Arrangement for knee diagnostics
Field
The invention relates to an arrangement for knee diagnostics.
Background
The knee joint can be affected by several conditions severely reducing its mobility or even leading to working disability. Common pathologic conditions affecting the knee joint are osteoarthritis and other traumatic- or inflammatoryrelated diseases inducing deterioration of the joint. The most common one of these conditions is knee osteoarthritis (OA) affecting approximately 10 % of population.
Knee OA involves multiple doctor appointments and expensive imaging examinations, often in specialized healthcare, due to its challenging diagnosis. For more information on osteoarthritis, see the following, incorporated herein by reference: Altman RD (1987). Overview of osteoarthritis. Am J Med. 83: 65-69. For more information on diagnosis, see the following, incorporated herein by 15 reference: Gunther KP, Sun Y (1999). Reliability of radiographic assessment in hip and knee osteoarthritis. Osteoarthritis and Cartilage 7: 239-46.
This complex disorder has been long recognized as a major public health problem: in addition to the deterioration of the quality of individuals’ life, it generates significant costs to society. First clinical symptoms of knee OA include 20 pain during joint movement, e.g., when running or walking in stairs. Subsequently, when the disease gets worse, pain will occur also during rest and the knee mobility will be significantly reduced. At the final stage, pain is intolerable and the knee mobility is highly limited, making survival of routine daily activities highly difficult. The only treatment at this stage is the complete knee replacement surgery, which 25 is major and relatively expensive operation requiring specialized healthcare.
While complete pharmaceutical cure of knee OA does not currently exist, the progression of the disease could be hindered by an early stage diagnosis. Similarly to knee OA, the diagnostics of other knee conditions suffers comparable issues due to their subjective assessment and can also lead to knee OA if not treated 30 properly, see the following, incorporated herein by reference: Culvenor AG,
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Crossley KM (2016). Accelerated return to sport after anterior cruciate ligament injury: a risk factor for early knee osteoarthritis? BrJ Sports Med. 50(5):260-1.
The primary drawbacks of current clinical diagnostics of knee conditions are: time to get the final diagnosis can be long, both direct and indirect 5 costs related to non-diagnosed knee conditions are high, and false detection allows the knee joint to deteriorate further.
Eventually, a late diagnosis of knee OA reduces the available treatment options. On the other hand, for other knee conditions, a late diagnostics often cause the apparition to OA. From an economic point of view, alternative low-cost 10 solutions could replace some unnecessary and expensive clinical examinations at the specialized healthcare related to knee diagnostics.
At the moment, the assessment of knee conditions in the primary healthcare is performed using clinical (physical) examination, X-ray imaging and assessment of symptoms (pain and limited joint movement). However, it is often 15 difficult for a general practitioner to provide an objective and accurate diagnosis due to the insensitivity of clinical examination and X-ray imaging to tissue changes, especially in the case of soft tissues (ligaments, articular cartilage, menisci). Consequently, a patient with knee complaints is quite often referred to a specialized healthcare unit where more comprehensive evaluation of the knee joint 20 is possible, e.g., by using magnetic resonance imaging (MR1) or invasive knee arthroscopy.
In summary, at the moment, accurate diagnosis of early knee OA is not possible at the primary healthcare as it requires advanced techniques, i.e. expensive MR1 or invasive arthroscopy, which are not available and are typically 25 performed at a later stage of the disease at the specialized healthcare.
US 2016/0015280 discloses epidermal electronics to monitor repetitive stress injuries and arthritis, but without any specific frame structures.
US 2013/0211259 discloses determination of joint condition based on vibration analysis, disclosing a two-part brace limiting the knee movement.
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Brief description
The present invention seeks to provide an improved arrangement for knee diagnostics.
According to an aspect of the present invention, there is provided an 5 arrangement as specified in claim 1.
According to another aspect of the present invention, there is provided a measurement apparatus as specified in claim 17.
According to another aspect of the present invention, there is provided a processing apparatus as specified in claim 18.
The present invention may provide the advantage of providing knee diagnostics in a non-invasive manner with a multi-modal data analysis. The present invention may allow to perform low-cost, comprehensive and efficient early knee OA diagnostics already at the primary healthcare. Furthermore, the present invention may allow to diagnose other knee conditions as well, such as anterior cruciate ligament (ACL) injury.
