BODY PART CONSISTENCY PATTERN GENERATION USING MOTION ANALYSIS

Disclosed embodiments describe techniques for body part biomarker and consistency pattern generation using motion analysis. Sensors are attached to a body part of an individual, where the sensors enable collection of motion data of the body part, and where the sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation. Data is collected from the sensors, where the sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol. Processors are used to analyze the electrical information from the sensors. Biomarker information for the individual is generated using the analyzing of the electrical information from the movement performance protocol. Additional data is collected from a subsequent attaching of sensors to the body part of the individual and the additional data is analyzed. Consistency pattern information is generated from the biomarkers.

FIELD OF ART

This application relates generally to motion analysis, and more particularly to body part consistency pattern generation using motion analysis.

BACKGROUND

For centuries, people have been fascinated by and argued about objects in motion. Whether the object in motion was an animal such as a human or other mammal transporting themselves by walking, trotting, or galloping; a bird or insect in flight; a tossed object or a fired projectile; or a weaving machine in operation, among many others, people have wanted to understand how those objects moved. The interest in movement likely originated from trying to understand physical phenomena, machine operation, or other movement that could not be detected easily if at all by the human eye. Did a galloping horse have all four hooves off the ground at some point while galloping? What was the number of rotations of a club that was being juggled? What was the wing movement pattern of a hummingbird while hovering? How did a projectile fired from a rifled barrel travel compared to one fired from a smooth barrel? These are just a few of any number of questions about movement that people have wanted to answer. The English photographer, Eadweard Muybridge, was an early developer and adopter of techniques for photography of objects in motion. His work, which included animals moving, horses galloping, men wrestling, and women dancing, showed details about motion that were typically not easily discernable by the naked eye. The work accurately depicted a horse galloping and demonstrated that racehorses did indeed have all four hooves off the ground at some point during their gallop.

Other techniques have been used to study motion. Time-lapse photography is a technique by which a series of photographs is taken over a period of time. The photographs are then strung together or sequenced, and played back at an increased speed. Time-lapse photography is used to show assembly progress along an assembly line, building construction, and other activities that involve movement and assembly of many components, or processes that take a long time to complete. The compression of the elapsed time into a short viewing time enables a person to view the assembly or construction processes in a compressed and logical manner. By contrast, high-speed photography can capture a process or event that occurs far too quickly to be seen by a human observer. A droplet of liquid falling into a pool of the liquid may be visible while falling, but details about the splash caused by the droplet, or the formation of the waves emanating from the droplet landing site, typically are not. Many photographs of the droplet as it falls and splashes into the can be captured within a short period of time. The sequence of many images can be shown over a longer period of time, enabling a viewer to see what actually occurred. The expansion of the short time event into a longer time frame enables the human observer to see details of the event that would be otherwise undetectable.

SUMMARY

The successful analysis of the motion of a body part is inextricably linked to the accurate measurement of the motion. The analysis of the motion can be used for diagnosing medical conditions or injuries, for measuring efficacy of medical treatment, or for enhancing athletic performance. Techniques for body part movement biomarker and consistency pattern generation using motion analysis are disclosed. At least two sensors, such as inertial measurement units (IMUs) or sensors for determining muscle activation, are applied to a body part of an individual. The electrical characteristics of a given sensor change as the sensor undergoes a change such as movement, stretching, compressing, etc. The electrical characteristics of an IMU change as the IMU accelerates, rotates, or changes position, and the electrical characteristics of the muscle activation sensor change as a muscle flexes or relaxes. The two or more sensors can also include stretch sensors that are based on an electroactive polymer. The electrical characteristics of an electroactive polymer change as the sensor is stretched.

The sensors are attachable to a body part. Tape, wrap, or a garment can be applied to the body part, and the sensors can be attached to the tape, wrap, or garment using hooks. The tape can be a specialized tape such as physical therapy tape, surgical tape, kinesiology therapeutic tape, and so on. One or more strips of tape can be attached to the body part. The one or more strips of tape can be attached in various configurations including a “T”, “W”, “X”, or “Y” configuration. The body part can include one or more of a knee, shoulder, elbow, wrist, hand, finger, thumb, ankle, foot, toe, hip, torso, spine, arm, leg, neck, jaw, head, or back. Data, including changes in electrical information based on microexpression of movement, is collected from the two or more sensors, where the changes in electrical information are caused by motion of the body part or muscle flexion. An at least third sensor can be applied to the body part to determine and analyze motion between symmetrical body parts. The collected electrical information is analyzed to generate consistency pattern information for the individual. The consistency pattern information is used for a clinical evaluation for an individual.

A processor-implemented method for motion analysis is disclosed comprising: attaching two or more sensors to a body part of an individual, wherein the two or more sensors enable collection of motion data of the body part, wherein the two or more sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation, and wherein the muscle activation comprises muscle deformation timing and muscle deformation displacement; collecting data from the two or more sensors, wherein the two or more sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol; analyzing, using one or more processors, the electrical information from the two or more sensors; and generating biomarker information for the individual, using the analyzing of the electrical information from the movement performance protocol. The generated biomarker information is used for a clinical evaluation of the individual where the clinical evaluation includes a degree of injury. The clinical evaluation is monitored over time to produce a healing trajectory. The healing trajectory is used to verify an extent of an injury. In embodiments, the two or more sensors comprise one or more integrated sensors, and the one or more integrated sensors comprise IMUs and muscle activation sensors. In embodiments, the two or more sensors comprise a network of sensors. In embodiments, the two or more sensors capture two or more modalities of body part motion.

DETAILED DESCRIPTION

Techniques for body part biomarker and consistency pattern generation using motion analysis are disclosed. Two or more wearable sensors can be attached to a body part of an individual. The wearable sensors comprise inertial measurement units (IMUs) for measuring acceleration, rotation, and position of a body part, and muscle activation sensors for determining muscle activation such as microexpressions of body part movement. The wearable sensors can be attached to a fabric which can be attached to a body part. The fabric can include tape, a woven fabric, a knitted fabric, a garment, a wrap, etc. The tape can be a specialized tape such as physical therapy tape, surgical tape, kinesiology therapeutic tape, and so on. The sensors can be used to measure various parameters relating to movement of the body part. The measurement of the body part can be used to perform symmetry evaluation; to evaluate a similar body part; to evaluate symmetrical operation of similar body parts; to perform microexpression movement evaluations; to evaluate angle, force and torque; etc. The body part can include one or more of a knee, shoulder, elbow, wrist, hand, finger, thumb, ankle, foot, toe, hip, torso, spine, arm, leg, neck, jaw, head, or back. The electrical characteristics of the IMU or the sensor for determining muscle activation sensor change as the IMU or the muscle activation sensor moves, stretches, or otherwise reacts to motion of the body part. In embodiments, a stretch sensor may also be used. The electrical information from the sensors can include changes in capacitance, resistance, impedance, inductance, etc. An electrical component coupled to the IMU or muscle activation sensor collects the changes in electrical characteristics produced by the IMU or muscle activation sensor. A communication unit can be coupled to the IMU or muscle activation sensor, and can provide electrical information from the IMU or sensor regarding the changes in electrical characteristics by the IMU or sensor. The electrical information collected from the IMU or muscle activation sensor is analyzed to generate biomarker information for the individual. The biomarker information is used for a clinical evaluation for the individual. The biomarker information is used to generate a consistency pattern.

Traditional IMU-based systems have attempted to infer the “absolute” location of a certain point of interest by integrating the acceleration reading in a 3D space. Thus, IMU systems have been used in various tracking applications such as tracking movement of a body part. However, the accuracy of such an approach is limited by the sampling rate of the IMU and the accuracy of the on-board accelerometer. One problem that is frequently encountered by all of these IMU-based solutions is referred to as drift. Drift is the error (herein location distance) between the actual location of an object versus the location that is calculated/observed by the IMU reading, The drift error results over time from the accumulative error, which is based on the calculation. The approach taken herein includes measuring microexpressions of movement based on determining muscle activation. This approach overcomes the accumulative error. Body movement, or 3D motion of a body part, such as a hand gesture, can be accurately represented in a 3D space over time.

