A system for joint stability assessment and method thereof

Abstract

A wearable system (100) and a method of operation of the said system for assessing stability of a skeletal joint is disclosed herein. The wearable system (100) comprises at least one multi-axis miniature inertial measurement unit (10) in a singular or pair format; a controller unit (20) communicatively coupled with a multi-axis miniature inertial measurement unit (10) in a singular or pair format; a memory module (30) for storing the processed data from the controller unit (20); a rechargeable battery pack (50) provided with a power regulating module (60) and an internal protection circuit.

Description

[0001]

[0002] “A SYSTEM FOR JOINT STABILITY ASSESSMENT AND METHOD THEREOF”

[0003] FIELD OF THE INVENTION:

[0004]

[0001] The present invention relates to assessing stability of a skeletal joint. More particularly, the present invention relates to a wearable system and method for significantly enhancing clinicians' ability to monitor skeletal joint stability.

[0005] BACKGROUND OF THE INVENTION:

[0006]

[0002] Stability of a skeletal joint refers to ability of the skeletal joint to maintain appropriate alignment and functioning, particularly under motion and load. The stability or instability of the joint is defined by the tissues surrounding the joint and the condition of the bones forming the joint.

[0007]

[0003] Anterior Cruciate Ligament (ACL) is one of the cruciate ligaments in a human knee which connects tibia to femur and plays a crucial role in the stability of the knee. The ACL injuries are among the most common and debilitating injuries in sports and other high-impact activities, wherein, at times, sudden jerk due to the intensive movements result in tibia & femur turning in opposite directions relative to each other, which may cause ACL tear. Such injury is not just painful, but also may require considerable time to heal completely. In some cases, the person with ACL injury may require to go through operative procedure. In such cases, the person requires a great post-operative care. In view of this, there have been attempts for the development of an ACL injury prevention mechanism.

[0008]

[0004] Osteoarthritis is another debilitating condition, with millions affected worldwide annually. In osteoarthritis, the protective cartilage around the skeletal joint are damaged, thereby hurting the bones inside. Similarly, certain other joint conditions can be painful and hence it is essential for the clinicians to have a mechanism that predict the joint stability, which will help them in prescribing appropriate remedy.

[0009]

[0005] A prior art US11774246 B2 titled ‘Correction of heading errors of inertial measurement units of a motion tracking system’ discloses A method comprising: digitally processing orientation measurements provided by each of first and second inertial measurement units, the first and second units being arranged on first and second body members of a person, respectively, according to a predetermined unit arrangement, and the first and second body members being connected by a joint; the measurements are digitally processed such that the computing device at least: computes a length vector of a segment of the first body member based on a first orientation measurement of the first unit; defines a joint axis plane of the joint based on a second orientation measurement of the second unit; and computes a heading rotation value for making the first orientation measurement to be contained within the joint axis plane defined; and the method further comprising digitally modifying the first orientation measurement or the second orientation measurement by applying a rotation at least based on the heading rotation value computed. Also, a motion tracking system and a computer program product is disclosed therein.

[0010]

[0006] In another prior art application numbered US20240108285 Al titled “Medical system having a position measurement patch device for providing measurement data or a therapy” discloses a medical system comprising a patch device and a computer. The patch device is in communication with the computer. The patch device is configured for generating measurement data or providing a therapy. The patch device comprises electronic circuitry, a battery, an antenna system, one or more sensors, an IMU (inertial measurement unit), and a flexible enclosure. The antenna system can comprise a dual antenna formed on a dielectric substrate with a first antenna on a first side of the dielectric substrate and a second antenna on a second side of the dielectric substrate. The one or more sensors can comprise devices configured to provide measurement data or a therapy. The IMU is configured to measure position, movement, and trajectory of the patch device. The electronic circuitry is configured to harvest energy from one or more radio frequency signals received by the antenna system to recharge the battery.

[0011]

[0007] In another prior art application numbered US7976482 B2 titled “Device and method for knee ligament strain measurement” discloses a device for measuring displacement of the tibia in relation to the femur in response to an applied force or torque on the tibia. A first shaft is tiltable about a first axis close to the ankle and approximately parallel to the coronal plane of the patient's body. A second shaft is connected to the first shaft and is rotatable about a second axis perpendicular to the first axis, and is close to the ankle and approximately parallel to the tibia. A foot support platform is mounted on the second shaft, the foot support platform being configured for attachment of the foot at a fixed position. A displacement test device is provided for applying forces to the tibia and measuring the shift or displacement of the proximal tibia relative to the distal femur.

[0012]

[0008] In another prior art application numbered US20150032034 Al titled “Apparatus and method for quantifying stability of the knee” discloses a wireless motion sensor platform comprising MEMS inertial sensors and accompanying software for classification of diverse motion characteristics and kinematics of patient anatomy at high resolution. The sensor platform comprises a low-cost, compact, and low-weight device that can be applied to a patient's upper and / or lower leg during a knee examination to measure acceleration along three axes as well as rotations about these axes. Further, the device in said patent application is configured to acquire an acceleration data from an acceleration sensor and a rotational data from a gyroscope. The system further computes the orientation data from the aforesaid data and generates a metrics relating to the kinematic characteristics of skeletal joint.

[0013]

[0009] In another prior art publication titled "Knee stability assessment on anterior cruciate ligament injury: Clinical and biomechanical approaches" authored by Mak-Ham Lam et al. describes about the Anterior cruciate ligament (ACL) injury which is common in knee joint accounting for 40% of sports injury. ACL injury leads to knee instability, therefore, understanding knee stability assessments would be useful for diagnosis of ACL injury, comparison between operation treatments and establishing retum-to-sport standard. This article firstly introduces a management model for ACL injury and the contribution of knee stability assessment to the corresponding stages of the model. Secondly, standard clinical examination, intra-operative stability measurement and motion analysis for functional assessment are reviewed. Orthopaedic surgeons and scientists with related background are encouraged to understand knee biomechanics and stability assessment for ACL injury patients.

[0014] SUMMARY OF INVENTION:

[0015]

[0010] It is an objective of the present invention to obtain a system and method for monitoring stability of ligaments of the joint.

[0016] [Oi l] It is also another objective of the present invention to provide a method to automatically calculate ligament displacement.

[0017]

[0012] It is yet another objective of the present invention to employ a method for on-board processing and specialized sensor fusion that allows for realtime data analysis.

[0018]

[0013] It is yet another objective of the present invention to provide a precise, non-invasive and cost-effective solution for medical practitioner to monitor the stability of the ACL and other joint ligaments.

