dynamic range. Here, the trends of the stance upward peaks and the swing downward valleys are very precise for the peak and valley time periods, arguing for a 50%/50% swing to stance peak time ratio, except at the zero crossing, the ratio in time is more typical at 23%/70%. - 3. More simultaneous sleeves, are shown in a further example of 16 channels of correlated, limb muscle pressures, shown in
FIG. 17 , for 2.8 sec/3 gait cycles of data; some data was truncated in the sampling as a clipped voltage); hence, only four muscle-aligned, channels are plotted, noted by limb type and board number abbreviation listed in the title.- Note that the thigh data of
FIG. 17 , has dominate singular pressure increase from lifting of the lower calf limb during the swing phase, shown being paired with calf data (numbers indicate which MEMS module is measured, i.e., LT3 with LC6 for left correlation, and RT19 with RC15 for right correlation, with thigh leading calf swing. - Note the correlative alignment of the thigh and calf (RT lagging to LC in swing; LT lagging to RC in Swing; RT and LT in precise periods on falling edge; LC has an amplitude offset artifact that reduced the stance and truncated the swing; RC has a stance peak reduction with subsequent cycles; RT and LT have trailing, secondary peaks.
- Note also there are individual changes in the stance (and thigh lifting phases, during the swing data), which will be corrected with improved electronics in data sampling, described later.
- Note that the thigh data of
- 1. Improvements shown in
- Gait dynamic characterization exists between a two-step, L-R-L sequence of three-ICs, as the gait cycle, with units of:
The elements of Action and Work are correlated as well, shown in a model representation in
In the earlier
The concept of using PST in a variety of data collections and analysis over a variety of time scales, emulates from the definitions of locomotion within standard gait cycle modeling, and the human cognition and muscle memory neurological processes, as used in psychological and physical therapy (PT) modeling. The standard gait cycle consists of two major phases for each lower body leg, being either stance or swing phase for one or the other leg, with a short time spent in double leg support. Within this cycle there is a two-step stride process for the L-step to Right-step, and then back to Left-step. This basic time scale is on the order of 1 sec (1 Hz) in standard walking, with four components each as complete 8-period locomotion for the two phases. There are also possibly more than TO and IC sub-gait time event components, e.g., the roughly 10 msec IC events in
Obviously, the variations in locomotion analysis over this micro to mesa scale of
With a parameter selection set for the diagonal setting in correlation and integration (τi, Tj), the gait cycle events are feed into a parallel processing to compute the B&T products and the A&W sums as integrations in time and space respectively, with a Buffer Memory to facilitate a realtime output rate of this processing. In this case the channel set is based on a left and right calf set of measurements, which are then merged for distribution in various applications. A higher resolution of the last data example in
The PST technology is based on a precise means of measuring limb muscle pressure concurrent with Earth's magnetic and gravitation field angular location, and vector acceleration on the body COG and linear momentum (CM), and angular velocity and acceleration of the inertia (ACM). The high fidelity of the pressure sensing allows for the many analysis scales of sampling to not loose long term trends, as would be typical in an averaging algorithm over periodic gait cycles. It is the aperiodic cycle of the swing events which creates this internal locomotion ‘ticking’ from both muscle and mental performance. Thus, the pressure sensor measurement circuit and analytic, calibrated scaling removes nonlinear outputs as shown in
Other example constructions for sensing the various parameters described herein are shown in U.S. Pat. No. 7,610,166 and U.S. Patent Publication No. 2011/0208444 (the contents of each of which are incorporated herein in their entirety).
Specific applications for the PST in some of the connectivity shown in
The described systems, methods, and techniques may be implemented in digital electronic circuitry, computer hardware, firmware, software, or in combinations of these elements. Apparatuses and systems embodying these techniques may, for example, include appropriate input and output devices, a computer processor, and a computer program product tangibly embodied in a non-transitory machine-readable storage device for execution by a programmable processor. A process embodying these techniques may be performed by a programmable processor executing a program of instructions to perform desired functions by operating on input data and generating appropriate output (e.g., visual output, aural output, and/or tactile output). The techniques may be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. Each computer program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language may be a compiled or interpreted language. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, a processor will receive instructions and data from a read-only memory and/or a random access memory. Non-transitory storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and Compact Disc Read-Only Memory (CD-ROM). Any of the foregoing may be supplemented by, or incorporated in, specially-designed ASICs (application-specific integrated circuits), logic circuits, gate arrays and the like.
As discussed in detail above, one of the most important activities humans do is to think about what they observe from their “sensors”, and how they use that information in general to move on with their life. An important aspect of this is self-controlled sensing, the ability to move under own power and have the freedom to go where we want, do what we want when we get there, and see/hear what we want as sensory perception of our observations from a better observation point. This is the dynamic of human locomotion, and is fundamental to our thinking and our life's desires.
