System and method for enhanced anthropometric analysis of exercise effectiveness

Abstract

A method and system for enhanced anthropometric analysis of exercise effectiveness comprising: selecting a reference exercise item from a selection of predetermined reference exercise items, wherein the reference exercise items are based on a subject having a reference anthropometry; retrieving a predetermined set of rules associated with said selected reference exercise item; providing anthropometric user information; providing an image sequence having a plurality of frames of a user performing movements associated with the selected reference exercise item; analysing the image sequence to extract anthropometric user information; calculating a discrepancy between the reference subject anthropometry and the user anthropometry for body parts associated with the selected reference exercise item; calculating a difference in a load level based on the anthropometric discrepancy between the reference subject anthropometry and the user anthropometry; adjusting the predetermined set of rules associated with the selected reference exercise item to calibrate for the calculated difference in load level; measuring one or more angles of body parts and / or joints of the user performing the movements from the image sequence; analysing the measurements of the one or more angles of body parts and / or joints against the calibrated set of rules to assess an efficiency of the exercise performance.

Description

[0001]SYSTEM AND METHOD FOR ENHANCED ANTHROPOMETRIC ANALYSIS OF EXERCISE EFFECTIVENESS Field of the invention The present invention relates to a system and method for biometric analysis of a user executing an exercise, and more particularly to artificial intelligence-based analysis of said exercise to provide a numerical efficiency value associated with the user’s execution of said exercise. Background Exercise and physical fitness are recognized as essential components of a healthy lifestyle. A regular exercise routine can contribute significantly to improved physical and mental well-being, reduced risk of chronic diseases, and enhanced quality of life. Consequently, there is a growing global emphasis on promoting physical activity and fitness. Furthermore, human healthcare resources can be expensive and scarce. When they regularly have to serve a chronic disease-group, which makes up a large proportion of the population, treatment may become a socioeconomic problem. Musculoskeletal diseases like Osteoarthritis are a disease group that affects the highest number of people and at the greatest cost. Effective treatment for the patient-group for maintaining functional capacity and quality of life is to perform specially adapted physical exercises. However, the effectiveness and safety of exercise programs are critically dependent on the accurate performance of specific exercises and training techniques. The improper execution of exercises can lead to a range of problems, including suboptimal fitness results, increased risk of injury, and discouragement among individuals attempting to adopt a fitness regimen. In the field of musculoskeletal diseases, current treatment takes place under professional supervision which is very resource-intensive and costly. Many therefore do not receive the treatment they need. One fundamental challenge is that individuals, especially beginners, often lack access to qualified fitness trainers, physiotherapists or instructors who can provide guidance and correction during exercise sessions. Furthermore, even when fitness trainers are available, they may not be able to closely monitor the form and technique of the individual on a continuous basis. Incorrect exercise training can manifest in various ways, in particular, performing exercises with improper posture, body mechanics, or range of motion, leading to reduced effectiveness and an increased risk of injury. Other issues associated with incorrect exercising may be over-exertion, under-exertion, and inconsistent progress since the absence of a consistent and accurate assessment of performance can hinder individuals from tracking their progress effectively. Most exercise training aids rely on static, non-interactive resources, such as printed materials, and video tutorials. These methods lack the feedback and guidance necessary to ensure proper exercise form and technique throughout an entire workout. Personal training sessions are often too expensive for the required regularity for most people. Furthermore, current technical solutions which incorporate computer assessed exercise training lack the precision required to ensure injury-free training. The present invention provides a method and system to address the above- described problems with incorrect, injury-prone methods of training and / or performing exercise-based treatments. Furthermore, the invention seeks to provide enhanced computer-assisted training to ensure correct and optimal training thereby achieving a maximum potential from each training session. Yet further the present invention enables personalised exercise program generation and user-specific feedback of performance correctness and efficiency. Summary of the Invention According to a first aspect of the invention there is provided a method for enhanced anthropometric analysis of exercise effectiveness comprising: selecting a reference exercise item from a selection of predetermined reference exercise items, wherein the reference exercise items are based on a subject having a reference anthropometry; retrieving a predetermined set of rules associated with said selected reference exercise item; providing anthropometric user information; - providing an image sequence having a plurality of frames of a user performing movements associated with the selected reference exercise item; analyzing the image sequence to extract anthropometric user information; calculating a discrepancy between the reference subject anthropometry and the user anthropometry for body parts associated with the selected reference exercise item; calculating a difference in a load level based on the anthropometric discrepancy between the reference subject anthropometry and the user anthropometry; adjusting the predetermined set of rules associated with the selected reference exercise item to calibrate for the calculated difference in load level; measuring one or more angles of body parts and / or joints of the user performing the movements from the image sequence; analyzing the measurements of the one or more angles of body parts and / or joints against the calibrated set of rules to assess an efficiency of the exercise performance. Anthropometric user information may be provided by: analyzing the image sequence provided and extracting anthropometric user information therefrom; providing one or more separate images of the user and extracting anthropometric user information therefrom; or inputting anthropometric user information by a user. The reference exercise item may be a dynamically generated reference exercise item, comprising: recording a first optimal performance of an exercise by a user; analyzing the recording using human pose estimation to identify biomechanical key points for each relevant limb and joint for each video frame of the recording; selecting an optimal single repetition from the recording if the recording contains multiple repetitions; automatically selecting a frame having an optimal starting point and a frame having an optimal stopping point of the reference repetition, and extracting a full single repetition from the plurality of frames of the recording; performing further biomechanical analysis of the reference repetition to retrieve reference values for the various limbs and joints associated with the reference repetition. The further biomechanical analysis may retrieve reference values including start motion angle per joint, end motion angle per joint, start angle to horizontal plane, end angle to horizontal plane, start motion speed, and end motion speed. Image analysis of each frame in the image sequence may estimate an anthropometry of the user in their body position for said frame, wherein the estimated anthropometry comprises a plurality of estimated key points representing corresponding apexes of an angle of an associated joint. Calculating the difference in the load level based on the anthropometric discrepancy may comprise: measuring a moment arm length from a first line of gravity through a first associated key point to a second line of gravity through a second associated key point; multiplying the moment arm length by a total weight through the associated moment and by gravitational acceleration. Anthropometric information about the user may comprise a length of one or more body parts of a user and a user’s body proportions. Said predetermined set of rules may comprise at least one of: posture angles of associated key points; and a relationship a body part has to a