[0019]

[0014] Accordingly, a wearable system for assessing stability of a skeletal joint is disclosed herein.

[0020]

[0015] In one of the aspect, the wearable system comprises at least one multi -axis miniature inertial measurement unit in a singular or pair format, wherein the multi-axis miniature inertial measurement unit are placed on at least an upper bone and / or at least a lower bone that form the skeletal joint. The multi -axis miniature inertial measurement unit is capable of obtaining multi-axis data sets pertaining to the skeletal joint comprising an orientation dataset, a static dataset and a dynamic dataset.

[0021]

[0016] In the same aspect, the multi -axis miniature inertial measurement unit in a singular or pair format comprises at least one sensor unit comprising an accelerometer, a gyroscope, and at least one magnetometer (12).

[0022]

[0017] In the same aspect, the wearable system comprises a controller unit communicatively coupled with the at least one multi-axis miniature inertial measurement unit. The controller unit is configured to obtain dynamic raw data, auto-correct and auto-calibrate obtained dynamic raw data, replicate dynamic raw data by visualise and quantify, interpret and detect anomalies of the replicated data

[0023] In the same aspect, the processing & control module is configured to acquire the skeletal data from the sensing device; receive patient specific data from outside devices; assess / calculate skeletal joint stability; encrypt & control the skeletal data; and generate a report relating to the skeletal joint stability.

[0024]

[0018] In the same aspect, the controller unit is configured to:

[0025] (i) receiving an orientation dataset associated with multi-axis miniature inertial measurement unit in a singular or pair format;

[0026] (ii) generate plurality of pre-calibrated signal, each of the pre-calibrated signal represents each of the orientation dataset obtained by the multi-axis miniature inertial measurement unit in a singular or pair format;

[0027] (iii) calibrate the plurality of the pre-calibrated signals;

[0028] (iv) obtain a static dataset relating to a skeletal joint for a pre-determined time by a multi-axis miniature inertial measurement unit in a singular or pair format, wherein the static dataset represents a static condition of the skeletal joint,

[0029] (v) obtaining a dynamic dataset when a movement of the skeletal joint is performed, and

[0030] (vi) calculating plurality of parameters indicating skeletal joint stability by correlating the dynamic dataset and the static dataset, wherein the plurality of parameters include, but not limited to, liner displacement, angular displacement, range of motion, proprioception, varus thrust, and optionally plyometric and turnout controls.

[0031]

[0019] In the same aspect, the static dataset obtained by a multi -axis miniature inertial measurement unit in a singular or pair format comprises (i) positioning of a multi-axis miniature inertial measurement unit in a singular or pair format on the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal joint.

[0032]

[0020] In the same aspect, the dynamic dataset obtained by a multi-axis miniature inertial measurement unit in a singular or pair format comprises (i) angular movement of the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal joint, (iii) Varus thrust measurement in a filtered format after elimination of soft tissue artefacts and unwanted frequencies, (iv) internal and external rotation of the joint, (v) quaternion, acceleration and gyroscopic data.

[0033]

[0021] In the same aspect, the wearable system further comprises a memory module for storing the processed data from the controller unit.

[0034]

[0022] In the same aspect, the wearable system further comprises a rechargeable battery pack provided with a power regulating module and an internal protection circuit.

[0035]

[0023] The wearable system, in accordance with the present invention, is capable of communicating with a remote data server to store the processed data received from the controller unit via a wired or wireless communication module (40); the stored processed data further adaptable to a printable skeletal joint stability report as well as configurable to predictive and / or generative capability for generating an array of predictive analytics.

[0036]

[0024] In another aspect, method of operating a wearable system for assessment of a skeletal joint stability is also disclosed.

[0037]

[0025] In this aspect, the method comprises of obtaining an orientation dataset associated with a multi-axis miniature inertial measurement unit in a singular or pair format, wherein a multi-axis miniature inertial measurement unit in a singular or pair format is placed on an upper bone and / or a lower bone that form the skeletal joint; sending the orientation dataset from a multi-axis miniature inertial measurement unit to a controller unit; generating plurality of pre-calibrated signal by the controlling unit, wherein each of the pre-calibrated signal represents each of the orientation dataset obtained by a multi-axis miniature inertial measurement unit in a singular or pair format; calibrating the plurality of the pre-calibrated signals by the controlling unit; obtaining a static dataset relating to a skeletal joint for a predetermined time by a multi-axis miniature inertial measurement unit in a singular or pair format, wherein the static dataset represents a static condition of the skeletal joint; obtaining a dynamic dataset when a movement of the skeletal joint is performed; and calculating plurality of parameters indicating skeletal joint stability by correlating the dynamic dataset and the static dataset, wherein the plurality of parameters include, but not limited to, liner displacement, angular displacement, range of motion, proprioception, varus thrust, and optionally plyometric and turnout controls.

[0038] BRIEF DESCRIPTION OF DRAWINGS:

[0039]

[0026] Following figures demonstrate the preferred embodiments and the components associated with it.

[0040] Figure 1 illustrates a wearable system (100) of the present invention.

[0041] Figure 2 shows a flowchart illustrating the steps of calculating linear displacement of the skeletal joint.

[0042] Figure 3 shows a flowchart illustrating the steps of calculating an angular displacement of the skeletal joint.

[0043] Figure 4 shows a flowchart illustrating the steps of calculating a range of motion of the skeletal joint.

[0044] Figure 5 shows a flowchart illustrating the steps of calculating a proprioception of the skeletal joint.

[0045] Figure 6 shows a flowchart illustrating the steps of calculating a varus thrust of the skeletal joint.

[0046] DETAILED DESCRIPTION OF INVENTION:

[0047]

[0027] The present invention relates to quantitatively evaluating and assessing the stability, condition and health of a skeletal joint in a comprehensive and holistic manner. More particularly, the present invention relates to a system and method for assessing condition and stability of any skeletal joint resulting in scientific facilitation of a clinician to monitor joint stability and plan optimised corrective interventions comprising non-invasive, minimally invasive, invasive and / or in combination with required physiotherapeutic interventions.

[0048]

[0028] In the context of the present invention, the term “skeletal joint” broadly refers to any joint in the human limbs and / or animal joint. However, “skeletal joint” may primarily refer to knee joint, wrist joint, shoulder joint or the elbow joint. However, the application of the present invention is not limited to the knee joint or the elbow joint, but extends to other joints in the human body as well.

[0049] The term “subject” or “the patient” refers to any human capable of undergoing the joint stability assessment with the use of the system of the present invention. The term “wearable” with reference to the system disclosed and claimed in the present invention may include system’s capability of wearing, attaching, fastening or any other means by which the system can be affixed to the human body parts.