List of drawings
Example embodiments of the present invention are described below, by way of example only, with reference to the accompanying drawings, in which
Figure 1 illustrates example embodiments of an arrangement for knee diagnostics comprising a measurement apparatus attachable to a leg, and a processing apparatus communicatively couplable with the measurement apparatus;
Figure 2 is a flow chart illustrating example embodiments of processing in the processing apparatus;
Figure 3 illustrates example embodiments of the measurement apparatus comprising a first frame, a second frame, and a third frame;
Figure 4 illustrates example embodiments of the second frame;
Figure 5 illustrates example embodiments of the third frame;
Figure 6 illustrates example embodiments of the first frame; and
Figures 7, 8 and 9 illustrates example embodiments of sensor frames.
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Description of embodiments
The following embodiments are only examples. Although the specification may refer to “an embodiment in several locations, this does not necessarily mean that each such reference is to the same embodiment(s), or that 5 the feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments. Furthermore, words comprising and including should be understood as not limiting the described embodiments to consist of only those features that have been mentioned and such embodiments may contain also features/structures that have not been 10 specifically mentioned.
Figure 1 illustrates example embodiments of an arrangement 100 for knee diagnostics comprising a measurement apparatus 110 attachable to a leg 300, and a processing apparatus 140 communicatively couplable 160 with the measurement apparatus 110.
In an example embodiment, the processing apparatus 140 is a computing device. It may be portable, mobile or stationary. A non-limiting list of example embodiments of the processing apparatus 140 comprises: a computer, a portable computer, a laptop, a mobile phone, a smartphone, a tablet computer, a smartwatch, smartglasses, or any other portable/mobile/stationary computing 20 device, which may output knee diagnosis with a user interface 150.
In an example embodiment, the processing apparatus 140 is a computing server. It may be implemented with any applicable technology. It may include one or more centralized computing apparatuses, or it may include more than one distributed computing apparatuses. It may be implemented with client25 server technology, or in a cloud computing environment, or with another technology applicable to the processing apparatus 140 capable of communicating 160 with the measurement apparatus 110.
In an example embodiment, the arrangement 100 may be an independent integrated apparatus comprising as its parts the measurement 30 apparatus 110 and the processing apparatus 140.
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In an example embodiment, the arrangement 100 is sold as a product in itself, or the arrangement 100 is marketed as a service per use of the device (the analysis of the signal is performed remotely and the results are sent back to the customer).
In an example embodiment, different customers are considered for the arrangement 100:
1) The primary healthcare (both public and private). At the public healthcare, the apparatus 110 and service 140 is used already in the health centres and the test itself is supervised by a nurse or other trained person. At the private healthcare, big health clinics as well as private physiotherapists are the first targeted customers. The arrangement 100 provides a complementary source of information to the practitioner for the diagnosis of early and middle stage knee OA. The easy access to this information already at the primary healthcare prevents extra expenses related to unnecessary advanced examinations and doctor 15 appointments at the specialized healthcare.
2) Sports centres to assess the quality of the knee of athletes.
3) Companies developing orthopaedic devices to validate the design of their product from follow-up populations.
4) Personal users who desire to know the state of their knee.
In an example embodiment, the user interface 150 implements the exchange of graphical, textual and/or auditory information relating to knee diagnostics with the user. The user interface 150 may be realized with various techniques, such as a (multi-touch) display, means for producing sound (such as loudspeaker or earpiece), a keyboard, and/or a keypad, for example. The keyboard/keypad may comprise a complete (QWERTY) keyboard, or only a few push buttons and/or rotary buttons. In addition, or alternatively, the user interface 150 may comprise other user interface components, for example various means for focusing a cursor (mouse, track ball, arrow keys, touch sensitive area etc.) or elements enabling audio control.
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In an example embodiment, the communication between the measurement apparatus 110 and the processing apparatus 140 may be implemented with wired and/or wireless communication technologies.
Figure 2 illustrates example embodiments of processing in the 5 processing apparatus 140.