horizontal plane at one or more positions of movements associated with the selected reference exercise item. The method may further comprise configuring a general benchmark exercise program by: refining the reference exercise to further identify predetermined segments to focus analysis on during analysis of the image sequence; setting threshold values for angles for the predetermined segments; setting a threshold frequency for one or more predetermined repetitions of the reference exercise; and setting anthropometric values for the various segments for the corresponding reference exercise. The method may further comprise converting the general benchmark exercise program into a user-specific benchmark exercise program by adjusting: the threshold values for angles for the predetermined segments; the threshold frequency for one or more predetermined repetitions of the reference exercise; and the anthropometric values for the various segments; wherein the user-specific benchmark exercise program may be used for a comparison analysis with the image sequence. The method may further comprise analysing the image sequence to determine a duration and / or frequency of each repetition and incorporating this analysis into the assessment of the efficiency of the exercise. A time of eccentric extension and a time of concentric extension may be measured separately. Analysing the image sequence to extract anthropometric information about the user may be performed using computer vision. The method may further comprise: assessing, on a frame-by-frame basis, whether an anthropometric deviation between an extracted anthropometry and a real anthropometry of a user is above a predetermined threshold; and correcting for said anthropometric deviation if it is determined that the anthropometric deviation is above the predetermined threshold. Correcting for said anthropometric deviation may comprise: discarding measurements from a corresponding frame in which the anthropometric deviation is above the predetermined threshold; extrapolating replacement measurements from a previous frame to the discarded frame and a subsequent frame from the discarded frame, provided the previous and subsequent frames have an anthropometric deviation below the predetermined threshold. No measurements may be calculated when: more than a threshold number of frames in the image sequence has an anthropometric deviation above the accepted predetermined deviation threshold; and / or more than a predetermined number of consecutive frames, from a discarded frame, have a deviation above the deviation threshold. The replacement measurements may be extrapolated from the previous and subsequent frames using a prediction filter. Correcting for said anthropometric deviation may comprise: identifying a key point generated from computer vision anthropometric analysis associated with an anthropometric deviation above the acceptable threshold; and replacing said computer vision generated key point with a corrected key point wherein the corrected key point is calculated using the anthropometric deviation. The method may further comprise: prioritizing angles which have a higher percentage change for the associated exercise over angles which have a lower percentage change when assessing anthropometric deviation. The method may further comprise: identifying estimated key points which have an anthropometric deviation below a predetermined threshold as acceptable key points; and using the acceptable key points and known anthropometric measurements to reconstruct a plurality of key points in a three-dimensional perspective using inverse kinematics. The method may further comprise augmenting feedback media indicative of exercise effectiveness using the enhanced anthropometric weighted measurements; and augmenting a feedback media indicative of the efficiency of the exercise. According to a second aspect of the invention, there is provided system for calculating an enhanced anthropometric weighted measurement of exercise effectiveness may comprise: a first memory module comprising a database with a plurality of reference exercise items stored thereupon; an image acquisition module comprising a camera for obtaining an image sequence of a user performing movements associated with a selected reference exercise item; an image processing module for analyzing the image sequence to extract anthropometric information about the user; a second storage module comprising computer readable media comprising instructions to perform the tasks of: calculating a discrepancy between a reference subject anthropometry and the user anthropometry for body parts associated with movement of the selected reference exercise item; calculating a difference in a load level based on the anthropometric difference between the reference subject and the user anthropometry; adjusting a predetermined set of rules for said selected reference exercise item to calibrate for the difference in load level; measuring one or more angles of body parts and / or joints of the user performing the selected exercise from the image sequence; analyzing measurements of the one or more angles of body parts and / or joints against the adjusted set of rules to assess an efficiency of the exercise performance; and a processor to execute the instructions stored in the second storage module. The computer readable media of the second storage module may further comprise instructions to perform the tasks of the first aspect of the invention. Each of the reference exercise items stored in the first memory module may comprise: exercise name, exercise description, exercise picture, exercise instruction video link, exercise voice link, exercise identification, joint or bone identification, primary or secondary identification per joint or bone, start motion angle per joint, end motion angle per joint, bone identification, start angle to horizontal plane, end angle to horizontal plane, start motion speed, end motion speed, and exercise coordinates. The system may be in the form of an application downloaded onto, or accessed by, a user’s personal computer device. The method of the first aspect, or the system of the second aspect, wherein the one or more angles of body parts and / or joints may be measured, and / or the resulting measurements may be analyzed, in real-time. Brief Description of the Drawings Fig.1 is a flowchart of the method of the invention herein; Fig.2a shows a first example exercise in the form of a weighted squat; Fig.2b shows a second example exercise in the form of a free weighted bicep curl; Fig.2c shows a measuring of load level based on the anthropometric difference between the reference exercise and analysis of anthropometry of the image sequence; Fig.2d shows a squat exercise used in a measuring of load level; Fig.3 shows a graphical representation of deviations between an actual length of a bone-segments and an estimated length; Fig.4 shows a visual representation of a method for correcting an anthropometric deviation; Fig.5 shows a flowchart of the method for generation of a dynamically generated reference exercise; Fig.6 shows generation of a dynamically generated reference exercise; Fig.7 shows a system according to the present invention. Figs.8a, 8b, and 8c is a series of screenshots of a user interface of the system corresponding to a plurality of frames from an image sequence of a first exercise; Figs.9a, 9b and 9c is a series of screenshots of a user interface of the system corresponding to a plurality of frames from an image sequence of a second exercise; and Fig.10 shows an example visual display of user performance. Definitions Unless otherwise defined, all terms of art, notations and other scientific terms or terminology used herein are intended to have the meanings commonly understood by those of skill in the art to which this invention pertains. In some cases, terms with commonly understood meanings are defined herein for clarity and / or for ready reference, and the inclusion of such definitions herein should not necessarily be construed to represent a substantial difference over what is generally understood in the art. In this text, the term ‘anthropometric’ refers to the scientific study of the measurements and proportions of the human body and involves the systematic measurement of various body dimensions, such as height, weight, length, girth, and breadth, as well as the analysis of their relationships and proportions. In this text, the term ‘biomechanics’ refers to the study of the mechanical principles that operate on our biological systems and includes the mechanical forces that are particularly linked to the musculoskeletal system. In this text, the term ‘exercise’ refers to a particular movement and / or position of a user’s body and / or posture, wherein said particular movement and / or posture is for the purpose of developing a strength and / or flexibility of the user’s body if the movement and / or posture is performed over