[0050]

[0029] The present invention may be comprehended by referring to figures 1-6 appended at the end of the specification. However, it may be considered that the figures are not intended to restrict the scope of the specification.

[0051]

[0030] Figure 1 illustrates a preferred embodiment of the wearable system (100) capable of assessing stability of a skeletal joint. As shown in Figure 1, the wearable system (100) comprises at least one multi-axis miniature inertial measurement unit (10) in a singular or pair format; a controller unit (20) communicatively coupled with a multi-axis miniature inertial measurement unit (10) in a singular or pair format; a memory module (30) for storing the processed data from the controller unit (20); a rechargeable battery pack (50) provided with a power regulating module (60) and an internal protection circuit.

[0052]

[0031] In this preferred embodiment, the at least one multi-axis miniature inertial measurement unit (10) is placed on at least an upper bone and / or at least a lower bone that form the skeletal joint, wherein the multi -axis miniature inertial measurement unit (10) is capable of obtaining multi-axis data sets pertaining to the skeletal joint comprising an orientation dataset, a static dataset and a dynamic dataset.

[0053]

[0032] In this preferred embodiment, the multi-axis miniature inertial measurement unit (10) in a singular or pair format comprises at least one sensor unit (11) comprising an accelerometer (I la) a gyroscope (11b), and at least one magnetometer (12).

[0054]

[0033] In this preferred embodiment, the static dataset obtained by a multiaxis miniature inertial measurement unit (10) in a singular or pair format comprises (i) positioning of a multi -axis miniature inertial measurement unit (10) in a singular or pair format on the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal j oint.

[0055]

[0034] In this preferred embodiment, the dynamic dataset obtained by a multi-axis miniature inertial measurement unit (10) in a singular or pair format comprises (i) angular movement of the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal joint, (iii) Varus thrust measurement in a filtered format after elimination of soft tissue artefacts and unwanted frequencies, (iv) internal and external rotation of the joint, (v) quaternion, acceleration and gyroscopic data.

[0056]

[0035] In a preferred embodiment, the multi-axis inertial measurement unit

[0057] (10) in a singular or pair format is provided with an adhesive means to be able to attach to the skeletal joints.

[0058]

[0036] In this preferred embodiment, the controller unit (20) is configured to obtain dynamic raw data, auto-correct and auto-calibrate obtained dynamic raw data, replicate dynamic raw data by visualise and quantify, interpret and detect anomalies of the replicated data.

[0059]

[0037] In this preferred embodiment, such capability is realised by following steps:

[0060] (i) receiving an orientation dataset (101,102) associated with multi -axis miniature inertial measurement unit (10) in a singular or pair format;

[0061] (11) generate plurality of pre-calibrated signal (q,a,g), each of the pre -calibrated signal represents each of the orientation dataset obtained by the multi -axis miniature inertial measurement unit (10) in a singular or pair format;

[0062] (iii) calibrate (103) the plurality of the pre-calibrated signals (a,q,g);

[0063] (iv) obtain a static dataset relating to a skeletal joint for a pre-determined time by a multi -axis miniature inertial measurement unit (10) in a singular or pair format, wherein the static dataset represents a static condition of the skeletal joint,

[0064] (v) obtaining a dynamic dataset when a movement of the skeletal joint is performed, and

[0065] (vi) calculating plurality of parameters indicating skeletal joint stability by correlating the dynamic dataset and the static dataset, wherein the plurality of parameters include, but not limited to, liner displacement, angular displacement, range of motion, proprioception, Varus thrust, optionally plyometric and turnout controls.

[0066]

[0038] In the preferred embodiment of the present invention, following types of skeletal joint stability may be assessed: linear displacement, angular displacement, range of motion proprioception, plyometric and turnout controls and Varus thrust. The assessment of these conditions is exemplified in this specification. The applicability of the present is not limited to the stated conditions and may extend to any condition that involves similar movement of the skeletal joints.

[0067]

[0039] In the preferred embodiment, the controlling unit (20) is adapted to calculate linear displacement of the joint, which is also illustrated in Figure 2. The calculation linear displacement of the joint comprises of:

[0068] (a) obtaining the calibrated data (103);

[0069] (b) obtaining quaternion, acceleration and gyroscopic data (201,202) by the at least one multi-axis miniature inertial measurement unit (10) of the lower joint;

[0070] (c) converting quaternion measurement into an axis angle measurement (203);

[0071] (d) smoothing the obtained measurement (204,205) including removal of frequencies pertaining to soft tissue artefacts;

[0072] (e) calculate multi-peak and multi-trough data from the axis angle;

[0073] (f) calculate average angle by calculating difference between the peaks and troughs; and

[0074] (g) calculate the linear displacement with respect to the average angle.

[0075]

[0040] In a preferred embodiment, the controlling unit (20) is adapted to calculate range of skeletal joint stability during angular displacement of the skeletal joint, as will be explained with reference to Figure 3, comprises steps of:

[0076] (a) obtaining calibrated data (103);

[0077] (b) measuring internal and external rotation (301,302) by a multi -axis miniature inertial measurement unit (10) in a singular or pair format;

[0078] (c) visualising the measured internal and external rotation; and

[0079] (d) calculating relative orientation (303) of a multi-axis inertial measurement unit (10) in a singular or pair format about an axis passing through the upper and / or lower bone of the skeletal joint.

[0080]

[0041] In a preferred embodiment, the controlling unit (20) configurable with a force application device capable of providing consistent and standardized single or repetitive force, is adapted to calculate range of motion, as will be explained with reference to Figure 4, comprises:

[0081] (a) obtaining the calibrated data (103);

[0082] (b) obtaining orientation data (402a, 402b) of the upper bone and / or the lower bone by a multi-axis miniature inertial measurement unit in a singular or pair format (10); wherein the data also comprises real-time input on applied force and quality of signal from said multi-axis miniature inertial measurement unit

[0083] (c) calculating axis angle relative to time (403a, 403b, 405a, 405b);

[0084] (d) smoothing the axis angle data (FT,WT);

[0085] (e) averaging axis angle (404a, 406a, 404b, 406b) at local maxima at the upper bone and / or the lower joint; and

[0086] (f) calculating the range of linear extension (407) by summing the local maxima at the upper bone and / or the lower joint.