Disclosed techniques address sensing of body part motion and motion analysis for generating body part biomarkers and consistency patterns. In embodiments, tape such as physical therapy tape, therapeutic kinesiology tape, surgical tape, etc. can be applied to a body part. In other embodiments, the body part can be wrapped, placed in a garment, etc. The body part can include one or more of a knee, shoulder, elbow, wrist, hand, finger, thumb, ankle, foot, toe, or hip, or some other body part such as a torso, spine, arm, leg, neck, jaw, head, or back. In embodiments, the tape can be applied to symmetrical body parts such as left shoulder and right shoulder, left hip and right hip, etc. Two or more stretch sensors can be applied to the tape that is applied to a body part. The attaching of the two or more sensors to the tape, wrap, garment, etc., can be accomplished using hooks, a hook and loop technique, fasteners, clips, bands, and so on. The two or more sensors that can be applied can provide electrical information which, when analyzed, can be used to generate biomarker information for an individual. The biomarker information can be used to generate a biomarker for the individual. The biomarker information can be augmented by collecting additional data from a subsequent attachment of an IMU and a muscle activation sensor to the body part. The resulting subsequent biomarker information can be analyzed to generate a consistency pattern. The consistency pattern can be compared to other consistency patterns in a library or analyzed as a change in the individual's biomarkers over time, either of which can enable a clinical evaluation of the individual.

Each muscular-skeletal (MSK) condition has a unique set of biomarkers. Disclosed techniques provide a detailed view of joint motion and muscle function, which can be used to improve accuracy in diagnosis of and treatment for many body-related conditions, such as orthopaedic conditions, degenerative body conditions, accident-related conditions, injury-related conditions, and so on. For example, a biomarker can describe a combination of joint motion with muscle function. This can include peak range of motion of a joint, quality of range of motion of a joint, peak muscle contraction measured in volumetric change, quality of muscle contraction, symmetry of muscle contraction timing in a bilateral moment, stability of motion during an isometric hold pattern, to name just a few.

Microexpressions of movement or motion include the measurable subtleties that a clinically trained eye would not be able to capture. Such microexpressions of motion include the quality of a muscle contraction, timing of activation of a muscle, as well as joint angle movement and velocities. One example of microexpressions can be seen during a test measuring isometric muscle endurance. In this test, an individual is asked to maintain an arm position with shoulders abducted at their sides without movement for up to two minutes. Often a person will shift the arm position to accomplish the task, and the person stops the activity by dropping her arms to her side due to a sense of fatigue. In the case of a motivated individual who wants to work through the sense of fatigue, the shift in arm position is often a subtle movement. The subtle movement can be a slight position change in any direction and is performed to allow other musculature to activate and reduce strain on the primary muscles originally engaged at the start of the task. The clinician observing this task often will not see the subtle change in arm position. This subtle motion can be the way a person completes the task of a timed hold of two minutes, but the person is unknowingly cheating the goal of the test by moving the limb slightly. The techniques disclosed herein enable detection of subtle microexpressions of motion. And in the example just discussed, they can identify both the time in the endurance test when the individual made the subtle position change as well as the magnitude and stability of the position change. Without the ability to capture microexpression of movement, the measurement of isometric endurance is not truly enabled.

Another example of a biomarker that originates from a microexpression is muscle quiver, which can be defined as the shaking of a muscle belly, often as a result of overuse, strain, or direct injury. The ability to capture and measure this subtle motion as a biomarker is a sophisticated digital diagnostic. Muscle quiver differences between an injured person and a non-injured person can be compared. The non-injured biomarker is generally smoother and devoid of rapid slope reversals on a best-fit line of sensor data, such as surface mechanomyography sensor data. The difference between the best-fit data line and the true output data line can be used to generate a numerical quiver score. Other examples of microexpression-based body part motion analysis exist, such as range of motion, symmetry of motion, flexion curve shape, etc.

The inertial measurement unit can include a six-axis or nine-axis IMU. The six-axis IMU can include a gyroscope for three axes, and an accelerometer for three axes. The nine-axis IMU can include a gyroscope for three axes, an accelerometer for three axes, and a magnetometer for an additional three axes. The addition of the magnetometer in the nine-axis IMU can improve accuracy. While the gyroscope and accelerometer can provide information about acceleration and rotation, their accuracy to measure location decreases over time due to drift. The information provided by the magnetometer can provide additional absolute direction sensing. The magnetometer measurements can be used to compensate for the drift over a time interval.

Techniques for motion analysis can be used for body part biomarker generation. The body part biomarker generation can include tracking symmetrical body parts as the body parts are moved based on a movement performance protocol. The movement of the body parts can be related to tracking body part motion, body part diagnosis, body part test, body part therapy, and so on. The body part biomarker generation can be based on acceleration and orientation information from the IMU. The acceleration and orientation information relating to a body part can be collected by a six-axis or a nine-axis inertial measurement unit (IMU). The six-axis IMU can include acceleration and rotation, and the nine-axis IMU can include acceleration, rotation, and absolute direction information. A muscle activation sensor can be used to detect activation of a given muscle. The muscle activation sensor can be based on detectable changes in electrical potential of a muscle. Because muscle activity can be detected and measured in the disclosed techniques, the process can be referred to surface mechanomyography. Depending on the desired biomarker, one or more movement performance protocols can be employed. The movement performance protocols can include both static poses, e.g., hold your arms out straight to the sides for two minutes, or active poses, e.g., do ten squats with your arms out in front of you.

FIG. 1is a flow diagram for body part biomarker generation using motion analysis. Two or more sensors are used to collect motion data from a body part of an individual. The motion data includes electrical information based on microexpression of movement of the body part. Analysis of the electrical information is used to generate biomarker information for the individual, using the analyzed electrical information. Additional data is collected from a subsequent attaching of the sensors to a body part. The additional data is analyzed to provide additional biomarker information. The additional biomarker information can include longitudinal consistency pattern information for the individual. The consistency pattern information can be used in the context of a clinical evaluation for the individual, a clinical treatment plan for the individual, and so on. Either or both a biomarker generated from the data analysis and an additional biomarker generated from the additional data analysis can comprise the consistency pattern information. Both a biomarker and a consistency pattern can be used for medical diagnosis, treatment of injury, athletic performance improvement, and so on. The flow100includes attaching two or more sensors to a body part110of an individual. The body part can include a muscle such as a biceps, triceps, or quadriceps muscle; or a joint such as a shoulder, elbow, wrist, hip, knee, or ankle. The attaching can be accomplished using hooks which can attach to tape, a wrap, a garment, etc. The tape can include physical therapy tape and kinesiology therapeutic tape, a woven material, etc. The two or more sensors enable collection of motion data112of the body part. The two or more sensors can be the same type of sensor or different types of sensors. In embodiments, the two or more sensors can include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation114. An IMU can include a variety of measurement components such as one or more of an accelerometer, a gyroscope, and a magnetometer. The IMU can measure acceleration, rotation, position, and so on. A sensor for determining muscle activation can measure electric potential for a given muscle and can output electrical information. The electrical information can be based on electrical characteristics of the sensor that can be changed based on muscle activation. The electrical characteristics that can be changed can include resistance, capacitance, inductance, etc. Other types of sensors can also be used, such as stretch sensors based on electroactive polymers.

The flow100includes collecting data from the two or more sensors120, where the two or more sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol. A movement performance protocol can include task performance such as sitting or standing; performing an operation such as reaching forward, up, down, or to the side; performing a squat; holding a pose; and the like. In embodiments, the electrical information that is provided by the sensors originates from detecting mechanical motion. The electrical information that is collected can be based on a DC signal, an AC signal, pulses, and so on. The electrical information can include values related to resistance, capacitance, inductance, etc. In embodiments, the electrical information that is provided can include surface mechanomyography information. The electrical information that is collected from the two or more sensors can be held or stored within the two or more sensors, transmitted to a receiving component, etc. Transmitting of electrical information can be accomplished using one or more wireless techniques including Wi-Fi, Bluetooth™, Zigbee™ near-field communication (NFC), and the like. Transmitting of the electrical information, when included, can provide continuous transmission, burst transmission, intermittent transmission, etc. The flow100can further include analyzing the task performance122to identify movement dysfunction. The movement dysfunction can include asymmetrical movement between symmetrical joints such as shoulders. In embodiments, the movement dysfunction that is identified can enable determination of typical injury impairment. Typical injury impairment can include reduced range of motion due to a wrist or ankle sprain, a neck strain, and the like. Typical injury impairment can further include tendonitis.