time. In this text, the term ‘key point’ refers to specific anatomical landmarks or joints on the human body that are used in the analysis of body posture and movement. These key points are used to assess the angles and positions of various body parts during the execution of an exercise. They serve as reference points for measuring the effectiveness and correctness of the exercise by comparing the user's actual movements to the optimal movements defined in the reference exercise. Key points typically include joints such as the ankles, knees, hips, wrists, elbows, and shoulders, as well as other significant body parts like the head and torso. In this text, the term ‘posture angle’ refers to specific angles formed by the joints or body parts of a user while performing an exercise. Posture angles include particular joint angles and / or angles of a body part with respect to a horizontal plane. These angles are used to assess the correctness and effectiveness of the exercise. In this text, the term “exercise efficiency”, “exercise correctness” or “exercise effectiveness is the measure of synchronicity between a calibrated reference exercise item and movements of the users. Detailed Description The method and system of the present invention combine biomechanical research and human pose estimation with artificial intelligence-based analysis to analyse a correctness of a user’s exercise training, provide a numerical efficiency score on said correctness level, and provide instruction to improve said efficiency score. The technology identifies and compensates for any anthropometric deviations during the biomechanical analysis. The anthropometric measurements are used to set individual biomechanical threshold values for the user to achieve optimal effect from each individual exercise. Figure 1 is a flowchart of a method of the invention herein. The method starts at 102 wherein a reference exercise item is provided. In a first example of the invention, the reference exercise is selected from a plurality of reference exercise items. More particularly, the reference exercise item is retrieved from a database of reference exercise items. In a second example of the invention, the reference exercise is dynamically generated, as is further described below. An exercise is used in the context of this application to refer to a particular movement and / or position of a user’s body and / or posture, wherein said particular movement and / or posture is for the purpose of developing a strength and / or flexibility of the user’s body when the movement and / or posture is performed over time. The exercise may be a strength-based exercise, a flexibility-based exercise, a mobility-based exercise, a balance-based exercise or a stability-based exercise. Exercises that are for the purpose of physical therapy, occupational therapy, and even aerobic exercise are within the scope of the invention. Wherein the exercise is a strength-based exercise, the exercise may be a weighted exercise or a bodyweight exercise. Preferably, each reference exercise item comprises a particular exercise of repeated movement of a subject body and may or may not include the use of weights. To name a few non-limiting examples, the particular exercise of the reference exercise item may be: a squat, a push-up, a lunge, a crunch, a jumping jack, a burpee, a tricep dip, a leg raise, a high knee, a calf raise, a side leg raise, and a box jump. Each reference exercise item comprises researched-based information about an effect of said exercise on a subject’s body. In a first example, this information includes which movements constitute the most optimal muscle stimulation during an exercise, for example, which posture angles and movements constitute the most optimal muscle stimulation. Posture angles include particular joint angles and / or angles of a body part with respect to a horizontal plane. Information about a particular reference exercise may be stored in a look-up table and may comprise control parameters and threshold values corresponding to biomechanical threshold values. The look-up table may contain a row for each joint and bone segment included in the exercise. For each exercise, it may be specified / configured which bones / joints should be analysed in the exercise. This may be divided into primary and secondary bones / joints, wherein only one joint or bone is primary. The primary bone is used to measure whether the start / end of a movement has been reached. More specifically, information about a particular reference exercise stored in a look-up table may comprise: start and stop positions for an exercise; upper and lower positions for a primary joint; position of the other joints and / or bone segments at the exercise stop position. When calculating joints, the angle between two bone segments connected by the joint is calculated. When calculating a bone segment, the angle to the horizontal plane is calculated. For example, the starting position for a thigh bone in a squat will be 90 degrees, while the knee joint will be 180 degrees. The analysis of secondary joints may be performed in relation to the primary joint. This can be illustrated as follows: The optimal threshold value for the end of a movement, such as the lower position of a squat, is set to 90 degrees for the knee, which is chosen as the primary joint. The corresponding optimal value for the hip, as the secondary joint, is set to 45 degrees. When calculating the primary joint for each video frame, the calculation of the secondary joint is performed simultaneously. This approach can reveal errors during the movement, not just at the start and stop. First, the ratio of the angle range between the primary joint and the related secondary joint must be determined. In this case, both joints have a starting point of 180 degrees (standing) and an end point of 90 degrees for the knee and 45 degrees for the hip. This means that while the knee angle is reduced by 90 degrees, the hip angle is reduced by 135 degrees. Halfway through the movement, the knee joint angle will be reduced to 135 degrees, and the hip joint angle will be reduced to 112.5 degrees. This forms the basis for identifying any incorrect performance during the movement, where the movement in one joint or bone segment does not correspond with another. For the calculation of 50% performed movement, the knee at 100% is 90 degrees, and at 50% movement, it is 45 degrees, resulting in an angle of 135 degrees (180 – 45). For the hip, at 100% it is 135 degrees, and at 50% movement, it is 67.5 degrees, resulting in an angle of 112.5 degrees (180 – 67.5). In a first example, the ratio between all relevant joints is only calculated at the start and stop positions of an exercise repetition. In a second example, the ratio between all relevant joints is calculated for each video frame. To provide an illustrative example, a first reference exercise item relates to a squat with a bar weight as shown in Figure 2a. The reference exercise item comprises information about the optimum angle of the lower leg with respect to the thigh, said optimum angle having been identified through research of a series of potential angles and measuring their effectiveness on muscle and skeletal strain. The information may also comprise an optimum angle, or range of angles, for the subject’s back with respect to a horizontal plane. The horizontal plane is defined as 90 degrees from the directional force of gravity or parallel to a surface on which the subject is standing. Information may further include an optimal head position, an optimal position of the bar weight with respect to the subject, and an optimal angle of the subject’s forearm with respect to their upper arm. To provide a second illustrative example, a second reference exercise item relates to a bicep curl as shown in Figure 2b. The second reference exercise item comprises information about the optimum angle of the forearm with respect to the upper arm, said optimum angle having been identified through research of a series of potential angles and measuring a resultant effectiveness on muscle and skeletal strain. The information may also comprise an optimum angle, or range of angles, for the subject’s arm with respect to their torso. Information may further include an optimal head position, and an optimal position of the free weight in the subject’s hand. After providing the reference exercise item, the method then progresses to step 104, wherein the reference exercise is used to configure a general benchmark exercise program. Alternatively, a plurality of general benchmark exercise programs are pre- generated from the plurality of reference exercises and stored in accessible storage media such that a user can directly select a desired general benchmark exercise program. Each general benchmark exercise program comprises a series of specified (predetermined) key performance indicators for the associated exercise. These key performance indicators