[0087]

[0042] In this embodiment, the force application device includes force application cum measuring mechanism configurable for manual force application or automated force application through actuators, the actuators receiving controlled feedback and operating under a closed-loop algorithm using a force sensor data

[0088]

[0043] In the preferred embodiment, the controlling unit (20) is adapted to calculate of range of skeletal joint stability in the event of joint extension and flexion deformity to calculate proprioception, as will be explained with reference to Figure 5, comprises:

[0089] (a) obtaining the calibrated data (103a, 103b);

[0090] (b) obtaining orientation data (50 la, 50 lb) of the upper bone and / or the lower bone for two sets of flexion by a multi-axis miniature inertial measurement unit (10) in a singular or pair format;

[0091] (c) calculating axis angle 1 and axis angle 2 (502a, 504a, 502b, 504b) for both the upper bone and the lower bone for each of two sets of flexion with respect to time;

[0092] (d) smoothing the data relating to the axis angle;

[0093] (e) calculating first range of motion (503a) by combining axis angle 1 of the upper bone and / or axis angle 1 of the lower bone;

[0094] (f) calculating second range of motion (503b) by combining axis angle 2 of the upper bone and axis angle 2 of the lower bone; and

[0095] (g) calculating the proprioception (506) by subtracting the second range from the first range; and

[0096] (h) calculating the fixed flexion deformity (507) by adding the lower joint and upper joint orientation static axis angles.

[0097]

[0044] In the preferred embodiment, the controlling unit (20) is adapted to calculate of range of skeletal joint stability in the event of Varus thrust, as will be explained with reference to Figure 6, comprises:

[0098] (a) obtaining the calibrated data (103);

[0099] (b) obtaining the static data and swing data (601) from a multi -axis miniature inertial measurement unit (10) in a singular or pair format;

[0100] (c) smoothing the obtained data (603,604);

[0101] (d) calculating RMS value of the stating data (605), and swing data sRMS by the controlling unit (20); and

[0102] (e) calculating the Varus thrust index (607) by dividing the stating data (606) by swing data sRMS by the controlling unit (20).

[0103]

[0045] In the same embodiment, the system further comprises of a negative temperature coefficient thermistor placed on the rechargeable battery pack (50) for monitoring battery temperature during charging and operation.

[0104]

[0046] In the same embodiment, the system further comprises plurality of LEDs controlled by the thermistor configured to serve as visual indicator comprising RGB colours for the state of the system.

[0105]

[0047] Further, the wearable system is capable of communicating with a remote data server (70) to store the processed data received from the controller unit

[0106] (20) via a wired or wireless communication module (40); the stored processed data further adaptable to a printable skeletal joint stability report as well as configurable to predictive and / or generative capability for generating an array of predictive analytics.

[0107]

[0048] The present invention also discloses a method of operating a wearable system (100) for assessment of a skeletal joint stability, the method comprises:

[0108] (a) obtaining an orientation dataset associated with a multi-axis miniature inertial measurement unit (10) in a singular or pair format, wherein a multi-axis miniature inertial measurement unit (10) in a singular or pair format is placed on an upper bone and / or a lower bone that form the skeletal joint;

[0109] (b) sending the orientation dataset from a multi-axis miniature inertial measurement unit (10) to a controller unit (20);

[0110] (c) generating plurality of pre-calibrated signal by the controlling unit (20), wherein each of the pre-calibrated signal represents each of the orientation dataset obtained by a multi-axis miniature inertial measurement unit (10) in a singular or pair format;

[0111] (d) calibrating the plurality of the pre -calibrated signals by the controlling unit (20);

[0112] (e) obtaining a static dataset relating to a skeletal joint for a pre-determined time by a multi -axis miniature inertial measurement unit (10) in a singular or pair format, wherein the static dataset represents a static condition of the skeletal joint;

[0113] (f) obtaining a dynamic dataset when a movement of the skeletal joint is performed; and

[0114] (g) calculating plurality of parameters indicating skeletal joint stability by correlating the dynamic dataset and the static dataset, wherein the plurality of parameters include liner displacement, angular displacement, range of motion, proprioception, varus thrust.

[0115]

[0049] In the method of the present invention, the generation of precalibration signal (103) by the controlling unit comprises averaging and fusing datasets (101,102) from a sensor unit (11) and a magnetometer (12) of a multi-axis miniature inertial measurement unit (10) in a singular or pair format.

[0116]

[0050] Figure 2 illustrates the steps of calculating linear displacement of the skeletal joint. It comprises steps of: obtaining the calibrated data (103); obtaining quaternion, acceleration and gyroscopic data (201,202) by the at least one multi-axis miniature inertial measurement unit (10) on the lower bone; converting quaternion measurement into an axis angle measurement (203); smoothing the obtained measurement (204,205), including removal of frequencies pertaining to soft tissue artefacts; calculate multi-peak and multi-trough data from the axis angle by plotting corrected axis angle vs time data; calculate average angle of tugs generated about the joint by calculating difference between the peaks and troughs; and calculate the linear displacement with respect to the average angle, .

[0117]

[0051] The integrated unit provided for this purpose further comprises a mechanism to apply standardized and controlled force for linear Displacement testing, and a processing unit configured to fuse signals from the plurality of sensors to yield results. The integrated unit has a mechanism, wherein the corrected post calibration data may be used to correct the fused sensor outputs, by fileting out the soft tissue artefacts using the wavelet transform technique . one configuration allows manual application of force, providing real-time feedback of the applied force and IMU signal quality.

[0118]

[0052] Another configuration enables automated force application through actuators, the actuators receiving controlled feedback and operating under a closed- loop algorithm using the force sensor data.

[0119]

[0053] Yet another configuration involves using a spring balance to apply a standard force, thus ensuring that in each test to measure linear displacement, we have a constant amount of force being applied.

[0120]

[0054] Figure 3 discloses the calculation of range of skeletal joint stability by the controlling unit (20) during angular displacement of the skeletal joint. It comprises steps of: obtaining calibrated data (103), wherein the pair of multi-axis miniature inertial measurement unit (10) is placed on the lower bone and the upper bone that form the joint.

[0121] - measuring internal and external rotation (301,302) by the multi -axis miniature inertial measurement unit (10), wherein the measurement is done through techniques for conversion of this data into 3D virtual realm which will remove the constraints put up by the physical domain.

[0122] - visualising the measured internal and external rotation,; and calculating relative orientation (303) of a multi -axis inertial measurement unit ( 10) in a singular or pair format about an axis passing through the upper and / or lower bone of the skeletal joint.