The flow100includes analyzing the electrical information from the two or more sensors130. The analyzing can include determining motion of a joint such as an ankle, knee, hip, wrist, elbow, or shoulder. The analyzing can be accomplished using one or more processors132. The analyzing can include determining a range of motion of a joint such as a knee or a shoulder. The analyzing can include comparing symmetrical joints. The comparing symmetrical joints can include comparing ranges of motion of a left knee and a right knee, the relative motions of a left shoulder and a right shoulder, and so on. The analysis can include determining an objective measurement of scapular movement. The analyzing can include kinetic phases. In embodiments, the one or more processors on which the analysis of the electrical information from the two or more sensors is performed can be coupled to the two or more sensors or can be performed beyond the two or more sensors. The one or more processors can include a smart device, a smartphone, PDA, laptop, a local server or blade server, a remote server, a mesh server, a cloud-based server, or a service such as computing as a service (CaaS), etc. The one or more processors can use machine learning134to identify electrical information, biomarker information, and consistency pattern information by training the machine using pre-identified examples of desired information. In embodiments, the electrical information from the two or more servers can be analyzed on a tablet. In other embodiments, the two or more sensors comprise one or more integrated sensors. For example, an integrated sensor may include a sensor to detect muscle activation and an IMU to detect muscle movement based on spatial positioning, acceleration, velocity, and so on. In embodiments, the one or more integrated sensors comprise stretch sensors. In further embodiments, the two or more sensors comprise a network of sensors. The network of sensors can be attached to a single body for multiple measurements of complex movement of that body or body part. The network of sensors can be attached to more than one body. For example, several bodies can have two or more sensors attached to the same body part on each body. Comparative muscle movement of each of the bodies is thereby enabled. In embodiments, the two or more sensors capture two or more modalities of body part motion. The modalities can include muscle activation, or contraction, and muscle movement in three dimensions. The modalities can be assessed using a stretch sensor and an IMU, although other sensors for capturing modalities are possible.

The flow100includes generating biomarker information140for the individual. The generating is accomplished using the analyzing of the electrical information from the movement performance protocol. Biomarker information can be used to create a biomarker, which is a digital representation of a body's state, and in this context, a body's state with respect to movement or motion. The flow100includes scoring mobility150of the individual, based on the biomarker information. Scoring mobility can be based on a value, a percentage, a range of values, a qualitative assessment, and so on. The scoring mobility can be used to categorize an injury, as described below. The flow100includes generating the consistency pattern information to identify movement pattern consistency160. The movement pattern consistency can be in reference to consistency of movement of a joint, consistency of movement between a pair of joints, and so on. The movement pattern consistency can be in reference to muscle activation such as a left quadriceps, consistency of muscle activations between left quadriceps and right quadriceps, and so on. A movement consistency pattern can be understood as a degree of replication of each piece or segment of a movement. In a usage example, a movement consistency pattern can be applied to left and right shoulder movement commensurate with a forward reach of left and right arms. Movement consistency patterns can be based on collecting and analyzing electrical data associated a movement performance protocol. The electrical data can be associated with movement timing, peak angular velocities, position of left and right shoulders, and so on. A consistency pattern can be generated with respect to an individual's own movement, with respect to an individual's own movement over time (longitudinal analysis), or with respect to a library of others' consistency patterns. Discussed below, the consistency pattern information can be used for a clinical evaluation for the individual. In embodiments, the movement pattern consistency can be used to detect an injury of the individual. Detection of an injury can be based on restricted motion, abnormal motion, lack of motion, and the like. In the flow100, the detection of an injury can further include categorizing the injury162of the individual based on the movement pattern consistency. The categorizing the injury can include determining a degree of injury such as mild or severe; recommending that the injury be treated by surgery or physical therapy; etc.

Various steps in the flow100may be changed in order, repeated, omitted, or the like without departing from the disclosed concepts. Various embodiments of the flow100can be included in a computer program product embodied in a non-transitory computer readable medium that includes code executable by one or more processors.

FIG. 2is a flow diagram for consistency pattern usage. Discussed throughout, consistency pattern information can be generated based on analyzing sensor information collected as an individual performs a movement protocol. The consistency pattern can be used to determine motion symmetry between joints such as shoulders of a human body. Motion analysis is used for body part consistency pattern generation. Sensors are attached to a body part of an individual, where the sensors enable collection of motion data of the body part. The sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation. Data is collected from the sensors, where the sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol. Processors are used to analyze the electrical information from the sensors, and biomarkers and/or consistency pattern information for the individual is generated.

The flow200includes collecting additional data210from a subsequent attaching of two or more sensors to the body part of the individual. Data can be collected over periods of time for a variety of purposes including gauging deterioration of a joint or joints, progression of a disease, recovery from an injury or surgery, and so on. The data that can be collected can include longitudinal data. The collecting additional data can be used to compare data collected from the individual with data collected from one or more other individuals. The additional data can comprise biomarker information and biomarkers. The flow200includes analyzing the additional data220to provide additional information. The additional information can include further sensor data based on microexpressions of movements of the body part. The analyzing can include analyzing further electrical information from the sensors. The electrical information can be based on a voltage or current; a frequency; a resistance, capacitance, or inductance; and the like. In the flow200, the analyzing additional data can provide additional consistency pattern information222.

The flow200further includes generating a consistency pattern230. The consistency pattern can be related to the first consistency pattern, can be a continuation of the first consistency pattern, can be separate from the first consistency pattern, and so on. In the flow200, the generating the consistency pattern uses the consistency pattern information232generated previously and the additional consistency pattern information. In the flow200, the generating the consistency pattern uses biomarker information234generated previously for the individual. The flow200includes using the consistency pattern for a clinical evaluation240for the individual. The clinical evaluation of the individual can include determining an extent of an injury, progress of recovery, efficacy of a rehabilitation protocol such as physical therapy, and so on. In embodiments, the clinical evaluation can enable determination of malingering. The determination of malingering can be used to evaluate whether pain reported by an individual is due to a physical injury or is psychosomatic. In other embodiments, the clinical evaluation can enable classification of a degree of an injury. The injury that can be classified can include injury to a limb, a muscle, a joint, and so on. In embodiments, the injury can include a neck strain. The classifying the injury can include determining an extent or degree of the injury. The classifying the injury such as neck strain can include a degree of mild or severe. Discussed throughout, the clinical evaluation can be used for further purposes. In embodiments, the clinical evaluation can be monitored over time to produce a healing trajectory. Other uses of the clinical evaluation can include comparing the healing trajectory of the individual to a library of healing trajectories. Various steps in the flow200may be changed in order, repeated, omitted, or the like without departing from the disclosed concepts. Various embodiments of the flow200can be included in a computer program product embodied in a non-transitory computer readable medium that includes code executable by one or more processors.

FIG. 3is an example system for motion evaluation. The motion evaluation can be based on a consistency pattern, on biomarkers, and on other criteria or parameters. A consistency pattern can be based on a consistency of movement of a given body part such as a joint, a limb, or a muscle; on symmetric movement of joints such as left and right shoulders; and so on. The consistency pattern can be used for a variety of purposes such as performing a clinical evaluation; determining a clinical treatment plan; evaluating a degree of injury, recovery from surgery, or the possibility of malingering; and so on. Markers such as biomarkers can include movement biomarkers which can be generated. The consistency pattern information, the movement biomarkers, etc., can be generated over a period of time. Motion analysis is used for body part consistency pattern generation. Sensors are attached to a body part of an individual, where the sensors enable collection of motion data of the body part, and where the motion data includes at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation. Data is collected from the sensors, where the sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol. Processors are used to analyze the electrical information from the two or more sensors, and consistency pattern information is generated. The consistency pattern information that is generated can be based on one or more biomarkers. Consistency pattern information for an individual is generated using the electrical information that was analyzed.