Figure 3 illustrates example embodiments of the measurement apparatus 110 comprising a first frame 116, a second frame 112, and a third frame 130.
In an example embodiment, the frames 112, 116, 130 are made of suitable material, such as metal, plastics, and/or composite.
In an example embodiment, the frames 112, 116, 130 are made of 3D printed material and covered by a specific tissue commonly used in orthopaedic industry for the patient comfort. Some aluminium may be added in some parts of the frames 112,116,130 to increase its strength and decrease its fragility.
In an example embodiment, all sensors 114, 118, 120, 122, 124, 126,
132 may be embedded in printed frames, which may be changed very easily by placing them in a sensor frame 362, 364, 366, 368, 610 couplable with the frames 112, 116,130.
One benefit of having the frames 112,116,130 compared to some other studies using either tapes or just straps is that the locations of the sensors 114,118, 120,122,124,126,132 is quite reproducible not only between nurses who might put it on patients, but also anatomically between patients. In an example embodiment, a coupling 334 connecting the first frame 116 and the second frame 130 maintains the distance between the frames 116,130.
In an example embodiment, the measurement apparatus 110 further comprises a radio transmitter 134 to communicatively couple 160 the measurement apparatus 110 with the processing apparatus 140.
In an example embodiment, the processing apparatus 110 also comprises a radio receiver (or a radio transceiver) to communicatively couple 160 30 the measurement apparatus 110 with the processing apparatus 140.
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In an example embodiment, the radio transmitter is a part of a radio transceiver. In an example embodiment, the radio transceiver comprises a cellular radio transceiver (communicating with technologies such as GSM, GPRS, EGPRS, WCDMA, UMTS, 3GPP, IMT, LTE, LTE-A, etc.) and/or a non-cellular radio 5 transceiver (communicating with short-range technologies such as Bluetooth, Bluetooth Low Energy, Wi-Fi, WLAN, etc.). With the cellular radio transceiver, the measurement apparatus 110 and the processing apparatus 140 may be distributed so that they are located in the same town, in different towns, or even in different continents. With the non-cellular radio transceiver, the measurement apparatus 10 110 and the processing apparatus 140 need to be near each other, in the same room or in the same building, for example, except if there is a communication network in between (such as a wireless access point connected to the Internet), then the distribution degree may be the same as with the cellular radio transceiver. Note that the use of the cellular radio transceiver may necessitate the use of a subscriber 15 identity module (SIM), and, consequently, the measurement apparatus 110 comprises a SIM card in a card reader, or a virtual (or software) SIM.
Note that the arrangement 100 may comprise other parts as well, which have not been described, but are naturally there: the measurement apparatus 110 comprises a power source (such as battery, which may be rechargeable) to feed 20 electric energy to the sensors 114,118,120,122,124,126,128,132, and also an interface, which collects the measurement data from the sensors 114, 118, 120, 122,124,126,128,132 to communicate the measurement data to the processing apparatus 140. The measurement data may be raw data from the sensors 114,118, 120,122,124,126,128, 132, or it may be pre-processed before communicated to 25 the processing apparatus 140.
In an example embodiment, the measurement apparatus 110 is designed to be totally non-invasive and painless to use.
In an example embodiment, the measurement apparatus 110 is divided into three main solid frames 112,116,130 covered with a protective textile (with 30 inner foam) for comfort of use. Each of the frames 112,116,130 may be designed
20165832 prh 20 -11- 2018 to fit the shape of the area it covers in order to ease its positioning and improve the reproducibility of data acquisition with fixed sensors.
The first frame 116 is attachable to a knee 306 and comprises a medial audio sensor 124 positioned and configured to measure a medial sound caused by 5 a movement of the leg 300 from a medial side of the knee 306, and a lateral audio sensor 126 positioned and configured to measure a lateral sound caused by the movement of the leg 300 from a lateral side of the knee 306.
In an example embodiment, the medial audio sensor 124 comprises a non-contact microphone 230, and the lateral audio sensor 126 comprises a non10 contact microphone 230.
In an example embodiment, the medial audio sensor 124 and the lateral audio sensor 126 are both located below the patella 308.