[0123]

[0055] Figure 4 discloses calculation of range of motion by the controlling unit (20) configurable with a force application device capable of providing consistent and standardized single or repetitive force, which comprises steps of: obtaining the calibrated data (103), wherein the pair of multi-axis miniature inertial measurement unit (10) is placed on the lower bone and the upper bone that form the joint; obtaining orientation data (402a, 402b) of the upper bone and / or the lower bone by a multi-axis miniature inertial measurement unit in a singular or pair format (10), wherein the data also comprises real-time input on applied force and quality of signal from said multi-axis miniature inertial measurement unit; calculating axis angle relative to time (403a,403b, 405a, 405b); smoothing the axis angle data (FT,WT), wherein FT and WT represents Fourier Transform and Wavelet Transform techniques; averaging axis angle (404a, 406a, 404b, 406b) at local maxima at the upper bone and / or the lower joint; and calculating the range of linear extension (407) by summing the local maxima at the upper bone and / or the lower joint.

[0124]

[0056] Figure 5 shows calculation of range of skeletal joint stability in the event of flexion deformity to calculate proprioception comprises steps of: obtaining the calibrated data (103a, 103b), wherein the pair of multi-axis miniature inertial measurement unit (10) is placed on the lower bone and the upper bone that form the joint; obtaining orientation data (501a,501b) of the upper bone and / or the lower bone for two sets of flexion by a multi-axis miniature inertial measurement unit (10) in a singular or pair format; calculating axis angle 1 and axis angle 2 (502a, 504a, 502b, 504b) for both the upper bone and the lower bone for each of two sets of flexion with respect to time; smoothing the data relating to the axis angle; calculating first range of motion (503a) by combining axis angle 1 of the upper bone and / or axis angle 1 of the lower bone; calculating second range of motion (503b) by combining axis angle 2 of the upper bone and axis angle 2 of the lower bone; and calculating the proprioception (506) by subtracting the second range from the first range; and calculating the fixed flexion deformity (507) by adding the lower joint and upper joint orientation static axis angles.

[0125]

[0057] Figure 6 shows calculation of range of skeletal joint stability in the event of Varus thrust comprises steps of: obtaining the calibrated data (103), wherein the pair of multi-axis miniature inertial measurement unit (10) is placed on the lower bone and the upper bone that form the joint; obtaining the static data and swing data (601) from a multi-axis miniature inertial measurement unit (10) in a singular or pair format; smoothing the obtained data (603,604); calculating RMS value of the stating data (605), and swing data sRMS by the controlling unit (20); and calculating the Varus thrust index (607) by dividing the stating data (606) by swing data sRMS by the controlling unit (20).

[0126]

[0058] Examples / test results are as follows:

[0127] Example 1:

[0128] A 45-year-old patient with early-stage osteoarthritis presented to the outpatient department with an inability to ambulate without pain.

[0129] Examination On physical examination by Dr. Sathish, Assistant Professor, Department of Orthopaedics, Stanley Medical College, Chennai, restriction in range of motion, difficulty in stair climbing, and impaired step perception were observed, consistent with a proprioceptive deficit. Evaluation: The patient was evaluated to provide quantitative staging of disease status.

[0130] Findings

[0131] Conclusion: The Knee health report is comprehensive and corroborated with the clinician findings. The physician was able to measure the actual status of the knee, and in this case even helps the patient assess how far away they are away from surgical intervention.

[0132] Example 2:

[0133] A 27-year-old patient with an acute anterior cruciate ligament (ACL) tear, following resolution of joint effusion, presented to the outpatient department for evaluation of knee buckling. Examination On clinical assessment by Dr. Sathish, Assistant Professor, Department of Orthopaedics, Stanley Medical College, Tamil Nadu, the Lachman test revealed anterior laxity.

[0134] Evaluation The patient was further evaluated to obtain quantitative measurements of knee stability and proprioception.

[0135] Findings

[0136] Conclusion: The Knee health report is comprehensive and corroborated with the clinician findings. Quantitative assessment enabled accurate assessment of the holistic picture of the knee and thus helped the clinician give the appropriate recommendation of surgical intervention.

[0137] Example 3:

[0138] A 30-year-old patient with a chronic anterior cruciate ligament (ACL) tear, 6 months post-injury, presented to the outpatient department with complaints of knee instability.

[0139] Examination On clinical assessment by Dr. Sathish, Assistant Professor, Department of Orthopaedics, Stanley Medical College, Tamil Nadu, the Lachman test revealed anterior laxity.

[0140] Evaluation The patient was subsequently assessed using the Nambikkai Knee Pucks for quantitative measurement of knee stability and proprioception.

[0141] Findings

[0142] Conclusion: Despite measurable laxity on the Lachman test, the absence of angular displacement, maintained range of motion, and only borderline proprioceptive loss indicated that the patient could be effectively managed with rehabilitation rather than surgical intervention.

[0143] Example 4:

[0144] A 50-year-old patient with a chronic anterior cruciate ligament (ACL) tear, 6 months post-injury, and an avid footballer who plays recreationally, presented to the outpatient department with complaints of knee instability.

[0145] Examination: On clinical evaluation, Dr. Sathish, Assistant Professor, Department of Orthopaedics, Stanley Medical College, Tamil Nadu, was able to elicit both pain and laxity.

[0146] Evaluation: The patient was further assessed for quantitative measurement of laxity, range of motion, varus thrust, and proprioception.

[0147] Findings

[0148] Conclusion The knee health report was comprehensive and aligned with the clinician’s findings. Although the measured laxity of 9 mm is borderline, it is of concern in an older individual, where lesser values are typically observed. The restricted range of motion, borderline varus thrust, and proprioceptive deficits collectively indicate that the chronic ACL tear has progressed to early signs of osteoarthritis, underscoring the impact of leaving the ligament injury unattended.

[0149] Example 5: A 25 -year-old patient who had undergone anterior cruciate ligament (ACL) reconstruction presented to the outpatient department for evaluation 2 weeks postsurgery.

[0150] Examination On clinical evaluation, the physician observed no laxity but noted a decrease in range of motion. Evaluation The patient was assessed to objectively quantify knee function postoperatively.

[0151] Findings:

[0152] Conclusion: The knee health report generated is comprehensive and corroborated with the clinical findings. The patient demonstrated normal stability parameters with no evidence of laxity. However, the range of motion was restricted to 100°, indicating impaired postoperative recovery compared to the standard expected progress by 2 weeks. The focus of rehabilitation should therefore be directed towards improving range of motion.

[0153] Example 6: A 67-year-old patient with severe pain on ambulation presented to the outpatient department.