An example system for motion evaluation300is shown. The system can include two or more sensors, where the two or more sensors can be attached to a body part of an individual. The sensors can include an IMU sensor310, a muscle activation sensor312, another sensor314, and so on. While three sensors are shown, other numbers of sensors can be used. The body part can include a muscle of a torso such as a pectoral; a deltoid; a muscle of a limb such as a biceps, triceps, or quadriceps muscle; and so on. The body part can include a limb such as an arm or a leg. The body part can include a joint such as a shoulder, elbow, wrist, hip, knee, or ankle. The attaching of the sensors can be accomplished using hooks, where the hooks can attach to tape, a wrap, a garment, etc. The two or more sensors enable collection of data such as motion data of the body part. The sensors can be similar types of sensors or different types of sensors. The sensors can include sensors for determining muscle activation. The sensors for detecting muscle activation can include a load cell, a torque sensor, and the like. In embodiments, the sensor can include an electroactive polymer. An electroactive polymer can include a polymer that changes size or shape when activated by an electrical signal or field. The electrical characteristics of the electroactive polymer change based on stretching or deformation. The electrical characteristics that can change can include resistance, capacitance, inductance, etc. Other types of sensors can also be used. The sensors can include inertial measurement units (IMUs). An IMU can include one or more of an accelerometer, a gyroscope, and a magnetometer. The IMU can measure acceleration, rotation, position, and so on. The use of sensors further to the at least two sensors can confirm data collected from the first two sensors using a majority vote or other technique, can provide additional information, etc. An additional sensor can enable body part symmetry analysis such as range of motion, stretch, and so on.

The system300includes a motion evaluation component320. The motion evaluation component can include one or more processors. The one or more processors can include a smart device, a smartphone, PDA, a tablet, a laptop, a local server or blade server, a remote server, a mesh server, a cloud-based server or service such as computing as a service (CaaS), and the like. The motion evaluation component can include a collection component322. The collection component can collect data from sensors such as sensor310, sensor312, sensor314, and so on. The data that is collected can include electrical data, where the electrical data can include a voltage, a current, a frequency, an offset, a phase, etc. The data such as electrical data can be collected continuously, periodically, occasionally, and so on. The data can be stored within the motion evaluation component; within a device such as a tablet, smartphone, or PDA; on a local server or a remote server; and the like. The motion evaluation component can include an analysis component324. The analysis component can use the one or more processors to analyze the electrical information collected from the sensors. The analyzing can include determining motion of a muscle such as a deltoid, biceps, quadriceps, and so on. The analyzing can include determining motion of a joint such as an ankle, knee, hip, wrist, elbow, or shoulder. The analyzing can include determining a range of motion of a joint such as a knee or a shoulder. The analyzing can include comparing motion of symmetrical joints such as knees or shoulders. The analysis can include determining an objective measurement of scapular movement. The analyzing can include kinetic phases. In embodiments, analysis of the electrical information by the analysis component can be performed on a smart device, a smartphone, PDA, tablet, laptop, a local or remote server, a cloud-based server, etc. The motion evaluation component can include a generator component326. The generator component can generate biomarker and/or consistency pattern information for the individual by using the analyzed electrical information from the sensors. Discussed throughout, biomarker information can include a measurable index of body part movement ability, and consistency pattern information can include a measurable motion pattern associated with a body or body part. The biomarker or consistency pattern information can be used to indicate a biological condition or quality such as healthy or injured, or a biological state such as in motion or at rest, etc. The biomarker or consistency pattern information can be generated to determine a degree of injury, to form a recommendation or series of recommendations for treatment or therapy, to measure progress of healing, and so on.

The system300includes one or more components that can use one or more biomarkers or one or more consistency patterns for a variety of applications or purposes. In embodiments, the system300includes a clinical evaluator330. The clinical evaluator can use a consistency pattern within a clinical evaluation of an individual. The consistency pattern can be used to identify asymmetry within motion, range of motion, and so on, between symmetrical joints. The symmetrical joints can include ankles, knees, hips, wrists, elbows, or shoulders. In embodiments, consistency pattern information can be used to perform a clinical evaluation such as determining scapular dyskinesia. The system300includes an injury classifier332. The injury classifier can enable classification of a degree of an injury. The degree of injury can include mild, moderate, severe, catastrophic, etc. In embodiments, the injury that is classified can include a neck strain. In other embodiments, the system300includes a malingering determiner334. The malingering determiner can be used as part of a clinical evaluation, where the clinical evaluation determines that a patient is indeed not injured or perhaps only mildly injured. For example, the malingering can be a part of an exaggerated or falsified claim of whiplash, which is made evident through analysis of the consistency pattern evaluated during one or more prescribed movements of an individual making the claim.

In embodiments, the system300includes an evaluation monitor336. The evaluation monitor can be used to monitor a clinical evaluation over time. The monitoring of the clinical evaluation over time can be used to determine whether a particular therapy is effective, whether an injury is healing, and so on. The system300includes a healing trajectory comparator338. The healing trajectory comparator can be used to verify an extent of an injury, to determine whether healing from the injury or from surgery is progressing, and so on. The healing trajectory, which can be produced by monitoring a clinical evaluation over time, can be compared to a library of healing trajectories. The library of healing trajectories can include trajectories for normal healing, typical healing, etc. In embodiments, the healing trajectory can be used to verify injury extent. The system300includes a healthy pathology identifier340. A pathology can be healthy, unhealthy, injured, and so on. In embodiments, the clinical evaluation can be used to identify movement patterns that fall outside of healthy pathology. By identifying such movement patterns, recommendations to change, modify, improve, or otherwise alter movement patterns can be made. In further embodiments, clinical evaluation can be used to identify one or more neurological disorders. The identifying neurological disorders can be based on muscle movements that include tremors, bradykinesia, muscle rigidity, walking or running gait, limb movement while swimming or dancing, and so on. In embodiments, analyzing muscle movements can be used to enhance diagnosis of one or more diseases or traumas such as Parkinson's disease, myotonic dystrophy, stroke, and so on.

FIG. 4is a flow diagram for calculating a kinematic summation and distribution ratio. A kinematic summation and distribution ratio can be calculated based on the microexpression of movement of a body part. The kinematic summation and distribution ratio can be used for body part biomarker and/or consistency pattern generation using motion analysis. Two or more sensors are attached to a body part of an individual, where the sensors enable collection of motion data of the body part. The sensors can include an inertial measurement unit, a muscle activation sensor, and so on. Sensor data is collected, where the data includes electrical information based on a microexpression of movement of the body part. The electrical information is analyzed to generate biomarker and/or consistency pattern information. The consistency pattern information is used for a clinical evaluation of the individual.

The flow400includes calculating a kinematic summation and distribution ratio410based on the microexpression of movement of the body part. The kinematic summation and distribution can be calculated using one or more processors. The calculating can be used to determine information such as position, rotation, movement, acceleration, and so on, related to the body part. In embodiments, the calculating provides information on kinematic phases412. The kinematic phases can include phase patterns, where a kinematic phase pattern can refer to a cycle of movements of a body part, where such movements can be related to walking or running, raising and lowering arms, and so on. A phase can include a stance-phase. A stance-phase can include a double stance from walking. The double stance can include a heel strike, a mid-stance where the legs are vertical, a toe-off, and so on. The double stance can include a movement of a center of mass. In the case of walking, an individual's head will appear to rise at mid-stand and fall for heel-strike or toe-off. Another phase can include a swing-phase. In a swing-phase, a leg to be moved forward undergoes knee-flexion before being swung forward.

The flow400includes using the information on kinematic phases to build a kinematic phase library420. A kinematic phase library can be used to collect, aggregate, store, etc., kinematic phase patterns. The kinematic phase patterns can result from analysis of the electrical information collected from the two or more sensors attached to the body part of the individual. The kinematic phase library can include data collected from other individuals. The data collected from the other individuals can represent populations of individuals, where the populations can include normal or healthy populations, injured populations, and so on. The data contained in the kinematic phase library can be compared to data from the individual to determine an injury, to measure progress of strengthening or healing, etc. The flow400can include using the kinematic phase library to enable pattern recognition422for information on kinematic phases obtained from the calculating. The pattern recognition can be used to recognize a kinematic phase; to measure a kinematic phase; to compare a kinematic phase to a “standard”, a “normal”, or a previous measurement obtained from the individual; and the like.

