WRIST RIGIDITY ASSESSMENT DEVICE AND METHOD FOR IDENTIFYING A CLINICALLY EFFECTIVE DOSE

Abstract

An articulation rigidity assessment device comprises a single-axis angular velocity sensor attachable to a limb such that an axis of measurement is parallel to a predetermined rotation axis of a bending motion imposed during a dosage administration regime of a drug in order to identify a clinically effective dose as having been administered. A data processor is configured to process an angular velocity sensor signal during the dosage administration, calculate a non-rigidity index of the articulation using the processed signal, wherein the non-rigidity index is the square root of the multiplication of the average of the angular velocity signal by the average peak value of the angular velocity signal. The device outputs feedback of the non-rigidity index at a current dose of the drug, whereby the clinically effective dose is identified as having been administered as a function of the output. A method is also disclosed.

Claims

1. An articulation rigidity assessment device for assessing the rigidity of an articulation when a bending motion is imposed to a limb of said articulation around a predetermined rotation axis of the articulation, during a dosage administration regime of a drug to a subject in order to identify a clinically effective dose as having been administered, said device comprising: a single-axis angular velocity sensor for attaching to said limb such that the axis of measurement is parallel to the axis of rotation of the imposed bending motion; and a data processor configured to process the signal of the angular velocity sensor during the dosage administration regime of the drug, calculate a non-rigidity index of the articulation using the processed angular velocity signal, non-rigidity index which is the square root of the multiplication of the average of the angular velocity signal by the average peak value of the angular velocity signal, and output feedback of the non-rigidity index at a current dose of the drug, whereby the clinically effective dose is identified as having been administered as a function of the output.

2. The device according to claim 1, further comprising a skin-contacting patch, wherein the single-axis angular velocity sensor is attached to said skin-contacting patch.

3. The device according to claim 1, wherein the articulation is a wrist articulation of a patient and the limb is the respective hand.

4. The device according to claim 1, wherein the axis of rotation of the imposed bending motion is the axis of rotation of extension-flexion of a wrist articulation.

5. The device according to claim 2, wherein the skin-contacting patch is a skin-contacting patch for applying to a palm or back of a hand.

6. The device according to claim 1, wherein the data processor is configured to calculate a non-rigidity index for a cycle of the imposed bending motion by the square root of the multiplication of the average of the angular velocity signal by the average peak value of the angular velocity signal.

7. The device according to claim 1, wherein the data processor is configured to distinguish between non-rigid and rigid states by detecting a non-rigid state if the calculated non-rigidity index is above a predetermined threshold.

8. The device according to claim 1, wherein the data processor is configured to calculate a quantitative continuous scale of the rigidity of the articulation using a polynomial function whose input is the non-rigidity index.

9. The device according to claim 1, wherein the data processor is configured to detect cogwheel rigidity of the articulation by detecting a non-minima valley bordered by two peaks of the angular velocity signal along a cycle of the imposed bending motion.

10. The device according to claim 9, wherein the configured data processor detects non-minima valleys, each of the valleys bordered by two peaks of the angular velocity signal, by: extracting all the peaks and valleys of the angular velocity signal along time; drawing each possible triangle between a valley and the two peaks enclosing it; and determining if the following calculation is true: $\frac{h}{.Math..Math.t.Math.A}$ wherein h is the distance between the valley and the midpoint between the two peaks, t is the time span of the triangle formed by the valley and the two peaks, A is the triangle area and is a predetermined threshold value for the detection of cogwheel rigidity.

11. The device according to claim 1, wherein the single-axis angular velocity sensor is a single-axis gyroscope.

12. The device according to claim 1, wherein the data processor is configured to pre-process the angular velocity sensor signal by filtering the angular velocity sensor signal with a moving average of the absolute value of the signal.

13. The device according to claim 2, further wherein the skin-contacting patch is an adhesive patch.

14. The device according to claim 2, further comprising a fingerless glove wherein the skin-contacting patch is an integral part of said glove.