The first frame 116 also comprises a medial thermal sensor 120 positioned and configured to measure a medial temperature from the medial side 15 of the knee 306, and a lateral thermal sensor 122 positioned and configured to measure a lateral temperature from the lateral side of the knee 306.
In an example embodiment, the medial thermal sensor 120 comprises a non-contact infrared sensor 234, and the lateral thermal sensor 122 comprises a non-contact infrared sensor 234.
As the sensors 118,120,122 124,126 are non-contact, there is no need to use gel, making the measurement apparatus 110 easier to use.
The second frame 112 is attachable to a thigh 302 and comprises a thigh inertial sensor 114 positioned and configured to measure thigh inertial data. Figure 4 illustrates an example embodiment of the structure of a main part 310 and 25 a strap 312 of the second frame 112.
The third frame 130 is attachable to a lower leg 304 and comprises a lower leg inertial sensor 132 positioned and configured to measure lower leg inertial data. Figure 5 illustrates an example embodiment of the structure of a main part 320 and a strap 322 of the third frame 130.
In an example embodiment, the thigh inertial sensor 114 comprises a six degrees of freedom inertial measurement unit 232, and the lower leg inertial
20165832 prh 20 -11- 2018 sensor 132 comprises a six degrees of freedom inertial measurement unit 232. Six degrees of freedom refers to the freedom of movement of a rigid body in threedimensional space: change position as forward/backward (surge), up/down (heave), left/right (sway) translation in three perpendicular axes, combined with 5 changes in orientation through rotation (pitch, yaw, and roll) about three perpendicular axes.
In an example embodiment, the inertial measurement sensor 114,132 detects rate of acceleration using one or more accelerometers, and detects changes in rotational attributes (pitch, yaw and roll) using one or more gyroscopes.
In an example embodiment, multiple sizes of the second frame 112 and the third frame 130 are available based on the diameter of the thigh 302 and the lower leg 304.
In an example embodiment, one or two flexible straps 312, 322, 358, 360 are used to maintain the frames 112,116,130 in place.
In an example embodiment, the second frame 112 and the third frame
130 have the function to prevent the inertial sensors 114,132 to be affected by skin movements since one inertial sensor 114,132 is fixed on each frame 112,130.
The second frame 112 and the third frame 130 are rigid so as not to be affected by skin movements and have a better normalization of the data measured 20 with the sensors 114,132.
In an example embodiment illustrated in more detail in Figure 6, the first frame 116 comprises a stable part 340 attachable to the lower leg, and a moving part 342, which is coupled with the stable part 340 to be positionable above a patella 308, and comprising the medial audio sensor 124, the lateral audio 25 sensor 126, the medial thermal sensor 120, and the lateral thermal sensor 122.
In an example embodiment, the moving part 342 is coupled with the stable part 340 by a hinge 344 below the knee 306, by a first spring 600 at the medial side of the knee 306, and by a second spring 346 at the lateral side of the knee 306.
In an example embodiment, a tension of the first spring 600 is adjustable, and a tension of the second spring 346 is adjustable. In an example
20165832 prh 20 -11- 2018 embodiment, the tension of the springs 346, 600 may be adjusted by shifting a fixing 356, 604 of the spring 346, 600 in a slot 606, 608 as illustrated in Figure 6.
In an example embodiment, a first position of a first fixing 602 of the first spring 600 is adjustable, and a second position of a second fixing 348, 350, 5 352, 354 of the second spring 346 is adjustable. With this example embodiment, the fit of the moving part 342 above the patella 308 may be adjusted.
In an example embodiment, the stable part 340 may be attached on the upper tibia 304 by the mean of two straps 358, 360. In an example embodiment, the moving part 342 is constantly pulled towards the patella 308 by the tensile 10 springs 346, 600. By these means, the patient may walk and perform any movement that is requested by the data acquisition protocol without any discomfort.
In an example embodiment, the moving part 342 is always kept on the top of the skin without affecting the comfort of the patient. The first frame 116 15 keeps the sensors 118,120,122,124,126 always at the same location on the knee 306 (and at the same distance to the skin), without restraining the patient in any way, meaning s/he can moves his/her knee 306 the same way he would without the measurement apparatus 110.