The flow400further includes combining the kinematic summation and distribution ratio with one or more additional kinematic summation and distribution ratios430for a segment of a related body part. The segment of a related body part can include muscles or joints adjacent to the body part, such as an elbow or a wrist adjacent to a shoulder to which the two or more sensors were attached. The segment of a related body part can include a symmetrical body part such as left or right shoulder, left or right hip, and so on. In embodiments, the combining can describe a kinematic sequence432. A kinematic sequence can be used to describe a sequence of movements of a body part and how that sequence of movements can be used to transfer energy. A kinematic sequence can describe a jump or a launch from a starting stance for a sprint or an individual medley swimming event, a swing of a golf club or a tennis racquet, arm movement of a baseball pitcher, and so on. In embodiments, the combining can enable microexpression analysis434of the individual. The microexpression analysis can determine specific motions, paths of travel, velocity, acceleration, etc., related to the body part. The microexpression analysis can be applied to body part performance, therapies, and so on. In embodiments, the microexpression analysis of the individual can be used for athletic performance enhancement. The athletic performance enhancement can be used to optimize the swing of a golf club, tennis racquet, lacrosse stick, baseball or cricket bat, and the like. In further embodiments, the microexpression analysis of the individual can be used for medical treatment. The microexpression analysis can be used to design a rehabilitation therapy, to measure progress toward a rehabilitation goal, etc. In embodiments, microexpression analysis of the individual can be used for medical diagnostics. The diagnostics can include excessive motion of a joint, unbalanced movement of symmetrical joints, etc. In embodiments, the microexpression analysis can enable an objective measurement of scapular movement and dyskinesia. In some instances, the manner in which an individual moves a particular body part may increase a risk of injuring the body part. In embodiments, the microexpression analysis of the individual is used for injury risk analysis. An injury risk analysis can indicate that further tests, measurements, or diagnostics should be performed. In embodiments, the microexpression analysis of the individual is used for injury diagnostics.

Further to techniques that can calculate kinematic summation and a distribution ratio based on the microexpression of movement of a body part, other measurement techniques can be used. In embodiments, techniques based on electrical impedance myography can be explored. Electrical impedance myography (EIM) can be applied to a measurement of an intensity and a velocity of a muscle contraction event, where the measurement can include electrical impedance of the muscle or muscle group. EIM can be a noninvasive technique that can measure the electrical impedance characteristics. The electrical impedance characteristics can be used to determine health of a muscle or group of muscles, such as diagnosing a neuromuscular disease or other medical condition, assessing progression of the disease or condition, etc. The muscle health determination also can be useful for physical therapies, measuring the progress of healing, and so on.

The composition of a muscle or a group of muscles can be altered by the occurrence of disease, as can the microstructure of the muscle or group of muscles. By measuring changes in electrical impedance of the muscle or muscles using EIM, the occurrence of a disease such as a neuromuscular disease can be detected. The measurement of muscle impedance can be represented by a resistance-capacitance (RC) model, where the resistance component can be associated with cellular fluids within the muscles, and the reactance component can be associated with capacitive effects attributable to the cell membranes of the cells within the muscles. The cellular fluid can include extracellular fluid and intracellular fluid. The cell membranes can represent the capacitor dielectric separating the extracellular and intracellular fluids. Since disease can alter, sometimes significantly, the membranes of the cells, the cells can also undergo significant impedance changes. Thus, measuring the impedance of the muscle or muscle group over time can determine disease presence, disease progression, atrophy of muscle fibers, etc.

Impedance, such as electrical impedance associated with myography, is based on real components described as resistance, and imaginary components described as reactance. By applying a signal such as a sinusoidal signal to the surface of a muscle or muscle group, and by measuring the amount of time or time delay taken for the signal to pass through the muscle, a phase value can be calculated. By measuring resistance and reactance, and by calculating phase, a muscle disease may be identified. Electrical impedance myography can be impacted by physical characteristics of the patients for whom EIM is being performed. Physical characteristics of the patient can include thickness of the skin, the amount of fat under the skin (subcutaneous fat), and so on. By applying more than one sinusoidal test signal, where the sinusoidal test signals are based on different frequencies, the effects that skin and fat can have on impedance measurements can be reduced. Further, an amount of subcutaneous fat between the skin and the muscle may also be determined. In embodiments, at least one of the two or more sensors comprises an electromyogram sensor.

Other body part movement detection techniques include mechanomyography, which is sometimes called phonomyogram, acoustic myogram, or sound myogram. Mechanomyography relies on sensing muscle activation through resultant stimulation of adjacent media. For example, a muscle twitch can cause a movement of the skin above or near the muscle, which can then result in the movement of air molecules around the skin that can be detected as sound waves. Thus, in embodiments, at least one of the two or more sensors comprises a mechanomyogram sensor. Various steps in the flow400may be changed in order, repeated, omitted, or the like without departing from the disclosed concepts. Various embodiments of the flow400can be included in a computer program product embodied in a non-transitory computer readable medium that includes code executable by one or more processors.

FIG. 5shows sensor configuration. Discussed below and throughout, two or more sensors can be attached to a body part of an individual and can be used to collect motion data. The motion data can be analyzed for body part biomarker and/or consistency pattern generation. The sensors that can be attached can include stretch sensors, inertial measurement units (IMUs), sensors for detecting muscle activation, stretch sensors, and so on. An IMU can include one or more of an accelerometer, a gyroscope, and a magnetometer. Data is collected from the sensors, where the sensor data includes electrical information based on a microexpression of movement of the body part. Processors are used to analyze the electrical information in order to generate biomarker and/or consistency pattern information. The consistency pattern information can be augmented by analyzing additional data collected from the sensors. The augmented consistency pattern information can be used for a clinical evaluation for the individual.

A stretch sensor configuration500for attachment to a body part is shown. An IMU, a sensor for determining muscle activation, a stretch sensor, or other sensors can be used for body part biomarker information generation using motion analysis. The electrical characteristics of a sensor, such as an IMU, a muscle activation sensor, or a stretch sensor, change as the IMU or sensor moves, activates, or stretches, respectively. The electrical characteristics can include resistance, capacitance, inductance, reluctance, and so on. The muscle activations determined by the muscle activation sensor can correspond to movement of a body part to which the sensor is attached. Similarly, motion of an IMU can include acceleration, rotation, or position of the body part. A collector or sensor coupled to the muscle activation sensor collects changes in electrical characteristics based on motion which results from muscle activation of the body part. A communication unit provides information from the sensor or collector to a receiving unit. The stretch sensor configuration500can comprise an apparatus for attachment to tape on a body part. The sensor configuration can include an electrical component510. The electrical component510can be coupled to a stretch sensor512and can collect data relating to changes in electrical characteristics of the stretch sensor512. The electrical component510can include a power source that can provide power to electrical circuits and can drive the stretch sensor512. The power source and circuitry provide other signals such as sinusoids or pulses at various frequencies, AC or DC voltages, etc., that may be required to operate the sensor. The electrical component can include an electrical characteristic calculation component. The electrical characteristic calculation component can be used to determine stretch, bulge, displacement, and other physical characteristics based on body part motion. The electrical component can include a Bluetooth™, Wi-Fi, Zigbee, or some other communication unit which can be used to send collected changes in electrical characteristics of the stretch sensor. While one stretch sensor is shown, other numbers of stretch sensors can be included in a sensor configuration. As stated throughout, additional sensors can be based on IMUs. The electrical component can be coupled to a button520, switch, or other device for energizing or deenergizing the electrical component.

The stretch sensor512can include various materials which can be used to detect or measure stretch. In embodiments, the stretch sensor can include an electroactive polymer. The stretch sensors can be configured in a variety of arrangements such as a t-shape, an offset-t-shape, a w-shape, an x-shape, a spider-shape, and so on. The stretch sensor512can be coupled to an anchor514for the stretch sensor. The stretch sensor anchor can include hooks, and the anchor can be used to attach the stretch sensor to an anchor516and518. The anchors516and518can include tape, fabric, wrap, and so on. When tape is used, the tape can be attached to the body part where the first stretch sensor can then be attached to the tape. In embodiments, the tape can include physical therapy tape. In other embodiments, the tape can include kinesiology therapeutic tape.

In other embodiments, the sensor configuration500can include a bend sensor. One or more bend sensors can be applied to a body part of an individual and can be used for body part motion analysis. The one or more bend sensors can be used to measure motion of the body part with one or more degrees of freedom. Various techniques can be used to implement a bend sensor such as configuring the bend sensor based on a compliant capacitive strain sensor. A compliant capacitive strain sensor can comprise a dielectric layer sandwiched between two conducting electrode layers. The dielectric layer and the electrode layers can be based on flexible materials, where the flexible materials can include polymers. The flexible materials such as the polymers can include natural rubber, silicone, acrylic, and so on. Since the polymers can typically be insulators, the electrodes of the bend sensor can be formed by introducing conducting particles into the polymers, where the conducting particles can include nickel, carbon black, and the like. In order for the capacitive strain sensor to be applied to the body part, one or more compliant capacitive strain sensors or other strain sensors can be applied to a material such as tape that can be applied to the body part, a fabric that can enwrap the body part, a garment that can be worn on the body part, and so on. In embodiments, at least one of the two or more sensors comprises a bending sensor.