may include threshold posture angles and optimum frequency for associated exercise moments. The general benchmark exercise program may provide identification of the primary segments of a subject’s body involved in the reference exercise and further provide anthropometric values for a reference subject performing the associated reference exercise. Each general benchmark exercise program is derived from evidence-based research of the corresponding exercise. For example, this research includes measuring muscle strain across a range of posture angles to determine optimum posture angles of key segments involved in the exercise. Alternatively or additionally, benchmark values can also be based on physical laws. Preferably, each general benchmark exercise program has a default value of number of sets, number of repetitions, and a load percentage, for example three sets, 10 repetitions and a load percentage of 100%. These default values can be overridden directly for individual customisation, as will be explained further below. Having configured the general benchmark exercise program, the general benchmark exercise program is to be converted into a user-specific benchmark exercise program. In a first example, this is achieved by the user providing an image sequence of them performing an exercise corresponding to the reference exercise, step 106a. The image sequence can be provided as a pre-recording or may be a live video stream. Alternatively, the user may provide one or more still images of their body from one or more perspectives, step 106b. At step 108, the image sequence or one or more still images is analysed. More specifically, biomechanical analysis is performed to extract anthropometric measurements unique to the specifics of the user 110. The basis of the biomechanical analysis is known as human pose estimation (HPE). This is the technology that, based on computer vision (image analysis), calculates the length and angles of limbs and joints. These estimations, for the purpose of extracting anthropometric measurements to generate a user-specific benchmark exercise program, may be done in the first instance in two-dimensions. Human pose estimation for the purpose of extracting anthropometric measurements of the user according to the invention is a computer vision task that involves detecting and locating key points on the human body, such as joints and body parts, in images or videos. A spatial arrangement of a user's body is determined and a pose or posture of the user is identified. Deep learning models such as convolutional neural networks and / or other deep learning architectures are commonly used for this task. These models are trained on large datasets with annotated human poses to learn the spatial relationships between body parts. Key points which are identified on the human body may be joints - such as ankles, knees, hips, wrists, elbows, shoulders. Key points may also be body parts such as the head and the torso. Some non-limiting examples of pose estimation models, suitable for biomechanical analysis according to the invention, include OpenPose, PoseNet, and HRNet (High-Resolution Network). Some non-limiting example datasets for training and evaluating pose estimation models included Common Objects in Context (COCO), MPII Human Pose, and PoseTrack. Additionally or alternatively, at 106c, the user’s specific anthropometric measurements are inputted by the user. Preferably, the user’s specific anthropometric measurements are saved to a user account of an application for carrying out method 100. The user’s specific anthropometric measurements may contain the anthropometric values for all the vital bone segments for both the right and left sides of the body and the user’s torso. At 112, using the provided or extracted user-specific anthropometric measurements, a comparison between the extracted anthropometric measurements and the reference anthropometry of the reference subject is calculated. Preferably, the comparison is calculated as a percentage ratio. Thus, a more accurate calculation of forces that the particular reference exercise has on the user’s body as a result of the impact of the user’s particular anthropometry is measured. At step 114, the calculated comparison, for example in the form of the percentage ratio, is used to adjust the posture angles given in the reference exercise item and / or the general benchmark exercise program to provide a personalised set of posture angles accounting for the user’s particular anthropometry and to measure the real forces on various parts of the user’s body as a result of the exercise. In this way, the user-specific benchmark exercise program is generated. Figures 2c and 2d demonstrate a more accurate calculation of forces of a particular reference exercise on the user’s body as a result of the impact different length of the femur of the user, from the reference anthropometry has on the effect of the exercise. In particular, Figures 2c and 2d demonstrate the principles and key points for calculating forces of a squat exercise based on the impact different length of the femur of the user, from the reference anthropometry, has on the effect of the exercise. In Figure 2c, dotted lines 9, 10, 11 are perpendicular lines used as reference lines to calculate lines on which moment-arms lie. Line 9 represents a perpendicular line through the user’s hip 8, line 10 represents a line of gravity from a weight 15 used in the exercise, and line 11 represents a perpendicular line through the user’s knee 4. Moment arm 6 extends from perpendicular reference line 10 (i.e. the line of gravity from the free weight) to the hip 8. Moment arm 7 extends from perpendicular reference line 10 to the knee 4. More particularly, at the deepest position, the perpendicular line 9 is calculated for the lowest point (i.e., the user’s hip 8). Perpendicular line 10, showing the force of gravity, forms the basis for other calculations. At the deepest position, the perpendicular line 11 for the knee 4 is calculated. The bold solid lines 12 show a simplified skeleton for a squat as demonstrated in the image of Figure 2d. The dotted line 13 shows the equivalent simplified skeleton for a squat for a person with longer femurs. The dotted line 14 shows the posture of the person with long femurs when achieving the same torque as the person with short femurs, that is, when the hip joint aligns with the perpendicular line 9 of the reference person with shorter femurs. The below equations 1 to 3 show an example calculation of load on a hip joint for a reference subject (with shorter femurs) and a comparison with a user of the system (with longer femurs when compared with the reference subject). Equation 1 calculates a load on a hip joint during a squat exercise such as the squat exercise shown in Figure 2d. ^^^^^^^^^^^^ ^^^^^^ 6 ∗ ^^^^^^^^^^ ^^^^^^^^ℎ^^ ^^ℎ^^^^^^^^ℎ ^^^^^^^^^^^^ 15 ∗ ^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^ ൌ ^^^^Equation 1 Equation 2 provides a load on the hip joint in the specific example of Figure 2d wherein the moment arm 6 is 0.27m and the weight is 102kg. The example values of equation 2 represent example values for a reference exercise. 0.27 ^^ ∗ ^102 ^^^^ ∗ 9.8^ ൌ 270 ^^^^Equation 2 Equation 3 below provides a load on the hip joint of the real user having the same posture as the reference exercise. 0,33 ^^ ∗ ^102 ^^^^ ∗ 9,8^ ൌ 330 ^^^^Equation 3 The real user has a 5 cm longer femur which results, according to equation 3, in a 22% greater load on the hip joint. The above calculation of load assumes that the calf angle is the same so that only the length of the moment arm on the hip is increased. At step 116, each of the frames in an image sequence of the user performing the exercise are assessed against the user-specific benchmark exercise program, i.e., one or more preset parameters with respect to predetermined ranges of values as adjusted in step 114. The image sequence of the user performing the exercise is analysed using HPE biomechanical analysis to extract preset parameters. At least one of these preset parameters against which the image sequence is assessed is posture angle, wherein the measured posture angle of the user in the image sequence is compared to the anthropometrically adjusted posture angle value or range. More specifically, the one or more parameters against which the image sequence is assessed are the key performance indicators of the general benchmark exercise program and, thus, include threshold posture angles and optimum frequency for associated exercise moments. Assessment of the image sequence over a plurality of frames constitutes an assessment of a user’s execution of the exercise. In a preferred example, a three-dimensional HPE is used. This enables the user’s execution of the exercise to be assessed from different angles, for example, from a profile point of view and from a frontal plane point of view. Beneficially, this multi-view, multi-angle analysis feature of