In an example embodiment, the first frame 116 is configured to provide 20 a space between the medial audio sensor 124 and skin, the lateral audio sensor 126 and skin, the medial thermal sensor 120 and skin, and the lateral thermal sensor 122 and skin.
The structure of the first frame 116, and the placement of the audio sensors 124,126 thereto avoids the generation of acoustic artefact.
In an example embodiment, three sensor frames 362, 368,610 are fixed on the moving part 342, but may be removed to ease the change of sensors 118, 120, 122, 124, 126. These frames 362, 368, 610 have been designed to keep the sensors 118,120,122,124, 126 at a constant distance from the skin of the user.
Figures 7, 8 and 9 illustrates example embodiments of sensor frames 30 362, 364, 366, 368, 612.
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As shown in Figure 7, the sensor frame 362, 610 accommodates the thermal sensor 120/122 in a first slot 712, and the audio sensor 124/126 in a second slot 714. Ridges 706, 708 couple with grooves of the first frame 116 to keep the sensor frame 362, 610 aligned when being placed in a half sphere -shaped 5 aperture in the first frame 116. Half sphere allows the rotation of the sensor frame 362,610 to keep it aligned to the skin. This rotation is regulated by a curvature 710 (there is a counterpart structure in the first frame 116 to follow the curvature 710).
There is also a half sphere aperture 704 in the sensor frame 362, 610 to keep the sensor frame 362, 610 in position (there is a counterpart structure in 10 the first frame 116 to couple with the half sphere aperture 704).
Slits 700, 702 on the sides of the sensor frame 362, 610 may be used to pass wires of the sensors 120,122,124,126, which enables their easy replacement.
As shown in Figure 7, a groove 716 in the slots 712, 714 may be used to regulate the insertion depth of the sensors 120,122,124,126.
Figure 8 illustrates example embodiments of the sensor frame 368. The reference thermal sensor 118 is inserted into an aperture 800. Wires of the sensor 118 may be routed through a slit 802. A groove 804 limits the insertion depth of the sensor 118 into the aperture 800.
Figure 9 illustrates example embodiments of the sensor frame 364, 366.
The thigh inertial sensor 114 or the lower leg inertial sensor 132 is inserted into an aperture 900.
The processing apparatus 140 comprises one or more processors 144, and one or more memories 146 including computer program code 148.
The term 'processor' 144 refers to a device that is capable of processing data. Depending on the processing power needed, the processing apparatus 140 may comprise several processors 144 such as parallel processors or a multicore processor. When designing the implementation of the processor 144 a person skilled in the art will consider the requirements set for the size and power consumption of the processing apparatus 140, the necessary processing capacity, production costs, and production volumes, for example.
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The term 'memory' 146 refers to a device that is capable of storing data run-time (= working memory) or permanently (= non-volatile memory). The working memory and the non-volatile memory may be implemented by a randomaccess memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), a flash memory, 5 a solid state disk (SSD), PROM (programmable read-only memory), a suitable semiconductor, or any other means of implementing an electrical computer memory.
The processor 144 and the memory 146 may be implemented by an electronic circuitry. A non-exhaustive list of implementation techniques for the 10 processor 144 and the memory 146 includes, but is not limited to: logic components, standard integrated circuits, application-specific integrated circuits (ASIC), system-on-a-chip (SoC), application-specific standard products (ASSP), microprocessors, microcontrollers, digital signal processors, special-purpose computer chips, field-programmable gate arrays (FPGA), and other suitable 15 electronics structures.
The computer program code 148 may be implemented by software and/or hardware. In an example embodiment, the software may be written by a suitable programming language (a high-level programming language, such as C, C++, or Java, or a low-level programming language, such as a machine language, or 20 an assembler, for example), and the resulting executable code 148 may be stored in the memory 146 and run by the processor 144. In an alternative example embodiment, the functionality of the hardware may be designed by a suitable hardware description language (such as Verilog or VHDL), and transformed into a gate-level netlist (describing standard cells and the electrical connections between 25 them), and after further phases the chip implementing the processor 144, memory 146 and the code 148 of the processing apparatus 100 may be fabricated with photo masks describing the circuitry.