Humans move like a locomotive traction-engine pulls a train, by creating friction forces on the ground with our feet, because that is the only place we can change our mass location by exerting a force. But because we stand upright, we have to place our feet in front as steps, to move another point down the path of our intended direction, as mentally reaching forward. The connection between the human brain and our sensor perception for locomotion is one and the same: no sensing means no locomotion; no-locomotion means no-brain stimulation. We Locomote by falling down in the general direction we want to go, but in order to not end up on the ground, we place another foot in front to catch us; otherwise we would end up crawling. This is a rather crude method, but it works for every human body, and locomotion is guided by all the sensors as a unitary action. Our feet in bipedal walking or running locomotion, surprisingly are not very sensitive to what shoes we wear, because the body is so adaptable.
While human bio-mechanic models today are extremely complex, we are just beginning to understand and predict how we do it. It is well known that the human is self-synchronizing in a manner that the brain just guides the locomotion goals and the sensing make corrections and change local directions to our muscle memories. But the muscles also tell what is going on by the feedback pain of steps, and twisting and turning, stretching other body action that is perceived. This upper body dynamic throws the legs to where they need to be, as a corrected trajectory and not a rifle precision. Thus, errors in the brain appear as errors in locomotion and errors in the locomotion can appear as problems in the traction engine to create frictional forces.
Thus, the best way to understand locomotion is to measure how the forces on the ground that make the friction with the feet are created and changed. The example systems and methods described above measure the calf muscles which are a major contribution to the foot thrusts in locomotion. The precision of this locomotion is tied to the precise manner that the swing of the leg in planting the foot on the ground is where necessary precision is applied and corrected as needed.
The example systems and methods described herein enable the combining of MEMS 3D gravitational measurement (G) and magnetometer measurements (B), with pressure (P) measurements in spatiotemporal integrations for estimating action (A) and work (W) using event detections of peak Stance and valley Swing events, along with ‘triangular’ curve shape area estimation, scaled relative to “zero” P measurements, and estimating Balance and Track transitions on ground contact for dω/dt=0.
The example systems and methods also enable distinguishing between three modes of PST pressure sensing during locomotion based on feet touching the ground, namely, Two Feet, as an in stance on both feet (double limb support), and while extending a force moving to one foot, e.g., hitting a ball; One Foot, as a) the hitting impulsive action creates an unbalancing, reactive force, or b) when applying a pushing force, which is less impulsive in time, it creates a direction for continued force application, e.g., throwing a ball on one leg, or having contact with another large mass body; and Zero Feet, as in regaining balance on return to track of one or two feet that must dissipate or redistribute the angular momentum.
The example systems and methods also enable incorporating the modeling of locomotion, with the energy absorption and generation model, within the Action and Work efficiency metric under these three modes, whereby the transfer of angular momentum (ACM) changing Balance is correlated with the transfer of linear momentum (CM) changing Track such that these transfers use the PST identification time of maximum swing extension force (maximum centrifugal force), and these transfers use the PST identification time of the minimum stance foot-step force (trailing zero crossing from peak pressure).
The example systems and methods also enable periodic and aperiodic time boundary detection using HOS correlation on PST data.
The example systems and methods also enable combining the B&T and A&W computations in a PST sleeve localized manner, in order that the two paired PST parts can be reconstructed as a complete, correlative estimate (e.g., R-Thigh to L-Thigh, R-Calf to L-Calf, R-Thigh to R-Calf, L-Thigh to L-Calf, and further upper body limb intra-correlation pairing in a similar manner, inter-correlation pairing with lower body limbs, computations of symmetry, computations of efficiency, and computation of optimized locomotion for local visual, aural, or electrical stimulus feedback.
The example systems and method also enable combining multiple PST module measurements on the same limb sleeve to separate angular circumference contributions from local muscle pressure, as a further metric in muscle physiology for determining how the locomotion structures and effectors use energy as net cost of transport, defined as the energy needed to move a given Track distance, per unit body mass.
The example systems and methods also enable calibration of PST using a simple jump after attaching the sleeves to the limbs to start the system from a sleep mode, perform an alignment with the magnetic North and jump again, and then perform a 90° rotation to magnetic West, followed by the last jump before beginning movement. Here, the jump aligns the GRAV MEMS within all PST modules on all bands, and then the rotation does the same for the MAG MEMS, and finally the last jump is compared to the first in the PRES MEMS to calibrate all the sensors in relative location at three “step” in double support mode events, which are a signal to the processing to derive calibration parameters before processing data. These parameters are updated depending on the application, or stored and reused at the control of the user.
The example systems and method also enable integration in PST of force amplifier to FSR as a directly attached puck to resistive sensing material. This is used in combination with the built-in backing material of the sleeve and the buckle adjustment to achieve a comfortable and yet snug fit.
The example systems and methods also enable combining local PST PCB MEMSW gravitational measurements (G) and magnetometer (B) 3D vector measurements with pressure P, to estimate foot thrust force A, following the equations in the figures and the selected time constants for integration and lag defined by each.
The example systems and methods also enable combining B, G for paired thigh and calf PST sleeves to estimate a dynamic “Q-angle,” defined over a gait period from stance into swing back to stance separately for each leg, as the 3D MAG location of each limb, with motion corrections.
The PST described herein provides application specifics for the data processing algorithms as typical constants:
-
- 1) Sensor type and placement.