15. The device according to claim 2, further comprising an elastic textile band wherein the skin-contacting patch is an integral textile part of said band.

16. The device according to claim 1, further comprising a display attached to the data processor, wherein the data processor is connected wirelessly to the angular velocity sensor and the data processor is arranged to output a real-time feedback of the non-rigidity index through said display.

17. The device according to claim 1, further comprising a display connected wirelessly to the data processor, wherein the data processor is electrically connected to the angular velocity sensor and the data processor is attached to the skin-contacting patch, and the data processor is arranged to output a real-time feedback of the non-rigidity index through said display.

18. A method for adjusting a medication dose administered to a subject in a dosage administration regime until a clinically effective amount has been reached, comprising the steps of: providing an articulation rigidity assessment device for assessing the rigidity of an articulation of the subject when a bending motion is imposed to a limb of said articulation around a predetermined rotation axis of the articulation, said device comprising a single-axis angular velocity sensor for attaching to said limb such that the axis of measurement is parallel to the axis of rotation of the imposed bending motion; and a data processor configured to process the signal of the angular velocity sensor during the dosage administration regime of the drug, calculate a non-rigidity index using the processed angular velocity signal, non-rigidity index which is the square root of the multiplication of the average of the angular velocity signal by the average peak value of the angular velocity signal, and output feedback of the non-rigidity index at a current dose of the drug; providing an initial dosage to the subject and assessing articulation rigidity using the articulation rigidity assessment device while a bending motion is imposed to the limb of the articulation; and providing a further dosage to the subject and assessing articulation rigidity using the articulation rigidity assessment device while a bending motion is imposed to the limb of the articulation, which is repeated until a clinically effective dose is identified as having been administered as a function of the output of the assessment of the further dosage.

19. The method according to claim 18, wherein a clinically effective dose is identified as having been administered if the output of the assessment of the initial dosage is a non-rigidity index below a first threshold and the output of the assessment of the further dosage is a non-rigidity index above a second threshold.

20. The method according to claim 18, wherein a clinically effective dose is identified as having been administered if the output of the assessment of a first further dosage is a non-rigidity index below a first threshold and the output of the assessment of a second further dosage, which is subsequent to the first further dosage, is a non-rigidity index above a second threshold.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The following figures provide preferred embodiments for the present disclosure and should not be seen as limiting the scope of the disclosure.

[0049] FIG. 1: Set up of the motion sensor and its placement on the hand during wrist rigidity assessment. The shown coordinate system is relative to the device, not the world.

[0050] FIG. 2: Illustration of the signal processing strategies followed.

[0051] FIG. 2a: The average angular velocity (upper dotted line) and the average peak value (lower dotted line) were extracted to describe the kinematics of the passive wrist flexion movement. The range of possible values for the signal descriptor, is also represented.

[0052] FIG. 2b: The cogwheel effect was detected using a geometric approach that defines triangles from fiducial points on the angular velocity signal. On the left, it is shown the difference between a smooth part of the signal (larger triangle) and a zone in which the cogwheel effect exists (smaller and tilted triangle). On the right, the features extracted from the drawn triangles are described.

[0053] FIG. 2c: Schematic representation of the wrist articulation flexion-extension axis of rotation, corresponding to the mentioned y-axis.

[0054] FIG. 3a: The polynomial function that best correlates the wrist rigidity and the average value of the signal descriptorfor each rigidity scale on the training dataset.

[0055] FIG. 3b: The ROC curve for the detection of cogwheel artefacts on the angular velocity signal. Optimal operating for =100.

[0056] FIG. 4: Schematic representation depicting the main blocks of an embodiment, in which IMU represents an inertial measurement unit and MCU represents a microcontroller Unit.

[0057] FIG. 5: Schematic representation depicting the data and workflow of an embodiment, in which IMU represents an inertial measurement unit and MCU represents a microcontroller Unit.