The compliant capacitive strain sensor can measure strain based on the amount of displacement experienced by the strain sensor. The ability of a compliant capacitive strain sensor to measure strain can be limited by the amount of displacement that can be sustained by the strain sensor before the strain sensor is temporarily or permanently damaged. Excessive strain applied to the strain sensor can cause electrical parameters of the strain sensor, such as the resistance of the strain sensor, to change significantly. The significant change in resistance of the strain sensor can include an “open circuit” (high resistance) resulting from a damaged or destroyed strain sensor.

An application of a sensor, such as the configuration shown, to a body part (e.g., a shoulder) can be used to determine angle measurements for the shoulder. In embodiments, angle measurements can include sagittal plane flexion and extension. In addition to angle measurements for a given body part, muscle function assessment can also be performed. In embodiments, muscle function assessment can include displacement of muscle contraction that can occur during an activity. The activity can include normal physical activity such as yoga and strenuous physical activity such as swimming, rowing, rock climbing, and so on. Peak displacement of a muscle can be based on maximum contraction of key superficial muscle groups. A sensor can be attached to a targeted muscle group, over the location of greatest muscle mass displacement. In addition to peak muscle displacement for muscle function determination, an amount of time required to reach peak muscle contraction can be recorded. Other sensors can be applied to shoulder measurements. In embodiments, the inertial measurement unit (IMU) can be used to track acceleration and orientation of a body part such as a shoulder. Based on measurements collected from the IMU, intersegmental movement can provide information on movement patterns across anatomical joints. The information based on the intersegmental movement provides information on a fluidity of movement and a quality of motion. This information can provide side to side comparison of movement of the anatomical joints for healthy populations in contrast with injured populations.

FIG. 6illustrates sensor placement and alternative sensor placement600. Sensors can be placed on a body part of an individual, where the sensors can include inertial measurement units (IMUs), sensors for determining muscle activation, stretch sensors, and so on. The sensors can enable collection of motion data of the body part. Data is collected from the sensors, where the data includes electrical information based on a microexpression of movement of the body part. The electrical information is analyzed to generate biomarker or consistency pattern information for the individual. The biomarker and/or consistency pattern information is used for a clinical evaluation of the individual. A placement610of sensors on an individual612is shown. The sensors, which can include two or more sensors, can be placed on a body part such as hips, knees, ankles, wrists, elbows, or shoulders, as shown. Further embodiments include attaching at least a third sensor to the body part. The third sensor can include an IMU, a muscle activation sensor, a stretch sensor, or some combination of sensors. In embodiments, the at least a third sensor can enable body part symmetry analysis. In the figure, three sensors are shown. A first sensor620can be attached to the left shoulder of the individual, a second sensor622can be attached to the right shoulder of the individual, and in embodiments, a third sensor624can be attached high and across the back of the individual. An alternative placement630of sensors is also shown, where the sensors are attached to an individual632. The sensors in this alternate configuration, as in the first configuration, can include two or more sensors. The two or more sensors can be placed on a body part, a joint, a limb, etc. In the alternative configuration, sensors are placed on the left scapula and the right scapula. A third sensor is further attached to the body part, where the third sensor can enable body part symmetry analysis. In the figure, three sensors are shown. A first sensor640can be attached to the left scapula of the individual, a second642can be attached to the right scapula of the individual, and in embodiments, a third sensor644can be attached high and across the back of the individual for symmetry and other measurements.

FIG. 7Ashows shoulder motion. Two or more sensors such as inertial measurement units (IMUs), muscle activation sensors, stretch sensors, etc. can be used for body part biomarker and/or consistency pattern generation using motion analysis. The sensors are attached to a body part of an individual. The sensors enable collection of data of the body part, where the data can include motion data, activation data, and the like. Sensor data is collected, where the sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol. Processors are used to analyze the electrical information to generate biomarker and/or consistency pattern information. Additional data can be subsequently collected and analyzed to provide additional information. The consistency pattern information and the additional consistency pattern information are used to generate further consistency pattern information. The consistency pattern is used for clinical evaluation for the individual.

FIG. 7Ashows an example of shoulder motion700. Two or more sensors such and IMUs and muscle activation sensors can be attached to left and right shoulders of a person710, where the person can be a surgery patient, an injury patient, a test subject, and so on. The sensors attached to the shoulders of the patient can be used to test for and quantify a severity and a location of a loss of joint stability. In the figure, the patient can raise712or lower714her left and right arms together as the motion of her left and right shoulders is measured. Embodiments include attaching at least a third sensor to the body part, where in this example, the body part is the shoulder region of the individual. The third sensor can be used to enable body part symmetry analysis. The body part symmetry analysis can be used to determine that two body parts, such as left shoulder and right shoulder, are moving properly. In embodiments, the at least third sensor enables an objective measurement of scapular movement.

FIG. 7Bshows data collected from shoulders702. The data can be collected from a single individual, a group of individuals, and so on. The data that can be collected can include data related to a clinical evaluation or diagnosis, a therapy, and the like. The collected data can include electrical information from an IMU, a sensor determining muscle activation, a stretch sensor, and so on. Plot750can show the flex return of the left scapula760and the flex return of the right scapula762. The flex return of the left scapula and the flex return of the right scapula can be measured while an individual is executing a movement, performing an action, etc. In embodiments, the movement can be based on a movement performance protocol. The plot750can include a time scale in seconds752and a displacement in millimeters754. The plot750shows that the percent displacement of the left scapula and the percent displacement of the right scapula differ. The difference in percent displacement can be associated with a surgery, an injury, and so on. The motion of the body part can be measured as the patient performs an action such as a forward reach activity, an overhead reach activity, a side-to-side reach activity, a backwards reach activity, and the like. Similar arm reaching activities can be performed with the patient holding one or more weights. A weight can be held in each hand, the weight can be shared between the left hand and the right hand, etc. The vertical line764can denote a particular point in time during the reach activity or other activity. For this example, less displacement of the scapula is preferred to more displacement. An increase in displacement of a scapula can indicate impairment, injury, damage, etc.

FIG. 7Cshows sensor position for data collection from the shoulders704. Wearable sensors can be used to collect data relating to a body part of an individual. The body part can include a muscle, a joint, a limb, etc. The data that is collected can include motion data, where the motion data can be analyzed and used for body part biomarker and/or consistency pattern generation. The data that is collected can include electrical information from the one or more sensors such as an IMU, a muscle activation sensor, a stretch sensor, or one or such as optical sensors. The electrical information can be based on a microexpression of movement of the body part during a movement performance protocol. A movement performance protocol can include a reach activity. Positionings of sensors for data collection from an individual for shoulder motion are shown. Sensors can be applied to body parts such as shoulders of the individual770. The sensors can be mounted as shown at the top of the left scapula772, on a torso centerline774, at the top of the right scapula776, and so on. In embodiments, the sensors can be mounted at other positions on the scapulae, at a different point of the torso centerline or spine, on other joints or body parts, and so on. The body parts or locations can include individual body parts such as an arm, shoulder, hip, knee, leg, etc. The sensors can include IMUs, muscle activity or activation sensors, linear displacement or stretch sensors, and so on. The body parts or locations can include symmetrical locations such as left and right shoulder, elbow, hip, or knee; left and right arm or leg; and the like. The sensors which can be mounted can include single-type sensors such as IMU, muscle activity, or linear displacement sensors; or can include combination sensors that can comprise two or more types of sensors. In embodiments, the “combination” sensors can include IMU and muscle activation sensors.