the invention can be achieved using only a single camera, such as a camera on the user’s phone. Assessing the execution of an exercise from different angles / views can be useful since one of the angles may reveal a posture angle error which appears correct in the primary view. The image sequence of the user performing the exercise may be the same as the image sequence provided in step 106a or may be a new image sequence. The method comprises a further step 115 of identifying and correcting for an anthropometric deviation. Anthropometric deviation corrections occur before or in parallel with step 116. In this step, at least one real anthropometric value of the user is known. This real, known anthropometric value is inputted by the user before step 115, and preferably on creation of a user profile of a user account of an application for carrying out method 100. An anthropometric deviation is measured by comparing deviation (inconsistency) between the at least one real anthropometric value with a corresponding extracted anthropometric value i.e., extracted during HPE of the image analysis step. When an anthropometric deviation is identified which is above a predetermined threshold, the method progresses to carry out steps to correct for this deviation. Figure 3 shows an illustrative example of anthropometric deviations between an actual length of bone-segments and an estimated length extracted during the image analysis step 108. The figure shows anthropometric deviations occurring during image analysis according to step 108 for a squat exercise. Each individual line represents a leg segment. In the example of Figure 3, a user’s left leg is focused on. This is because it is the most prominent, where the greatest motions occur and where the greatest deviation has been identified. The x-axis is the timeline measured in video frames, i.e., 30 frames per second (FPS). The y-axis is metric deviation between actual anthropometry and estimated anthropometry. Figure 3 shows the anthropometric deviation seems to increase proportionally with the change of angle. In the example of Figure 3, the posture angle is a knee angle. The anthropometric deviation for the femur, which has the greatest movement, varies from about -17 cm to about -27 cm. A difference between the highest and lowest deviation is approximately 10 cm, which, in this example, is identified as a significant deviation. For comparison, a difference between the highest and lowest deviation for the lower leg, for which there is less movement, the deviation fluctuates by only 2.5 cm. Figure 3 demonstrates that there can be a significant discrepancy between actual and estimated anthropometric measurements. For this particular exercise example of a squat, there is evidence of a pattern in which the deviation increases proportionally with motion, although this may not apply to other exercises and / or other bone segments. The method can compensate for anthropometry which is, to the greatest extent possible, independent of motions, camera position and a particular HPE-model. In a first method ALT.1 for correcting for anthropometric deviation, a frame (fn) in which the deviation is identified is discarded for the purpose of obtaining a measured posture angle of the user. This method is preferred when the anthropometric deviation is a result of a bone-segment of the user being estimated too short or too long compared to the known length of the particular bone segment, and thus a key point position is anomalous. A replacement posture angle will be calculated from extrapolating the key point position of both a previous and subsequent frame having an acceptable or no anthropometric deviation to provide a predicted replacement key point. This predicted replacement key point is then used in the calculation for the corresponding posture angle associated with that frame. The first method ALT.1 for correcting for anthropometric deviation has limitation criteria. A first limitation criteria in the first method ALT.1 is that a previous frame (i.e. fn-x) to be used in the prediction extrapolation is within a predetermined threshold number of previous frames away from the discarded frame (e.g. up to 3 frames away from the rejected frame, x=3). A second limitation criteria is the frame used for the prediction extrapolation must have an anthropometric deviation beneath a predetermined threshold. If the immediately adjacent previous frame (fn-1) also has an unacceptable anthropometric deviation, this cannot be used in the prediction estimation. In some examples, a next previous frame (fn-2) is used in the prediction estimation provided that frame has an acceptable anthropometric deviation. Thus, the method of ALT.1 includes reviewing the previous frames in sequence until a frame having an acceptable anthropometric deviation is identified and this frame is used in the prediction calculation, provided said identified frame having an acceptable anthropometric deviation is within the threshold number of previous frames away from the particular discarded frame (i.e. fn). In a similar manner, a subsequent frame (fn+x) to be used in the prediction extrapolation is a predetermined threshold number of frames (x) away from the particular discarded frame (fn). If no previous / subsequent frames having an acceptable anthropometric deviation are identified within the predetermined threshold number of frames (x), a posture angle is not calculated for that frame using the ALT.1 method. The method of ALT.1 is suitable wherein the deviation only applies to a few frames in the plurality of frames constituting the image sequence. In addition, the method of ALT.1 is particularly suitable when a quality of closely related frames is high. The quality of a frame is considered high, at least in part, if the anthropometric deviation is below a predetermined threshold. Preferably, the number of frames constituting an unacceptable number of frames having anthropometric deviation above the threshold deviation in the image sequence is between 5 and 20, more preferably less than ten consecutive frames. Since the method ALT.1 uses information from previous and subsequent frames (fn+x, fn+x), there is a time delay to the posture angle analysis. In an illustrative example wherein there is a four-frame margin and a frame rate of 30 frames per second, the time delay will be thirteen hundredths of a second. In a second method ALT.2 for correcting for anthropometric deviation, the frame is not discarded for the purposes of calculating posture angles. Instead, a position of one or more key points is recalculated. Preferably, each of the one or more key points is an apex of a corresponding posture angle associated with the selected exercise. Figure 4 shows a visual representation of the method of ALT.1 or ALT.2 for correcting for anthropometric deviation, wherein a key point is recalculated using alternative information. Skeleton lines 302, 304, 306 and 308 represent posture angles for a sequence of frames as the user moves to perform a squat exercise. The user moves downwards into the squat through four frames in the illustrative figure. The line 306 representing a posture angle between a user’s back and thigh in frame 37 of 41, has been identified as having an anthropometric deviation above an acceptable threshold. The solid line represents part of the measured posture angle before correction. Point A represents a key point of the measured anthropometry of the user in the second of the four frames, and is an apex of the posture angle between the user’s back and the user’s thigh. Point B represents a key point of the measured anthropometry of the user in the third of the four frames, and is the apex of the posture angle between the user’s back and the user’s thigh at a later time. Point D represents a key point of the measured anthropometry of the user in the fourth of the four frames, and is the apex of the posture angle between the user’s back and the user’s thigh at a still later time. Since the length of the user’s thigh has been incorrectly measured by the image analysis (HPE), key point B is in an incorrect position. Point C is the corrected key point recalculated using the methods of ALT.1 or ALT.2. Corrected point C can then be used to calculate the measured posture angle between the user’s back and the user’s thigh. This corrected measured posture angle is then the posture angle analysed against the adjusted reference and / or benchmark exercise for correctness, and to ultimately establish a numerical efficiency value. In a third method ALT.3 for correcting for anthropometric deviation each and every approved key point is identified and, the identified key points and known anthropometric measurements of the user are fed into an inverse kinematics model to render a three-dimensional reconstruction of key points of a user’s body. The inverse kinematic model can determine posture angles required to achieve the specific approved key point positions