[0058] FIG. 6: angular velocity data of a wrist rigidity assessment recorded using a device according to an embodiment of the present disclosure for a first subject (male, 47 years) for a period of time (a) without levodopa and (b) with a clinically effective dosage regime of levodopa. Also pictured are values of a rigidity index (label) calculated according to the present disclosure. Higher index values correspond to lower perceived wrist rigidity.

[0059] FIG. 7: angular velocity data of a wrist rigidity assessment recorded using a device according to an embodiment of the present disclosure for a second subject (female, 61 years) for a period of time (a) without levodopa and (b) with a clinically effective dosage regime of levodopa. Also pictured are values of a rigidity index (label) calculated according to the present disclosure. Higher index values correspond to lower perceived wrist rigidity.

DETAILED DESCRIPTION

[0060] According to an embodiment, the designed hardware comprises a Texas Instruments Microcontroller (MCU), a Invensense's ITG-3200 gyroscope (range of 2000/s and 6.5 mA operating current), a KXTF9-1026 Kionix accelerometer (with ranges 2g, 4g and 8g) and a Honeywell's HMC5883L magnetometer (with compass heading accuracy of 1 to 2). The MCU gathers data from the sensors at 100 Hz, building packages that are transmitted via Bluetooth to a synced device at a 42 Hz rate, and can compute quaternions in real time.

[0061] The sensor signal was acquired and processed using National Instruments Labview 2014, in an Intel Core i7-4600U CPU @ 2.70 GHz computer, according to an embodiment.

[0062] Six patients (Mean Age: 67 years; 3 male and 3 female) subjected to bilateral DBS surgery were tested and a total of 48 signals was acquired to train a rigidity classification model. Medication was withdrawn for 12 h prior to the procedure and local anaesthetic was administered. The DBS electrodes were inserted in the STN stereotactic target and electrophysiological inspection was performed to determine the definitive stimulation site. Stimulation frequency was fixed at 130 Hz and both voltage and electrode position were varied, while searching for the greatest reduction in wrist rigidity during passive wrist flexion without secondary effects. The optimal setting was agreed between two experienced physicians. The patients wore the developed system during the whole procedure for signal recording purposes. Additional 4 patients (Mean Age: 64 years; 2 male and 2 female) had their rigidity classified under variable stimulation settings by the present disclosure. Patients were submitted to the same medical procedure as the training group. Signal classification (156 signals as total) performance was evaluated against the agreement of two expert physicians: classifications were accepted if contained inside a 5% margin with respect to the clinical score.

[0063] It is disclosed a device and method to quantitatively evaluate wrist rigidity and help on the determination of the optimal stimulation setting. The statistical analysis results summarized in Table I demonstrated the capability of the selected kinematic measures to distinguish between rigid and non-rigid states. Furthermore, it was observed that has a slightly more discriminative (p.sub.=0.027) than its counterparts (p.sub.=0.034 and p.sub.P=0.029). This confirms the present disclosure in that the combination of both features describes well the correlation between the signal amplitude and shape while maintaining the simplicity.

TABLE-US-00001 TABLE I Both the selected kinematic measures and the signal descriptor are able to discriminate between rigid and non-rigid states (angular velocity values in s.sup.1) Rigid Non-Rigid Feature Mean Std Mean Std P-value Average Angular 3.33 0.58 5.62 1.51 0.034 Velocity Average Peak Value 12.9 3.13 29.9 6.60 0.029 Signal Descriptor 6.55 1.22 11.3 3.07 0.027

[0064] The derived mathematical model for rigidity classification, depicted in FIG. 3a, had high correlation with the data and presented a training error of 8.247.95%. This error range is acceptable, especially considering that a discrete scale is being modelled using a continuous function. Other relevant error source is the possibility of existing some undesired facilitation of the movement by patients.