FIG. 8illustrates a plot of jump data800. Body part biomarker and/or consistency pattern generation uses motion analysis. Two or more sensors such as an inertial measurement unit (IMU) and a sensor determining muscle activation are attached to a body part, where the sensors can enable collection of motion data of a body part. Data is collected from the sensors, where the sensors provide electrical information. The electrical information is based on microexpression of body part movement during a movement performance protocol. The electrical information is analyzed to generate biomarker and/or consistency pattern information for the individual. A plot810of jump data is shown. The two or more sensors can be attached to joints such as a hips, knees, or ankles. The sensors can be attached to muscles such as quadriceps, calf (gastrocnemius and soleus) muscles, and so on. Data based on electrical information collected from stretch sensors or IMUs can be analyzed and plotted. The plot810can include a percentage stretch814versus time in seconds812. The plot for the jump can show jump takeoff820and jump landing822. The takeoff can correspond in time to a maximum compression of left832or right834quadriceps, and landing can similarly correspond in time to a maximum compression of left or right quadriceps. The plot for deflection of or force on left knee830or right knee can also be shown. Changes in electrical characteristics by a stretch sensor or an IMU can be rendered, along with an animation. The animation can include a human body, a body part of the human body, etc.

FIG. 9shows a block diagram for a kinematic phase pattern from muscle data. Sensors, including two or more sensors attached to a body part of an individual, enable collection of motion data of the body part. The collected body part motion data enables body part biomarker and/or consistency pattern generation using motion analysis. The sensors include an inertial measurement unit (IMU) and a muscle activation sensor. The sensors provide electrical information based on microexpression of movement of the body part during a movement performance protocol. The electrical information is analyzed using processors to generate biomarker information which can be used for a clinical evaluation for the individual. The sensors that provide the electrical information can include an accelerometer, a gyroscope, or a magnetometer, a sensor for determining muscle activation, a stretch sensor, etc.

The block diagram900includes a kinematic phase pattern910. A kinematic phase pattern can include one or more of angles, positions, accelerations, and velocities of segments of body parts and joints during the motions of the body parts and joints. A kinematic phase pattern can refer to a cycle of movements such as movements related to walking or running, raising and lowering arms, and so on. A phase can include a stance-phase. A stance-phase can include a double stance from walking. The double stance can include a heel strike, a mid-stance where the legs are vertical, a toe-off, and so on. The double stance can include a movement of a center of mass. In the case of walking, an individual's head will appear to rise at mid-stand and fall for heel-strike or toe-off. Another phase can include a swing-phase. In a swing-phase, a leg to be moved forward undergoes knee-flexion before being swung forward.

In order to form a kinematic phase pattern, various types of information can be collected. The types of information can be based on microexpressions of movement of the body part of the individual. In embodiments, the microexpression of movement of the body part includes muscle contraction amplitude920and muscle contraction timing922. The muscle contraction amplitude and the muscle contraction timing can be collected from one or more sensors such as stretch sensors. In other embodiments, the microexpression of movement of the body part can include linear movements and rotational movements. Linear movements can include raising or lowering arms or legs, while rotational movements can include clockwise or anticlockwise rotations of shoulders, elbows, wrists, hips, knees, or ankles. In embodiments, the linear movements and the rotational movements each can comprise velocity930, position932, and momentum934. The velocity, position, and momentum can be collected from one or more sensors such as inertial measurement units (IMUs). In embodiments, the velocity, position, and momentum each can include a magnitude and a time-dependent function. The velocity, position, and momentum of the body part change as the individual moves that body part. Various coordinate systems can be used to describe microexpressions of movement of the body part. In embodiments, the position can comprise a three-dimensional coordinate. Momentum can also be referenced using a variety of techniques. In embodiments, the momentum can include a center mass of a segment of a body part.

FIG. 10Ashows example lower body sensor locations1000. As discussed throughout, one or more wearable sensors can be used to collect data relating to a body part of an individual. The data that is collected can include electrical information from the one or more sensors. The body part can include a muscle, a joint, and so on. The collected electrical information can be analyzed to generate biomarker and/or consistency pattern information for an individual. The biomarker and/or consistency pattern information can be used for clinical evaluation for the individual. The lower human body1005to which sensors of various types can be mounted is shown. The sensors can include IMUs, muscle activity sensors, linear displacement or stretch sensors, and so on. The sensor can be mounted to the human body at various locations and for a variety of purposes. The body parts or locations can include individual body parts such as an arm, shoulder, hip, knee, leg, etc. The body parts or locations can include symmetric locations such as left and right shoulder, elbow, hip, or knee; left and right arm or leg; and the like. The sensors which can be mounted on the human body can include single-type sensors such as IMU, muscle activity, or linear displacement sensors; or can include combination sensors that can comprise two or more types of sensors. In embodiments, the “combination” sensors can include IMU and muscle activation sensors.

In embodiments, sensors can be applied to one leg or both legs. In the figure, sensors1030and1032are applied to the thigh of a leg, and sensor1020can be applied to the calf of the leg. Sensors1020,1030, and1032can be combination sensors that include both IMUs and muscle activation sensors. In addition, sensor1010can be applied to the top of the foot of the lower body1005. Sensor1010can be only an IMU sensor, which can provide baseline lower body positioning data and the like.

FIG. 10Bshows example upper body sensor locations1002. Discussed throughout, one or more wearable sensors can be used to collect data relating to a body part of an individual. The data that is collected can include electrical information from the one or more sensors such as an IMU, a muscle activation sensor, a stretch sensor, and so on. The electrical information can be based on a microexpression of movement of the body part during a movement performance protocol. The body part can include a muscle, a joint, a limb, etc. The collected electrical information can be analyzed to generate a body part biomarker and/or consistency pattern. The body part biomarker and/or consistency pattern can be generated using the analyzing of the electrical information from the movement performance protocol. An upper human body1070to which sensors of various types can be mounted is shown. The sensors can include IMUs, muscle activity or activation sensors, linear displacement or stretch sensors, and so on. The sensor can be mounted to the human body at various locations and for a variety of purposes. The body parts or locations can include individual body parts such as an arm, shoulder, hip, knee, leg, etc. The body parts or locations can include symmetrical locations such as left and right shoulder, elbow, hip, or knee; left and right arm or leg; and the like. The sensors which can be mounted on the human body can include single-type sensors such as IMU, muscle activity, or linear displacement sensors; or can include combination sensors that can comprise two or more types of sensors. In embodiments, the “combination” sensors can include IMU and muscle activation sensors.

In embodiments, sensors can be mounted at specific locations on the upper human body such as at the neck1060, at the small of the back1062, and so on. Sensor1060and sensor1062can include IMUs. Sensors1060,1062, and others (not shown) can be particularly useful for detecting, calculating, or determining body motions. Other sensors can be applied to various body parts. In embodiments, sensors can be applied to one arm or both arms. In the figure, sensor1040is applied to an upper arm, and sensor1042is applied to the lower arm. The arm sensors can include IMUs and/or muscle activity sensors. The arm sensors can be the same types of sensors or may include different types of sensors, such as IMU sensor1050on the back of a hand, which can be an IMU sensor used to determine baseline hand/arm movement and positioning. When sensors are applied to both arms, movement, muscle displacement, etc., can be compared between the arms. Such comparisons are useful for detecting imbalances or asymmetries between muscles of the limbs, evaluating recovery from injury, etc.

In other embodiments, not shown, other numbers of wearable sensors may be applied to the various body parts. The wearable sensors can include muscle activation sensors, skeletal movement sensors, linear displacement sensors, inertial measurement unit sensors, and so on. The sensors can be applied to body parts in order to measure muscle or joint activity, displacement, deformation, etc. A sensor can be applied to the knee, for example. The sensor can be used to measure motion of the knee, for example, and can record a number of degrees of flexion of the knee as the person to whom the sensor is applied engages in various activities such as standing, walking, running, bicycling, dancing, swimming, and so on. Other sensors may be applied to the leg. A sensor can be applied to a quadriceps muscle. A sensor can be applied to a calf. The data collected or obtained from the sensors may be aggregated. Sensors can be applied to one leg or both legs, one arm or both arms, one scapula or both scapulae, etc. When sensors are applied to both legs, or other limbs, for example, the data collected from the sensors of the left limb and the sensors of the right limb can be analyzed for symmetry such as body posture symmetry. The data obtained from the sensors may also be used to quantify differences in muscle activity, joint movement, etc. The quantified difference can be correlated with changes in footwear, protective gear, clothing, and so on.