The one or more memories 146 and the computer program code 148 are configured to, with the one or more processors 144, cause the processing 30 apparatus 140 at least to:
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- analyze 202 the medial sound and the lateral sound to produce an assessment 204 of a joint friction of the knee 306;
- analyze 206 the thigh inertial data and the lower leg inertial data to produce an assessment 208 of a malalignment of the knee 306;
- analyze 210 the medial temperature and the lateral temperature to produce an assessment 212 of an inflammation of the knee 306; and
- compile 214 an assessment 216 of a condition of the knee 306 based on the assessment 204 of the joint friction of the knee 306, the assessment 208 of the malalignment of the knee 306, and the assessment 212 of the inflammation of the knee 306.
The data acquisition is performed by acquiring signals from the knee joint 306 using three modalities: acoustic (microphones on medial and lateral sides), thermal (temperature) in three locations (non-contact infrared sensors) and kinetic (6 degrees of freedom inertial measurement units). The arrangement 15 100 combines these three modalities together for the diagnostics of knee disorders.
The basis of choosing these modalities are as follows:
- Acoustic modality assesses the friction between the femoral and tibial cartilage, see the following, incorporated herein by reference: Mascaro Bl, Prior J, Shark LK, Selfe J, Cole P, Goodacre J (2009). Exploratory study of a non-invasive method based on acoustic emission for assessing the dynamic integrity of knee joints. Med Eng Phys. 31(8): 1013-22.
- Thermal modality assesses the inflammation of the knee joint, see the following, incorporated herein by reference: Ammer K (2012). Temperature of the human knee - a review. Thermology international 22(4): 137-51.
- Kinetic modality provides information on knee malalignment as a measure of varus/valgus angle within the joint, see the following, incorporated herein by reference: Chang A, Hochberg M, Song J, Dunlop D, et al. (2010). Frequency of varus and valgus thrust and factors associated with thrust presence in persons with or at higher risk of developing knee osteoarthritis. Arthritis Rheum.
62(5):1403-11.
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During the signal acquisition, the user has to perform specific tasks (e.g. leg flexion ant extension, sit-to stand, walking, etc.) to collect the relevant information for each sensor 114, 118,120,122,124,126, 132.
After the signal acquisition, the automatic multi-modal data analysis is performed and relevant features for each modality are extracted (e.g. differences in temperatures, amount of acoustic emissions above a given threshold, varus/valgus angle, etc.).
In an example embodiment, in addition to the signal data, other variables such as age and body mass index are incorporated to the diagnostics 10 algorithm. The final diagnostics may be performed using machine learning techniques. Eventually, different algorithms may be used for classification. Additional diagnostic information may be provided by visualizing the measured data on a 2-dimensional plot using unsupervised machine learning methods. Such plot indicates the belonging of the analyzed subject to specific clusters 15 corresponding to different conditions, such as early OA, mid-stage OA, end-stage OA, etc.
As a final result, a diagnosis of a knee condition may be provided with an appropriate confidence interval. The diagnosis, naturally, may be subjected to an approval by authorized medical personnel.
In an example embodiment, patient clinical background information (e.g. pain map + clinical evaluation), medical imaging information (X-ray etc.) may be combined with the assessment 216 of the condition of the knee 306 in order to make a more specific diagnosis and prediction of knee condition.
As shown in Figure 1, a database 152 coupled with the processing 25 apparatus 140 may be used to store data processed by the arrangement 100.
In an example embodiment, the processing apparatus 140 is further caused to analyze 206 the thigh inertial data and the lower leg inertial data to produce a varus/valgus angle 226 of the knee 306 as a part of the assessment 208 of the malalignment of the knee 306. As was explained earlier, the inertial sensors 30 114,132 may provide raw data provided by both the accelerometer and gyroscope of each inertial sensor 114, 132. Then, the processed data of the thigh inertial
20165832 prh 20 -11- 2018 sensor 114 is related with the data from the lower leg inertial sensor 132 to assess the varus/valgus angle 226.
In an example embodiment, the processing apparatus 140 is further caused to analyze the thigh inertial data and the lower leg inertial data to produce 5 a flexion angle 224 of the knee 306 as a part of the assessment 208 of the malalignment of the knee 306.