and orientations and, thus, derive a three-dimensional anthropometric model. As mentioned above, approved key points are those wherein an anthropometric deviation between an image analysis (HPE) measurement and a known body proportion is below a predetermined threshold. The deviation correction methods can be applied to both the initial anthropometric measurement analysis at the start of a user’s exercise and on a frame-by-frame basis for each re-adjusted position as the user moves through the exercise. Wherein the deviation correction is applied to the anthropometric model of each frame this is referred to as a deviation adjusted-extracted anthropometric model (DAEM). At step 116, the deviation adjusted-extracted anthropometric model (DAEM) for each of the frames is assessed against one or more preset parameters with respect to predetermined ranges of values. At least one of these preset parameters against which the DAEM is assessed is posture angle, wherein the measured posture angle of the user in the image sequence is compared to the anthropometrically adjusted posture angle value or range. Preferably, the one or more parameters against which the DAEM is assessed are the key performance indicators of the general benchmark exercise program and, thus, include threshold posture angles and optimum frequency for associated exercise moments. Assessment of the DAEM over a plurality of frames constitutes an assessment of a user’s execution of the exercise. In a preferred example, the three-dimensional anthropometric model is used. This enables the user’s execution of the exercise to be assessed from different angles, for example, from a profile point of view and from a frontal plane point of view. Beneficially, this multi-view, multi-angle analysis feature of the invention can be achieved using only a single camera, such as a camera on the user’s phone. Assessing the execution of an exercise from different angles / views can be useful since one of the angles may reveal a posture angle error which appears correct in the primary view. At step 118, the frame-by-frame analysis of the measured parameters against predetermined values is used to calculate an effectiveness score. This effectiveness score can then be transmitted to the user in the form of augmented feedback media. In addition to tracking the correlation between the reference / benchmark exercise and the user’s image sequence, the method and system of the invention can further automatically track valid repetitions. The tracking of repetition may comprise tracking a pace and consistency. Vertical movement velocity may provide a useful indication of relative effort. Thus, the relative effort estimate given by the user may be estimated by monitoring the pace. Based on measurements of velocity, the method may further include dynamically adapting the relevant general benchmark exercise program. The dynamic adaptation of the general benchmark exercise program can occur in real-time for optimal effect achievement. Alternatively, dynamic adaptation of the general benchmark exercise program can occur periodically. In a particular illustrative example, the pace is analysed to assess if the last repetitions take longer than the first. This may provide an indication of a good disposition of the exercise performed. In the squat exercise example, the average time for the first three lifts can be compared to the last three lifts in a set of a plurality of repetitions. If the average time for the last three repetitions is shorter than for the first three repetitions, this is recorded and optionally a visual or audible alert is given. By tracking the pace of the repetition in a set, unfortunate jerks can be uncovered during a user’s execution of an exercise. These jerks can be both ineffective and harmful. Ideally, a majority of exercises are carried out with smooth movements. The efficiency score (i.e. the enhanced anthropometric weighted measurement associated with exercise effectiveness) can then be based on a biomechanical analysis of a combination of posture angle and motion. Referring back to Figure 1, in a second example of the invention, the reference exercise is provided 102 by being dynamically generated. The flow chart in Figure 5 shows a method 200 for generation of the dynamically generated reference exercise. The method 200 starts at step 202 wherein a first optimal performance of the exercise is recorded by a user. In this case, the user may be the user who is the target of the exercise program, or the user may be a third-party user such as a physiotherapist, personal trainer or similar. At step 204, the recording is analysed using HPE for biomechanical key points for each relevant leg and joint for each video frame of the recording. The biomechanical data includes selecting which limbs and joints perform the most movement and should, thus, be included in the exercise. Preferably, it is possible to manually adapt the automatically selected limbs and joints by adding or removing limbs and joints. Figure 6 demonstrates recording of a first optimal performance of the exercise by a user and the analysis using HPE for biomechanical key points for each relevant bone and joint. If the recording of the first optimal performance of the exercise contains multiple repetitions, at step 206, an optimal single repetition is selected. Preferably, the user can override the choice of reference repetition by navigating to another repetition in the recording. At step 208, a frame having an optimal starting point of the reference repetition is automatically selected and a frame having an optimal stopping point of the reference repetition is automatically selected. A full single repetition is then cut out of the plurality of frames of the recording. At step 210, further biomechanical analysis of the reference repetition is retrieves reference values for the various legs and joints associated with the reference repetition. Optional further information can be added, step 212, to the dynamically generated reference exercise file. For example, the user may press a microphone button to add voice-based information; press a camera button to select a video frame to be used as the icon for the exercise; and / or press the note button to add written information. An example file for a reference exercise, whether dynamically generated or retrieved from a stock database contains general configuration information such as: threshold values for the various limbs and joints included in the exercise and exercise coordinates - angles for all limbs and joints for the exercise's movement for each video frame. For example, an exercise with 3 joints involved, recorded at 30 frames per second and lasting 3 seconds, will result in 270 rows in a corresponding look-up table for this reference exercise. Below is an example of data associated with a reference exercise file: Exercise name Exercise description Exercise picture Video frame as the image for the exercise Exercise Video recording of the reference exercise instruction video link Exercise voice link Recorded voice instructions Exercise identification Joint or bone Indicates whether the element is a bone or joint. identification Primary or Element is marked as a primary element secondary identification per joint or bone Start motion angle Specified only if it is a joint per joint End motion angle Specified only if it is a joint per joint Bone identification Specified only if it is a bone Start angle to This is the start angle of a bone relative to the horizontal horizontal plane plane End angle to This is the end angle of a bone relative to the horizontal horizontal plane plane Start motion speed This is the time for movement from start to end End motion speed This is the time for movement from end to start Exercise Exercise identification Coordinates Joint identification Sequence number Joint angle Dynamically generated reference exercises allow creation of exercises "on the fly." Large exercise libraries may make it difficult to find relevant exercises while not covering special needs that often arise. User-customised exercises can, therefore, be more motivating for a user with reduced functional ability since they are measured against their own optimal performance instead of that of an athlete. For example, a standard exercise (e.g. a squat) may be performed with lower resistance and / or with a narrow movement range. Figure 7 shows an example system 400 according to the invention for analysing a correlation between an image sequence of a user performing an action against an anthropometrically adjusted reference action. More particularly, the system 400 is for carrying out the above-described method 100. The system has a first memory storage module 402, an image acquisition module 404, an image processing module 406, a second memory storage module 408, a processor 410, and a user interface 412. In a first example, the first memory storage module 402 comprises a database with