FIG. 11illustrates neck range of motion differences. A normal range of motion of an individual's neck can be based on an individual being healthy, uninjured, physically fit, and so on. In contrast, the neck range of motion can be restricted, shaky, reduced, etc., due to injury, degeneration, surgery, and the like. The neck range of motion can be measured, tracked, and so on based on collecting data from sensors attached to the neck, shoulders, etc., of the individual. Measuring neck range of motions differences is enabled by body part biomarker and/or consistency pattern generation using motion analysis. Joint movement comparison enables body part biomarker and/or consistency pattern generation using motion analysis. Two or more sensors are attached to a body part of an individual, wherein the two or more sensors enable collection of motion data of the body part, wherein the two or more sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation, and wherein the muscle activation comprises muscle deformation timing and muscle deformation displacement. Data from the two or more sensors is collected, wherein the two or more sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol. The electrical information from the two or more sensors is analyzed using one or more processors. Biomarker information is generated for the individual, using the analyzing of the electrical information from the movement performance protocol.

Collected neck range of motion data is plotted for an injured neck1100and for a healthy neck1102. While a motion of neck is described here, the motion can include motion of another body joint such as a shoulder, elbow, wrist, hip, knee, or ankle. In embodiments, the injury can include neck strain. The motion could also refer to motion of a muscle or muscle group. The data can be collected from sensors attached to an individual, obtained from a library of collected data, and so on. The data can include data collected before and after an injury, pre-operation and post-operation, etc. The data can include an individual's data compared to data obtained from a library of data, and the like. The data can include data collected over a period of time and can be used to measure healing, efficacy of a treatment plan, and so on. The collected neck range of motion data can be used to generate a biological marker or “biomarker” such as a range of motion biomarker. Additional biomarkers can be generated based on collected additional data such as the data collected over a period of time. The biomarkers, the additional biomarkers, etc. can be used for clinical evaluation of the individual. The clinical evaluation can be used to determine an injury, an extent or degree of the injury, and so on. In embodiments, the evaluation can enable classification of a degree of an injury. The classification of the degree of the injury can include qualitative classifications such as “injured” and “healed”. The classification can include an extent of the injury such as “mild” and “severe”. In embodiments, machine learning is employed to analyze the biomarkers, additional biomarkers, consistency pattern information, and so on.

A plot of neck range of motion is shown for an injured neck1100. The plot can show a magnitude1110of motion versus time1112. Sensor data can be collected from attached sensors, analyzed, and plotted1114. The collected sensor data can be associated with a motion of the neck such as raising or lower a head (e.g., nodding “yes”), rotating the head to the left or right (e.g., nodding “no”), tilting the head to the left or right (e.g., tilting left ear toward left shoulder), and so on. The rising portion1116of the graph1114shows a rough or shaky transition from a starting point such as chin lowered to an end point such as chin raised. The transition can show that the individual experienced some pain during the transition, a restriction in the movement such as a “hitch”, an inability to attain a full or normal range of motion, etc. A period of time T0during which the motion takes place can be longer than for motion of a healthy or uninjured neck. A plot of neck range of motion for a healthy neck1102is shown. The plot of the range of motion of the healthy neck can be associated with the individual, can be obtained from a library, and so on. The plot of the healthy neck range of motion can be based on collected sensor data. The collected sensor data can include sensor data collected prior to an injury, subsequent to treatment of an injury or postoperative therapy, and so on. The sensor data can be based on electrical information associated with the sensors, and can be plotted1124based on a magnitude1120versus time1122. The rising portion1126of the graph1124shows a faster and smoother rise in comparison to the rise of the graph associated with the injured neck. Further, a period of time T1associated with the motion of the healthy neck is smaller in comparison to the time associated with the injured neck. The smoother motion and the shorter amount of time during which the motion takes place can indicate that an injury sustained by the individual has healed, that a treatment was successful, and so on.

FIG. 12is a system diagram for body part biomarker and/or consistency pattern generation using motion analysis. Motion analysis can describe motion of a body part including a joint such as a shoulder, elbow, hip, knee, ankle, wrist; a limb such as an arm or leg; and so on. The motion analysis can be used to determine extent of an injury, progress toward recovery, and the like. Sensors, including body-attachable sensors, can be used to analyze motion of a body part. Sensors can be applied to a body part of an individual, where the application can be accomplished using tape or straps using hooks, suction cups, wraps, and so on. The sensors can include inertial measurement units (IMUs), muscle activation sensors, stretch sensors, etc. IMUs can include an accelerometer, a gyroscope, a magnetometer, etc. Information can be collected from the IMU or muscle activation sensors, where the information can include changes in electrical characteristics of the IMU or muscle activation sensors. The electrical characteristics can change based on the sensor stretching, accelerating, moving, rotating, and the like. The electrical characteristics can include electrical information, where the electrical information can be based on microexpressions of movement of the body part during a movement performance protocol. Processors can be used to analyze the electrical information collected from the sensors. The collected electrical information can be used to generate biomarker and/or consistency pattern information for an individual. The consistency pattern information can be used to measure improvements, changes, or deterioration of movement of a muscle or joint; to identify movement patterns that fall outside of healthy pathology; to determine clinical evaluations and to recommend treatment plans; to determine malingering; etc. In embodiments, the generated consistency pattern can be rendered as an animation of the body part. The data relating to the generated consistency pattern for the body part can be used for body part treatment including medical techniques, physical therapy, occupational therapy, athletic training, strengthening, flexibility, endurance, conditioning, or rehabilitation therapy treatment.

The system1200can include an analysis computer1210. The analysis computer can include one or more processors used to analyze electrical information from two or more sensors. The analysis performed by the analysis computer can generate a body part biomarker information using motion analysis. The analysis computer1210can comprise one or more processors1212, a memory1214coupled to the one or more processors1212, and a display1216. The display1216can be configured and disposed to present collected data, analysis, intermediate analysis steps, instructions, algorithms, or heuristics, and so on. The system1200can comprise a computer system for motion analysis comprising: a memory which stores instructions; one or more processors coupled to the memory wherein the one or more processors, when executing the instructions which are stored, are configured to: attach two or more sensors to a body part of an individual, wherein the two or more sensors enable collection of motion data of the body part, wherein the two or more sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation, and wherein the muscle activation comprises muscle deformation timing and muscle deformation displacement; collect data from the two or more sensors, wherein the two or more sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol; analyze the electrical information from the two or more sensors; and generate biomarker information for the individual, using the analyzing of the electrical information from the movement performance protocol. The muscle deformation can include volumetric changes of the muscle.

The system1200can include an electronic component characteristics component1220. The electronic component characteristics can include a library of lookup tables, resistance characteristics, capacitance characteristics, functions, algorithms, routines, and so on, that can be used for the analysis of the electrical information collected from the one or more sensors. In a usage example, the electrical component characteristics can include a lookup table that enables mapping of an electrical signal from a muscle activation sensor to millimeters of motion of the body part. The system1200can include a collecting component1230. The collecting component can act as an interface between one or more sensors and the analysis computer1210. The collecting component can collect data based on electrical signals, where the electrical signals can be generated by an inertial measurement unit1232, a muscle activation sensor1234, a stretch sensor (not shown), etc. The collecting component can include resistance and/or capacitance measuring hardware, and can include hardware for measuring current, voltage, resistance, capacitance, impedance, and/or inductance.

The system1200can include an analysis component1240. The analysis component can analyze the data based on electrical information that is collected from various sensors such as inertial measurement units, muscle activation sensors, stretch sensors, and so on. The analysis can be performed on electrical signals related to acceleration, rotational motion, magnetic field, activation, stretch, and the like. The system1200can include a generation component1250. The generation component1250can include hardware for generating direct current and/or alternating current signals used for obtaining resistance and/or capacitance measurements. Typically, the current values are low (e.g., microamperes) and in embodiments, the frequency range includes signals from about 100 hertz to about 1 megahertz. The generating component can generate consistency pattern information for the individual. In embodiments, the consistency pattern can be used for a clinical evaluation for the individual, designing therapies such as physical therapy for recovery by the individual from injury or surgery, and the like.

The system1200can include a computer program product embodied in a non-transitory computer readable medium for motion analysis, the computer program product comprising code which causes one or more processors to perform operations of: attaching two or more sensors to a body part of an individual, wherein the two or more sensors enable collection of motion data of the body part, wherein the two or more sensors include at least one inertial measurement unit (IMU) and at least one sensor determining muscle activation, and wherein the muscle activation comprises muscle deformation timing and muscle deformation displacement; collecting data from the two or more sensors, wherein the two or more sensors provide electrical information based on a microexpression of movement of the body part during a movement performance protocol; analyzing, using one or more processors, the electrical information from the two or more sensors; and generating biomarker information for the individual, using the analyzing of the electrical information from the movement performance protocol.