[0154] Examination: On physical examination by , there was severe restriction in the range of motion, significant difficulty in stair climbing, and impaired ability to gauge steps accurately, consistent with a proprioceptive deficit. Evaluation: The patient was assessed to quantitatively stage the severity of the condition.

[0155] Findings:

[0156] Conclusion: The knee report card generated is comprehensive and corroborated with physical examination findings. The knee health report confirmed severe restriction of motion, increased varus thrust, and deranged proprioception, consistent with advanced disease. This patient requires surgical intervention to ensure an acceptable return to function, as the findings are beyond the threshold for correction through rehabilitation alone.

[0157] Clinical Applications & Advantages:

[0158]

[0059] In a clinical setting, the one or more sensing devices are placed at three specific locations on the joint. These locations are carefully chosen to maximize the accuracy of the data collected and to provide a comprehensive assessment of joint stability. The system is designed to be user-friendly, with clear markings on the casings of the sensing devices and detailed guidelines to help clinicians position the sensing devices correctly. This ease of use is crucial for ensuring that the data collected is accurate and that the subsequent stability assessment is reliable.

[0159]

[0060] The system's ability to track joint movements dynamically, rather than just statically which is common with current methods, offers a significant advantage in assessing ACL stability. Traditional methods often focus on static measurements, such as those taken during a physical examination, which may not fully capture the dynamic nature of joint instability. By providing real-time data on joint movements, this system allows clinicians to assess ACL stability during activities that more closely mimic the conditions under which injuries occur, such as walking, running, or jumping.

[0160] Cost-Effectiveness and Impact on ACL Stability Assessment

[0161]

[0061] The novel system and method for assessing ACL stability using miniaturized IMUs represent a cost-effective solution for clinicians and patients alike. Traditional methods of assessing ACL stability, such as MRI scans or invasive procedures, can be expensive and time-consuming. This system offers a non-invasive alternative that can be used in a clinical setting with minimal discomfort to the patient. The use of affordable, high-precision sensors and robust algorithms ensures that the system delivers accurate results without the need for expensive equipment or procedures.

[0162]

[0062] Furthermore, the system's ability to assess not only ACL stability but also the stability of other joint ligaments adds to its value. Joint injuries often involve multiple ligaments, and a system that can assess the stability of all relevant ligaments provides a more comprehensive understanding of a patient's condition. This capability can lead to more accurate diagnoses, better treatment plans, and improved outcomes for patients with joint injuries.

[0163]

[0063] By combining high-precision sensors, an adhesive attachment mechanism, and advanced sensor fusion algorithms, the system provides accurate and reliable data on joint movements, allowing for a comprehensive assessment of joint stability. The ease of use, cost-effectiveness, and ability to track dynamic joint movements make this system a valuable tool for clinicians and a promising option for improving patient outcomes in the diagnosis and treatment of joint injuries.

[0164] Other features:

[0165] Focus on Lachman Test:

[0166]

[0064] The system is specifically designed to track joint movements during the Lachman test, which is a targeted application not explicitly mentioned in the cited prior art.

[0167] Ease of Use for Clinicians:

[0168]

[0065] The system includes clear markings on IMU casings and guidelines for correct placement, which addresses the practical needs of clinicians. This focus on usability could lead to more consistent and accurate assessments in clinical settings.

[0169] Improved Accuracy:

[0170]

[0066] The combination of high-precision sensors, specialized software, and direct attachment to the joint potentially offers higher accuracy in ACL stability assessment compared to existing methods.

[0171] Non-Invasive and Portable:

[0172]

[0067] Unlike some existing methods that may require large, stationary equipment, this system is portable and non-invasive, allowing for easier and more frequent assessments.

[0173] Real-Time Data Processing:

[0174]

[0068] The use of on-board processing and specialized sensor fusion algorithms allows for real-time data analysis, which is crucial for immediate clinical decision-making.

[0175] Specialized Algorithm Development:

[0176]

[0069] The development of algorithms specifically for ACL stability assessment using IMU data represents a potential advancement in medical data analysis.

[0177] Economic Significance:

[0178] Potential for Wider Adoption:

[0179]

[0070] The ease of use and portability of the system could lead to wider adoption in various clinical settings, potentially improving overall patient care and outcomes.

[0180] Reduced Need for Multiple Diagnostic Tools:

[0181]

[0071] By providing comprehensive data on joint stability, this system could potentially reduce the need for multiple diagnostic tools, leading to cost savings for healthcare providers.

[0182]

[0072] Useful in helping accurately measuring joint health, the present invention can be used by orthopaedician to assess ACL health and the same approach can be extrapolated to other ligaments such as the posterior cruciate ligament (PCL), medial collateral ligament (MCL) and lateral collateral ligament (LCL), Helps in assessing the joint including the neck, elbow, wrist, hip, ankle, metacarpophalangeal and interphalangeal joints as a whole while in motion which is dynamic stability, which transcends the current market devices which focus only on the ligament. Also, the use of force sensitive resistor in the present invention measures the force generated by the muscles as a parameter that will be used to assess the health of the secondary stabilizers of the knee - Quadriceps and Hamstring muscles.

[0183]

[0073] It is possible, with the present invention, to evaluate for conditions such as adhesive capsulitis, rotator cuff pathologies, labral pathologies, Ehler Danlos syndrome, shoulder osteoarthritis, post-surgical stiffness, glenohumeral instability. The ankle joint measurement is also of prime significance with it being particularly useful for ballet dancers, where we again measure the range of motion, joint laxity, proprioception other dynamic measurements include plyometric control, turnout control. At the elbow, it can be extrapolated it measure Range of motion, Joint stability, proprioception.

[0184]

[0074] Joint alignment (Carrying load) and the Valgus load can be predicted to evaluate and recommend the exercise regimen for conditions such as golfer’s elbow, arthritis and bursitis.

[0185]

[0075] At the cerival, it can be used to measure ROM in all directions, with emphasis on measuring muscle strength by asking the patient to move against a fixed resistance, helps in evaluating cervical spondylosis and osteoarthritis.

[0186]

[0076] At the lumbar spine, the IMU units can be placed accordingly to evaluate for flexion and extension at that region.