In an example embodiment, the processing apparatus 140 is further caused to analyze the thigh inertial data and the lower leg inertial data to produce an assessment 228 of a ligament laxity of the knee 306 as a part of the assessment 10 208 of the malalignment of the knee 306.
In an example embodiment, the processing apparatus 140 is further caused to analyze the thigh inertial data and the lower leg inertial data to produce an assessment 220 of a deficiency in a cartilage of the knee 306 and/or an assessment 222 of a deficiency in a meniscus of the knee 306 as a part of the 15 assessment 208 of the malalignment of the knee 306.
In an example embodiment, the processing apparatus 140 is further caused to analyze the medial sound and the lateral sound to produce an assessment 220 of a deficiency in a cartilage of the knee 306 and/or an assessment 222 of a deficiency in a meniscus of the knee 306 as a part of the assessment 204 of the joint 20 friction of the knee 306.
In an example embodiment, the measurement apparatus 110 further comprises a reference thermal sensor 118 positioned and configured to measure a reference temperature from a patella 308, and the processing apparatus 140 is further caused to analyze 210 the medial temperature and the lateral temperature 25 in view of the reference temperature in order to produce the assessment 212 of the inflammation of the knee 306. Instead, or additionally, the processing apparatus 140 may further be caused to analyze 210 the medial temperature and the lateral temperature in view of each other in order to produce the assessment 212 of the inflammation of the knee 306.
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In an example embodiment, the reference thermal sensor 118 is positioned and configured on top of the centre of the patella 308 and it is used as reference for skin temperature.
In an example embodiment, the measurement apparatus 110 further comprises a vibration sensor 128 positioned and configured to measure vibration caused by the movement of the leg 300, and the processing apparatus 140 is further caused to analyze 236 the vibration to produce an assessment 238 of an instability of the knee 300, and compile 214 the assessment 216 of the condition of the knee 306 also based on the assessment 238 of the instability of the knee 306.
In an example embodiment, illustrated also in Figure 2, a method for knee diagnostics is provided. The operations are not strictly in chronological order, and some of the operations may be performed simultaneously or in an order differing from the given ones. Other functions may also be executed between the operations or within the operations and other data exchanged between the 15 operations. Some of the operations or part of the operations may also be left out or replaced by a corresponding operation or a part of the operation. It should be noted that no special order of operations is required, except where necessary due to the logical requirements for the processing order. Note that also Figure 1 is referred to while explaining Figure 2.
The method starts in 200.
The method for knee diagnostics comprises:
analyzing 202 a medial sound and a lateral sound to produce an assessment 204 of a joint friction of the knee 306, wherein, both caused by a movement of the leg 300, the medial sound is measured with a medial audio sensor 25 124, and the lateral sound is measured with a lateral audio sensor 126;
analyzing 206 a thigh inertial data and a lower leg inertial data to produce an assessment 208 of a malalignment of the knee 306, wherein, both caused by a movement of the leg 300, the thigh inertial data is measured with a thigh inertial sensor 114, and the lower leg inertial data is measured with a lower 30 leg inertial sensor 132;
analyzing 210 a medial temperature and a lateral temperature to produce an assessment 212 of an inflammation of the knee 306, wherein the medial temperature is measured with a medial thermal sensor 120, and the lateral temperature is measured with a lateral thermal sensor 122; and compiling 214 an assessment 216 of a condition of the knee 306 based on the assessment 204 of the joint friction of the knee 306, the assessment 208 of the malalignment of the knee 306, and the assessment 212 of the inflammation of the knee 306.
The method ends in 218.
The example embodiments of the arrangement 100, measurement apparatus 110, and the processing apparatus 140 may be utilized to enhance the method with various further example embodiments. For example, various structural and/or operational details may supplement the method.
In an example embodiment, the sensors 114, 118, 120, 122, 124, 126,
132 are constituent parts of a measurement apparatus 110 attachable to the leg 300.
It will be obvious to a person skilled in the art that, as technology advances, the inventive concept can be implemented in various ways. The invention and its embodiments are not limited to the example embodiments 20 described above but may vary within the scope of the claims.