[0065] Nevertheless, 131 out of 156 classifications performed by the present disclosure did not differ from the agreement between two expert physicians, corresponding to an acceptance rate above 80%. Major limitations were found on the evaluation of signals corresponding to intermediate rigidity states, whose correlation with the classification model was lower (see FIG. 3a). Conversely, the present disclosure detects more correctly low-rigidity states, meaning the optimal stimulation setting can be identified with low error. Such results suggest that the present disclosure can be a reliable second opinion on wrist rigidity evaluation (e.g., during DBS parameter trimming), with clinical benefits.

[0066] Additional biomechanical properties can also be explored in this context, such as work and impulse, both derived from resistive torque. However, these quantities are often dependent on the speed of the imposed movement which cannot be guaranteed by physicians.

[0067] In fact, such variability in the imposed velocity caused by the imposed movement by the physician can help to better perceive the wrist rigidity. A constant velocity would only be ensured by using a mechanical system attached to the limb, increasing the invasiveness and complexity of the procedure.

[0068] Regarding the detection of cogwheel rigidity, the ROC Curve on FIG. 3b suggests high sensitivity of the presently disclosed methodology while keeping the false positive rate low. The optimal operation point was obtained for =100, yielding a sensitivity of 0.93. These results, along with the low computational cost required for the arithmetic operations that create and characterize the computed triangles, enable a real-time detection of cogwheel artefacts and its quantification for rigidity classification purposes.

[0069] Levodopa is an amino acid precursor of dopamine with antiparkinsonian properties. Levodopa is a prodrug that is converted to dopamine by DOPA decarboxylase and can cross the blood-brain barrier. Two patients (male aged 47 years and female aged 61 years) were subjected to a Levodopa level medication test where an experienced neurologist evaluates the subjective Unified PD Rating Scale (UPDRS) [11] motor semi-quantitative classification for Rigidity & Rest Tremor (0 to 4) for a baseline (no levodopa) and in steps of increasing levodopa dosages until a UPDRS of 0 is achieved. This is the dosage as prescribed to the patient. At this dosage, wrist rigidity was evaluated as presently disclosed. As described above, higher index values correspond to lower perceived wrist rigidities. See also FIG. 6-7. A threshold of a rigidity measure according to the present disclosure, for example of 70% or 80%, can thus be used to objectively identify a clinically effective dosage regime. Furthermore, a threshold of a rigidity measure according to the present disclosure can also be used to identify a clinically effective dosage regime which also minimizes the risk of side effects.

TABLE-US-00002 TABLE II overall comparison of medication dosage, rigidity as measured under the present disclosure and UPDRS. Higher index values correspond to lower perceived wrist rigidities. Rigidity label Subject Levodopa UPDRS (Avg) % 1 No 3 40-50 (46.9) Yes 0 70-80 (74.3) 2 No 2 50 (48.6) Yes 0 80-90 (84.9)

[0070] The term comprising whenever used in this document is intended to indicate the presence of stated features, integers, steps, components, but not to preclude the presence or addition of one or more other features, integers, steps, components or groups thereof. It is to be appreciated that certain embodiments of the disclosure as described herein may be incorporated as code (e.g., a software algorithm or program) residing in firmware and/or on computer useable medium having control logic for enabling execution on a computer system having a computer processor, such as any of the servers described herein. Such a computer system typically includes memory storage configured to provide output from execution of the code which configures a processor in accordance with the execution. The code can be arranged as firmware or software. If implemented using modules, the code can comprise a single module or a plurality of modules that operate in cooperation with one another to configure the machine in which it is executed to perform the associated functions, as described herein. It is also disclosed a non-transitory storage media comprising computer program instructions for implementing a method as disclosed, the computer program instructions including instructions which, when executed by a processor, cause the processor to carry out one of the disclosed methods.

[0071] The disclosure should not be seen in any way restricted to the embodiments described and a person with ordinary skill in the art will foresee many possibilities to modifications thereof. The embodiments described above are combinable. The following claims further set out particular embodiments of the disclosure.

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