Claims

We Claim,1. A wearable system (100) for assessing stability of a skeletal joint comprises: at least one multi -axis miniature inertial measurement unit (10) in a singular or pair format, being placed on at least an upper bone and / or at least a lower bone that form the skeletal joint, wherein the multi-axis miniature inertial measurement unit (10) is capable of obtaining multi -axis data sets pertaining to the skeletal joint comprising an orientation dataset, a static dataset and a dynamic dataset; a controller unit (20) communicatively coupled with a multi-axis miniature inertial measurement unit (10) in a singular or pair format, wherein the controller unit (20) is configured to obtain dynamic raw data, auto-correct and auto-calibrate obtained dynamic raw data, replicate dynamic raw data by visualise and quantify, interpret and detect anomalies of the replicated data by steps of:(i) receiving an orientation dataset (101,102) associated with multi -axis miniature inertial measurement unit (10) in a singular or pair format;(ii) generate plurality of pre-calibrated signal (q,a,g), each of the pre-calibrated signal represents each of the orientation dataset obtained by the multi-axis miniature inertial measurement unit (10) in a singular or pair format;(iii) calibrate (103) the plurality of the pre -calibrated signals (a,q,g);(iv) obtain a static dataset relating to a skeletal joint for a predetermined time by a multi-axis miniature inertial measurement unit (10) in a singular or pair format, wherein the static dataset represents a static condition of the skeletal joint,(v) obtaining a dynamic dataset when a movement of the skeletal joint is performed, and(vi) calculating plurality of parameters indicating skeletal joint stability by correlating the dynamic dataset and the static dataset, wherein the plurality of parameters include liner displacement, angular displacement, range of motion, proprioception, varus thrust, plyometric and turnout controls and such; a memory module (30) for storing the processed data from the controller unit (20); a rechargeable battery pack (50) provided with a power regulating module (60) and an internal protection circuit; wherein the wearable system is capable of communicating with a remote data server (70) to store the processed data received from the controller unit (20) via a wired or wireless communication module (40); the stored processed data further adaptable to a printable skeletal joint stability report as well as configurable to predictive and / or generative capability for generating an array of predictive analytics.

2. The wearable system (100) as claimed in claim 1, wherein a multi -axis miniature inertial measurement unit (10) in a singular or pair format comprises at least one sensor unit (11) comprising an accelerometer (1 la) a gyroscope (1 lb), and at least one magnetometer (12).

3. The wearable system (100) as claimed in claim 1, wherein the controlling unit (20) is configured to generate the pre -calibration signal (q,a,g) by averaging and fusing datasets from a sensor unit (11) and a magnetometer (12) of a multi-axis miniature inertial measurement unit (10) in a singular or pair format.

4. The wearable system (100) as claimed in claim 1, wherein the static dataset obtained by a multi -axis miniature inertial measurement unit (10) in a singular or pair format comprises (i) positioning of a multi-axis miniatureinertial measurement unit (10) in a singular or pair format on the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal joint.

5. The wearable system (100) as claimed in claim 1, wherein the dynamic dataset obtained by a multi-axis miniature inertial measurement unit (10) in a singular or pair format comprises (i) angular movement of the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal joint, (iii) Varus thrust measurement in a fdtered format after elimination of soft tissue artefacts and unwanted frequencies, (iv) internal and external rotation of the joint, (v) quaternion, acceleration and gyroscopic data.

6. The wearable system (100) as claimed in claim 5, wherein the controlling unit (20) is adapted to calculate linear displacement of the joint which comprises of:(a) obtaining the calibrated data (103);(b) obtaining quaternion, acceleration and gyroscopic data (201,202) by the at least one multi-axis miniature inertial measurement unit (10) of the lower joint;(c) converting quaternion measurement into an axis angle measurement (203);(d) smoothing the obtained measurement (204,205), including removal of frequencies pertaining to soft tissue artefacts;(e) calculate multi-peak and multi-trough data from the axis angle;(f) calculate average angle by calculating difference between the peaks and troughs; and(g) calculate the linear displacement with respect to the average angle.

7. The wearable system (100) as claimed in claim 1, wherein the controlling unit (20) is adapted to calculate range of skeletal joint stability during angular displacement of the skeletal joint which comprises steps of:(a) obtaining calibrated data (103);(b) measuring internal and external rotation (301,302) by a multi -axis miniature inertial measurement unit (10) in a singular or pair format;(c) visualising the measured internal and external rotation; and(d) calculating relative orientation (303) of a multi-axis inertial measurement unit (10) in a singular or pair format about an axis passing through the upper and / or lower bone of the skeletal joint.

8. The wearable system (100) as claimed in claim 1, wherein the controlling unit (20) configurable with a force application device capable of providing consistent and standardized single or repetitive force, is adapted to calculate range of motion comprises:(a) obtaining the calibrated data (103);(b) obtaining orientation data (402a, 402b) of the upper bone and / or the lower bone by a multi-axis miniature inertial measurement unit in a singular or pair format (10), wherein the data also comprises real-time input on applied force and quality of signal from said multi-axis miniature inertial measurement unit;(c) calculating axis angle relative to time (403a, 403b, 405a, 405b);(d) smoothing the axis angle data (FT,WT);(e) averaging axis angle (404a, 406a, 404b, 406b) at local maxima at the upper bone and / or the lower joint; and(f) calculating the range of linear extension (407) by summing the local maxima at the upper bone and / or the lower joint.

9. The wearable system (100) as claimed in claim 8, wherein the force application device includes force application cum measuring mechanism configurable for manual force application or automated force applicationthrough actuators, the actuators receiving controlled feedback and operating under a closed-loop algorithm using a force sensor data.

10. The wearable system (100) as claimed in claim 5, wherein the controlling unit (20) is adapted to calculate of range of skeletal joint stability in the event of joint extension and flexion deformity to calculate proprioception comprises:(a) obtaining the calibrated data (103a, 103b);(b) obtaining orientation data (50 la, 50 lb) of the upper bone and / or the lower bone for two sets of flexion by a multi-axis miniature inertial measurement unit (10) in a singular or pair format;(c) calculating axis angle 1 and axis angle 2 (502a, 504a, 502b, 504b) for both the upper bone and the lower bone for each of two sets of flexion with respect to time;(d) smoothing the data relating to the axis angle;(e) calculating first range of motion (503a) by combining axis angle 1 of the upper bone and / or axis angle 1 of the lower bone;(f) calculating second range of motion (503b) by combining axis angle 2 of the upper bone and axis angle 2 of the lower bone;(g) calculating the proprioception (506) by subtracting the second range from the first range; and(h) calculating the fixed flexion deformity (507) by adding the lower joint and upper joint orientation static axis angles.

11. The wearable system (100) as claimed in claim 5, wherein the controlling unit (20) is adapted to calculate of range of skeletal joint stability in the event of Varus thrust comprises:(a) obtaining the calibrated data (103);(b) obtaining the static data and swing data (601) from a multi -axis miniature inertial measurement unit (10) in a singular or pair format;(c) smoothing the obtained data (603,604);(d) calculating RMS value of the stating data (605), and swing data sRMS by the controlling unit (20); and(e) calculating the Varus thrust index (607) by dividing the stating data (606) by swing data sRMS by the controlling unit (20).