- 2) Information extraction details; algorithms, processing, time scales, spatial integrations, data flow parameters for change-detection algorithms.
- 3) Information display and database organization and analysis tailored for each application.
- 4) Computing of correlation in sensor dynamics with both second—order and fourth-order (in excess as a higher-ordered statistic, HOS). These are used in:
- Additional modeling of combined limb correlations used in algorithms defined by second order:
- i. calves/thighs, forearm/biceps,
- ii. shoulder-(collar-bone (clavicle), humerus (the upper arm bone), and scapula (shoulder blade), rotary cuff joint)
- iii. pelvis-(hip-bone (coxal-bone), sacroiliac-joint, sacral-sromontory; hip joint)
- Additional modeling of combined limb correlations used in algorithms defined by fourth order:
- i. upper-body/lower-body,
- ii. twisting-spine (ACM) foot-placement (CM)
- iii. joint centered motion in elbow/knee, shoulder/hips.
- Additional modeling of combined limb correlations used in algorithms defined by second order:
- 5) Gait applications in physical therapy, with the addition of the swing measurement and L-R leg correlation analysis from using PST metrics.
- 6) ACL injury in preventive medicine applications and training for predisposition to injury, using PST metrics.
- 7) Sports training and equipment development applications, using PST metrics.
- 8) Pre-/post-operative diagnosis, treatment, and recovery assessment, using PST metrics.
- 9) Mental impairment applications (stroke, Alzheimer's, brain damage), using PST metrics.
- 10) Elderly and aging applications (foot drop, feet stuck to floor, unbalance, and asymmetric gait), using PST metrics.
Thus, the technology described herein can provide enhanced sports performance coupled with injury avoidance as continuous, two-beat gait information from paired leg measurements. For example, as described herein, PST provides paired sensors in sleeves on lower body calves. Calf sensors with local feedback can, for example, coordinate a runner's stride to have an efficient pace and warn of potential weakening that could lead to injury. In addition, the technology described herein can provide for new measurements such as precise proprioception muscle action measurements. Such measurements can be used, for example, to evaluate central nervous system disorders.
The technology described herein can be widely utilized by professionals and amateurs in sports training and risk assessment including racing, jumping, hiking; team and individual sports including, but not limited to, basketball, baseball, lacrosse, hockey, soccer, tri-athletics, golf, tennis, football, and the like, at all levels (e.g., high school, college, professional, recreational) and including all styles (e.g., bike, skateboard, ski, run, swim, barefoot).
The technology described herein is also useful for practitioners in orthopedic ACL surgery, diagnosis, rehabilitation, and recovery, as well as researchers in locomotion and gait analysis, health care workers, medical elderly care professionals. The technology can also be used in central nervous system (CNS) diagnosis, monitoring, and treatment of conditions including, but not limited to, dementia, fall down, stroke, spine, lower back, Alzheimer's disease and the like.
In the past, locomotion analysis used photographs, force plates, shoe pads and video to collect data to review and understand how people move. This is technologically equivalent to filming a car's tires to determine where it is going. The technology described herein provides on-board internal locomotion sensing that is fast, inexpensive, provides real-time feedback, can be used outdoors, and measures both stance ground reaction force (GRF) and swing phase (free leg motion) without cameras.
With reference to
With reference to
The technology described herein uses a model to interpret the measurements and provide user feedback in real time. The model incorporates correlation analysis between paired limbs using an RF link (e.g., computation may occur in a watch or cell phone or other computer device). The technology can tie all data to a singular event in stride for each limb, being the SwRT (TSwR) “tick” to relate all stride data in an absolute time reference. The technology uses physically modeled Action and Work to estimate efficiency and incorporates estimates of upper body balance as the difference in summed 3D vectors for each leg in 3D gravity (G), to that of the estimated 3D leg thrust force (A) using a calibrated sleeve contraction/expansion as amplitude and the vector angle from a calibrated magnetic leg shank angle.
With reference to
As summarized in
With reference to
With reference to
With reference to
With reference to
With reference to
In
In
In
a. correlating left-right leg gait stance & swing phases
b. ANT+ to watch to display information
c. information provides real-time, graphic user feedback
d. corrections to run faster with less injury
e. health improvement in efficiency and symmetric balance
f. precursors to potential injury prevention
The systems and methods described herein are described in connection with certain non-limiting example embodiments. The following claims are not limited to these example embodiments, but on the contrary, are intended to cover various modifications and equivalent arrangements.
Claims
1. A system comprising:
- one or more sleeves, each configured for attachment to a leg and comprising a pressure sensor, an accelerometer and a magnetometer;
- a processor for processing sensor signals from the pressure sensor, the accelerometer and the magnetometer to estimate action (A) and work (W) using event detections of peak stance and valley swing events associated with leg movement, wherein the processor detects small changes during swing from brain feedback of proprioception sensing of the lower body component locations and movement dynamics to adjust the swing reversal behavior with feedback for a minimum off-unbalance and use of friction in stepping forward with each stance step.
Type: Application
Filed: Oct 22, 2018
Publication Date: Jun 27, 2019
Inventor: James C. SOLINSKY (Plymouth, MA)
Application Number: 16/167,483