plurality of reference exercise items. In a special case, the reference exercise items are general benchmark exercise programs and include further key performance indicators as described above. Preferably, the first storage module 402 is non-volatile memory and may be ROM, HDD, SSD or cloud storage. The image acquisition module 404 comprises a camera and is configured to take an image sequence (video) of a user performing an action, for example a selected exercise. The acquired image sequence can be stored in a cache memory 405 for access by the image processing module 406 and the processor 410. The image processing module 406 is configured to retrieve the image sequence and perform an image analysis (HPE) to extract anthropometric information about the user, to track a correlation between the user’s action and a reference action, and to identify anthropometric deviations between measured anthropometry of the user and known anthropometry of the user. The processor 410 is configured to execute instructions stored in the second storage module 408 to perform the tasks of steps 112, 114, 116 of the above-described method 100, in combination with the image processor. The processor 410 is further configured to execute instructions stored in the second storage module 408 to perform the tasks of steps 118 and 120 of the method 100. Preferably, the second storage module 408 is non-volatile memory and may be ROM, HDD, SSD or cloud storage. The user interface 412 enables a user to control the system 400 including selecting a reference exercise item, starting an image acquisition, and commencing analysis. The user interface also provides the user with information. This information is preferably information about the correlation between the posture angles of the user’s performed exercise against optimal posture angles provided in the reference exercise. The information can be displayed as an overlaid rendering of the reference posture angles and / or real posture angles. It can include visual indicators of the amount of correlation such as colour coding, line flashing or differing line style. The information can also be visually displayed to the user via other symbology. For example, a numerical efficiency score can be provided on the user interface. The efficiency score can be provided in real-time or can be given at the end of an exercise, or at the end of every repetition of an exercise. In some examples, there is an efficiency score for a plurality of body parts. Figure 8 shows a series of screenshots of the user interface corresponding to a plurality of frames from an image sequence of a first exercise with overlaid visual enhanced biomechanical analysis. The graphs on the right-hand side show the human pose estimation from front on, left-side on and right-side on. Figure 8a to 8c is a user on a downward movement of a weighted squat exercise from standing. The human pose-based algorithm of the invention may perform, and feedback, the following calculations: hip angle, knee angle; Squat stage = Up; Hip feedback; Knee feedback; Valid squat counter; All squat counter; Hip efficiency sum; Average hip efficiency score; Knee efficiency sum; Average knee efficiency score; Average squat efficiency score. Figure 9 is a series of screenshots of the user interface of the system corresponding to a plurality of frames from an image sequence of the exercise of Figure 8 from the second viewpoint. Types of hip feedback include: Bend forward, Correct. Types of knee feedback: Get lower, Perfect height, Correct. Types of squat stage: Down (too high), Down (correct), Up Figure 10 shows an example visual display on a user interface of the user’s actual performance compared to optimal performance The information is displayed using a graphical representation, any deviations in both movement and speed are highlighted, not just for each repetition but for the entire set. In the example of Figure 10, a video of the performance of the exercise is uploaded and ready for playback. Checkboxes for selecting joints / legs to be analysed are displayed. Only checkboxes for joints and legs included in the exercise are shown. The user may select the joint or leg they want to analyse. In the example of Figure 10, the knee is selected. A graph for all repetitions in the set is displayed. The dotted curve shows optimal performance, whilst the solid curve shows the user's performance. When playing the video recording a vertical line may be synchronized with the video to show the connection between the diagram and the video recording. Alternatively or in addition to the display of visual information, feedback on the correctness of the execution of the exercise may be delivered audibly. This has the benefit that the user does not have to adjust their position to receive the information since adjusting their position may result in incorrect exercise execution. Thus, the augmented feedback media to convey exercise efficiency of the method and system herein can comprise overlaid rendering of the reference posture angles and / or real posture angles, visual indicators of the amount of correlation such as colour coding, line flashing or differing line style, other visual or audible symbology, and even physical indicia such as vibrations from a connected vibratable device, to name some non-limiting examples. Data processing, computation, and / or analysis, associated with the system and method herein described, may take place in the form of software as a service (SAAS) or Edge based processing. In an example, the system is implemented as an application on a user’s personal computing device such as their mobile phone or tablet. The application can request a permission to use the computing device hardware such as its camera and storage. In this way, no additional accessories are needed to realise the system or implement the method of the invention. In addition to showing the effect and results of single exercises, the method and system of the invention can provide the user with tracking their development over time. Data derived from the method analysis of each exercise session can be saved and statistical analysis performed thereon. In this way, an improvement on a particular exercise and / or posture correctness with respect to a particular body part or joint can be tracked over time. The present invention combines evidence-based research which gives rise to the specialised general benchmark exercise programs combined with artificial intelligence and biomechanics-based analysis provides a method and system for carrying out correct treatment safely without expert supervision. A particular benefit of the method and system of the invention is that treatment can be performed in a patient’s / user's own home, without the presence of a healthcare professional. A user only needs the technical competency level of standard use of a personal computing device, installing applications and taking a video recording. The system and method uncover exercises that are considered ineffective or directly harmful if performed incorrectly. In the event of deviations or incorrectly performed exercises, information to this effect is communicated to the user through the user interface or otherwise. Instructions for correction can be communicated to the user. A communication connection with a user profile of the system can be established with a healthcare professional such as a physiotherapist for professional overview and assessment. In this way computational analysis, artificial intelligence and human intervention can be combined to provide improved treatment. In a particular example, the communication link with a healthcare professional is only established when the efficiency score dips below a predetermined threshold. Alternatively or additionally, an image sequence associated with an executed exercise wherein a predetermined threshold efficiency score was not met can be stored in memory and marked to indicate that said image sequence should be evaluated by the healthcare professional. The healthcare professional can then access the saved file for review retrospectively and review several exercises / exercise sessions in one go. The healthcare provider or personal trainer can review the feedback provided by the computational analysis of the method and review the image sequence and provide their own feedback. They can add notes to the associated exercise. The system and method of the invention provide a more efficient, economic, safe and diverse treatment to a larger proportion of a user group. It is particularly suited to the field of physiotherapy treatment and workout training. Having described preferred examples of the invention it will be apparent to those skilled in the art that other embodiments incorporating the invention may be used. These and other examples of the invention illustrated above are intended by way of example only and the actual scope of the invention is to be determined from the appended claims.