12. The wearable system (100) as claimed in claim 1, wherein a multi -axis inertial measurement unit (10) in a singular or pair format is provided with an adhesive means to be able to attach to the skeletal joints.

13. The wearable system ( 100) as claimed in claim 1 , wherein the system further comprises of a negative temperature coefficient thermistor placed on the rechargeable battery pack (50) for monitoring battery temperature during charging and operation.

14. The wearable system (100) as claimed in claim 1, wherein the system further comprises plurality of LEDs controlled by the thermistor configured to serve as visual indicator comprising RGB colours for the state of the system.

15. The wearable system (100) as claimed in claim 1, wherein the wearable system optionally comprises agentic visual transformers, convolutional neural networks for generating an array of predictive analytics.

16. A method of operating a wearable system (100) for assessment of a skeletal joint stability comprises:(a) obtaining an orientation dataset associated with a multi-axis miniature inertial measurement unit (10) in a singular or pair format, wherein a multi-axis miniature inertial measurement unit (10) in a singular or pair format is placed on an upper bone and / or a lower bone that form the skeletal joint;(b) sending the orientation dataset from a multi-axis miniature inertial measurement unit (10) to a controller unit (20);(c) generating plurality of pre-calibrated signal by the controlling unit (20), wherein each of the pre-calibrated signal represents each of the orientation dataset obtained by a multi-axis miniature inertial measurement unit (10) in a singular or pair format;(d) calibrating the plurality of the pre -calibrated signals by the controlling unit (20);(e) obtaining a static dataset relating to a skeletal joint for a pre-determined time by a multi -axis miniature inertial measurement unit (10) in a singular or pair format, wherein the static dataset represents a static condition of the skeletal joint;(f) obtaining a dynamic dataset when a movement of the skeletal joint is performed; and(g) calculating plurality of parameters indicating skeletal joint stability by correlating the dynamic dataset and the static dataset, wherein the plurality of parameters include liner displacement, angular displacement, range of motion, proprioception, varus thrust, plyometric and turnout controls and such.

17. The method as claimed in claim 16, wherein the generation of precalibration signal (103) by the controlling unit comprises averaging and fusing datasets (101,102) from a sensor unit (11) and a magnetometer (12) of a multi-axis miniature inertial measurement unit ( 10) in a singular or pair format.

18. The method as claimed in claim 16, wherein the static data comprises (i) positioning of a multi-axis miniature inertial measurement unit (10) in a singular or pair format on the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal joint.

19. The method as claimed in claim 16, wherein the dynamic data obtained by a multi -axis miniature inertial measurement unit (10) in a singular or pair format comprises (i) angular movement of the skeletal joint, (ii) axis angle with respect to time in the event of linear extension as well as a flexion deformity of the skeletal joint, (iii) Varus thrust measurement in a fdtered format after elimination of soft tissue artefacts and unwanted frequencies, (iv) internal and external rotation of the joint, (v) quaternion, acceleration and gyroscopic data.

20. The method as claimed in claim 16, wherein the calculation of linear displacement of the joint by the controlling unit (20) comprises of:(a) obtaining the calibrated data (103);(b) obtaining quaternion, acceleration and gyroscopic data (201,202) by the at least one multi-axis miniature inertial measurement unit (10) on the lower bone;(c) converting quaternion measurement into an axis angle measurement (203);(d) smoothing the obtained measurement (204,205), including removal of frequencies pertaining to soft tissue artefacts;(e) calculate multi-peak and multi -trough data from the axis angle;(f) calculate average angle by calculating difference between the peaks and troughs; and(g) calculate the linear displacement with respect to the average angle.

21. The method as claimed in claim 16, wherein calculation of range of skeletal joint stability by the controlling unit (20) during angular displacement of the skeletal joint comprises:(a) obtaining calibrated data (103);(b) measuring internal and external rotation (301,302) by a multi-axis miniature inertial measurement unit ( 10) in a singular or pair format;(c) visualising the measured internal and external rotation; and(d) calculating relative orientation (303) of a multi -axis inertial measurement unit (10) in a singular or pair format about an axis passing through the upper and / or lower bone of the skeletal joint.

22. The method as claimed in claim 16, wherein calculation of range of motion by the controlling unit (20) configurable with a force application device capable of providing consistent and standardized single or repetitive force, comprises:(a) obtaining the calibrated data (103);(b) obtaining orientation data (402a, 402b) of the upper bone and / or the lower bone by a multi-axis miniature inertial measurement unit in a singular or pair format (10); wherein the data also comprises realtime input on applied force and quality of signal from said multiaxis miniature inertial measurement unit(c) calculating axis angle relative to time (403a, 403b, 405a, 405b);(d) smoothing the axis angle data (FT,WT);(e) averaging axis angle (404a, 406a, 404b, 406b) at local maxima at the upper bone and / or the lower joint; and(f) calculating the range of linear extension (407) by summing the local maxima at the upper bone and / or the lower joint.

23. The method as claimed in claim 16, wherein calculation of range of skeletal joint stability in the event of flexion deformity to calculate proprioception comprises:(a) obtaining the calibrated data (103a, 103b);(b) obtaining orientation data (501a, 501b) of the upper bone and / or the lower bone for two sets of flexion by a multi-axis miniature inertial measurement unit (10) in a singular or pair format;(c) calculating axis angle 1 and axis angle 2 (502a, 504a,502b, 504b) for both the upper bone and the lower bone for each of two sets of flexion with respect to time;(d) smoothing the data relating to the axis angle;(e) calculating first range of motion (503a) by combining axis angle 1 of the upper bone and / or axis angle 1 of the lower bone;(f) calculating the proprioception (506) by subtracting the second range from the first range; and(g) calculating the fixed flexion deformity (507) by adding the lower joint and upper joint orientation static axis angles.

24. The method as claimed in claim 16, wherein calculation of range of skeletal joint stability in the event of Varus thrust comprises:(a) obtaining the calibrated data (103);(b) obtaining the static data and swing data (601) from a multi -axis miniature inertial measurement unit (10) in a singular or pair format;(c) smoothing the obtained data (603,604); (d) calculating RMS value of the stating data (605), and swing data sRMS by the controlling unit (20); and(e) calculating the Varus thrust index (607) by dividing the stating data (606) by swing data sRMS by the controlling unit (20).

Images