Claims

AMENDED CLAIMS received by the International Bureau on 10 August 2025 (10.08.2025)1. A method for enhanced anthropometric analysis of exercise effectiveness comprising: selecting a reference exercise item from a selection of predetermined reference exercise items, wherein the reference exercise items are based on a subject having a reference anthropometry; retrieving a predetermined set of rules associated with said selected reference exercise item; optionally, providing anthropometric user information comprising a user’s specific anthropometric measurements; providing an image sequence having a plurality of frames of the user performing movements associated with the selected reference exercise item; analysing the image sequence to extract anthropometric user information; calculating a discrepancy between the reference subject anthropometry and the user anthropometry, using the provided and / or extracted anthropometric user information, for body parts associated with the selected reference exercise item; calculating a difference in a load level based on the anthropometric discrepancy between the reference subject anthropometry and the user anthropometry; adjusting the predetermined set of rules associated with the selected reference exercise item to calibrate for the calculated difference in load level; measuring one or more angles of body parts and / or joints of the user performing the movements from the image sequence; analysing the measurements of the one or more angles of body parts and / or joints against the calibrated set of rules to assess an efficiency of the exercise performance.

2. The method of claim 1 , further comprising providing anthropometric user information by: providing one or more separate images of the user and extracting anthropometric user information therefrom; or inputting anthropometric user information by a user.

3. The method of claim 1 or claim 2, wherein the reference exercise item is a dynamically generated reference exercise item, comprising: recording a first optimal performance of an exercise by a user; analysing the recording using human pose estimation to identify biomechanical key points for each relevant limb and joint for each video frame of the recording; selecting an optimal single repetition from the recording if the recording contains multiple repetitions; automatically selecting a frame having an optimal starting point and a frame having an optimal stopping point of the reference repetition, and extracting a full single repetition from the plurality of frames of the recording; performing further biomechanical analysis of the reference repetition to retrieve reference values for the various limbs and joints associated with the reference repetition.

4. The method of claim 3, wherein the further biomechanical analysis retrieves reference values including start motion angle per joint, end motion angle per joint, start angle to horizontal plane, end angle to horizontal plane, start motion speed, and end motion speed.

5. The method of any of claims 1 to 4, wherein image analysis of each frame in the image sequence estimates an anthropometry of the user in their body position for said frame, wherein the estimated anthropometry comprises a plurality of estimated key points representing corresponding apexes of an angle of an associated joint.

6. The method of claim 5, wherein calculating the difference in the load level based on the anthropometric discrepancy comprises: measuring a moment arm length from a first line of gravity through a first associated key point to a second line of gravity through a second associated key point;multiplying the moment arm length by a total weight through the associated moment and by gravitational acceleration.

7. The method of any preceding claim, wherein anthropometric information about the user comprises a length of one or more body parts of a user and a user’s body proportions.

8. The method of any preceding claim, wherein said predetermined set of rules comprises at least one of: posture angles of associated key points; and a relationship a body part has to a horizontal plane at one or more positions of movements associated with the selected reference exercise item.

9. The method of any of claims 5 to 8, further comprising configuring a general benchmark exercise program by: refining the reference exercise to further identify predetermined segments to focus analysis on during analysis of the image sequence; setting threshold values for angles for the predetermined segments; setting a threshold frequency for one or more predetermined repetitions of the reference exercise; and setting anthropometric values for the various segments for the corresponding reference exercise.

10. The method of claim 9, further comprising converting the general benchmark exercise program into a user-specific benchmark exercise program by adjusting: the threshold values for angles for the predetermined segments; the threshold frequency for one or more predetermined repetitions of the reference exercise; and the anthropometric values for the various segments; wherein the user-specific benchmark exercise program is used for a comparison analysis with the image sequence.

11. The method of any preceding claim, further comprising analysing the image sequence to determine a duration and / or frequency of each repetition and incorporating this analysis into the assessment of the efficiency of the exercise.

12. The method of claim 11 , wherein a time of eccentric extension and a time of concentric extension is measured separately.

13. The method of any of claims 5 to 12, wherein analysing the image sequence to extract anthropometric information about the user is performed using computer vision.

14. The method of any preceding claim, further comprising: assessing, on a frame-by-frame basis, whether an anthropometric deviation between an extracted anthropometry and a real anthropometry of a user is above a predetermined threshold; and correcting for said anthropometric deviation if it is determined that the anthropometric deviation is above the predetermined threshold.

15. The method of claim 14, wherein correcting for said anthropometric deviation comprises: discarding measurements from a corresponding frame in which the anthropometric deviation is above the predetermined threshold; and extrapolating replacement measurements from a previous frame to the discarded frame and a subsequent frame from the discarded frame, provided the previous and subsequent frames have an anthropometric deviation below the predetermined threshold.

16. The method of claim 15, wherein no measurements are calculated when: more than a threshold number of frames in the image sequence has an anthropometric deviation above the accepted predetermined deviation threshold; and / ormore than a predetermined number of consecutive frames, from a discarded frame, have a deviation above the deviation threshold.

17. The method of claim 15 or 16, wherein the replacement measurements are extrapolated from the previous and subsequent frames using a prediction filter.

18. The method of claim 14 when dependent on claim 13, wherein correcting for said anthropometric deviation comprises: identifying a key point generated from computer vision anthropometric analysis associated with an anthropometric deviation above the acceptable threshold; and replacing said computer vision generated key point with a corrected key point wherein the corrected key point is calculated using the anthropometric deviation.

19. The method of any of claims 14 to 18, further comprising: prioritising angles which have a higher percentage change for the associated exercise over angles which have a lower percentage change when assessing anthropometric deviation.

20. The method of claim 14, further comprising: identifying estimated key points which have an anthropometric deviation below a predetermined threshold as acceptable key points; and using the acceptable key points and known anthropometric measurements to reconstruct a plurality of key points in a three-dimensional perspective using inverse kinematics.

21. The method of any of claims 1 to 20, further for augmenting feedback media indicative of exercise effectiveness using the enhanced anthropometric weighted measurement, comprising the steps of any of claims 1 to 20; and augmenting a feedback media indicative of the efficiency of the exercise.

22. A system for calculating an enhanced anthropometric weighted measurement of exercise effectiveness comprising: a first memory module comprising a database with a plurality of reference exercise items stored thereupon; an image acquisition module comprising a camera for obtaining an image sequence of a user performing movements associated with a selected reference exercise item; an image processing module for analysing the image sequence to extract anthropometric information about the user; a second storage module comprising computer readable media comprising instructions to perform the tasks of: calculating a discrepancy between a reference subject anthropometry and the user anthropometry for body parts associated with movement of the selected reference exercise item; calculating a difference in a load level based on the anthropometric difference between the reference subject and the user anthropometry; adjusting a predetermined set of rules for said selected reference exercise item to calibrate for the difference in load level; measuring one or more angles of body parts and / or joints of the user performing the selected exercise from the image sequence; analysing measurements of the one or more angles of body parts and / or joints against the adjusted set of rules to assess an efficiency of the exercise performance; and a processor to execute the instructions stored in the second storage module.

23. The system of claim 22, wherein the computer readable media of the second storage module further comprises instructions to perform the tasks of: any of claims 2 to 21.

24. The system of claim 22 or 23, wherein each of the reference exercise items stored in the first memory module comprises: exercise name, exercise description, exercise picture, exercise instruction video link, exercise voice link, exerciseidentification, joint or bone identification, primary or secondary identification per joint or bone, start motion angle per joint, end motion angle per joint, bone identification, start angle to horizontal plane, end angle to horizontal plane, start motion speed, end motion speed, and exercise coordinates25. The system of any of claims 22 to 24, wherein the system is in the form of an application downloaded onto, or accessed by, a user’s personal computer device.

26. The method of any of claims 1 to 21 , or the system of claims 22 to 25, wherein the one or more angles of body parts and / or joints are measured, and / or the resulting measurements are analysed, in real-time.

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