Control system for closed-loop neuromodulation

Abstract

A control system for a movement reconstruction and/or restoration system for a patient, comprising a sampling module configured and arranged to sample signals describing directly and/or indirectly motion at a sampling rate of at least 50 Hz; at least one stimulation system configured and arranged to provide stimulation for movement reconstruction and/or restoration to the patient; a prediction module configured and arranged to provide a prediction of at least a next movement, especially movement stage and/or sequence, to reduce latency and to synchronize stimulation to the movement phase, wherein the control system further comprises at least one controller, the controller being configured and arranged to provide stimulation control signals to the stimulation system on the basis of the information obtained by the sampling module and the prediction provided by the prediction module.

Claims

1. A control system for a movement reconstruction and/or restoration system for a patient, comprising at least one sensor or sensor network configured to sample signals describing body motion at a sampling rate of at least 50 Hz; wherein the sensor network is a wireless sensor network; at least one stimulation system configured to provide stimulation for movement reconstruction and/or restoration to the patient; a prediction module configured to provide a prediction of at least a next movement to reduce latency and to synchronize stimulation to the movement phase, wherein the control system further comprises at least one controller, the controller being configured to provide stimulation control signals to the stimulation system on the basis of the information obtained by the at least one sensor or sensor network and the prediction provided by the prediction module.

2. The control system according to claim 1, wherein the sampling rate is at least 75 Hz.

3. The control system of claim 1, wherein the control system is configured to allow an overall latency budget, the latency budget being distributed to one or more subsystems of the control system.

4. The control system of claim 3, wherein the allowed overall latency budget is 100 ms or less.

5. The control system of claim 3, wherein the subsystems include at least one of the at least one sensor or sensor network, a controller, a pulse generator, a programmer, a communication module (COM), a telemetry module (TEL).

6. The control system of claim 5, wherein the control system is configured and arranged such that the controller has an allowed latency budget between one of 10-15 ms or and 11-13 ms.

7. The control system of claim 5, wherein the telemetry module comprises a near field magnetic induction module (NFMI).

8. The control system of claim 7, wherein the control system is configured and arranged such that the telemetry module (TEL) has an allowed latency budget between 5-10 ms and wherein out of the latency budget, a larger part of the latency budget is for waiting time for establishing a telecommunication link and a smaller part of the latency budget is a buffer to accommodate with the technological natural latency of the near field magnetic induction module (NFMI).

9. The control system of claim 5, wherein the control system is configured and arranged such that the sensor network has an allowed latency budget of approx. 15-20 ms.

10. The control system of claim 5, wherein the control system is configured and arranged such that the pulse generator has an allowed latency budget of approx. 0.1-3.0 ms.

11. The control system of claim 5, wherein the control system is configured and arranged such that the controller has an allowed latency budget of approx. 10-14 ms.

12. The control system of claim 5, wherein the prediction module is connected directly and/or indirectly with the at least one sensor or the sensor network and wherein the prediction module is configured to predict patient motion and/or movement on the basis of sensor input data, or to manage and/or monitor latency of the control system in order to stay within an overall allowed latency budget.

13. The control system of claim 1, wherein the control system comprises a latency budget monitoring and/or management system, which is configured and arranged to monitor and manage the overall latency of the control system by monitoring and/or managing latency of subsystems of the control system.

14. The control system of claim 1, wherein the control system is a closed-loop system.

15. The control system of claim 1, wherein the control system has a pre-warning module, which is configured and arranged to provide a pre-warning signal indicative of providing an upcoming stimulation event.

16. A method for a control system, comprising: responsive to detecting motion at least one sensor or sensor network configured to detect an indication of movement at a region of a patient; wherein the sensor network is a wireless sensor network; sampling data from the at least one sensor or sensor network at a sampling module at a threshold sampling rate; predicting a motion at the region of the patient by a prediction module; collecting the data from the at least one sensor or sensor network and the prediction module at a controller configured to process the data; and stimulating movement at the region of the patient by generating a pulse signal via a stimulation system based on the processed data from the controller.

17. The method of claim 16, wherein detecting motion at the at least one sensor or sensor network includes receiving three-dimensional accelerations, angular velocities, and orientations from an inertial measurement unit and wherein the inertial measurement unit includes an accelerometer and a gyroscope.

18. The method of claim 16, wherein sampling data at the at least one sensor or sensor network includes obtaining data from the at least one sensor or sensor network at a minimum rate of 50 Hz.

19. The method of claim 16, wherein stimulating movement via the stimulation system includes electronically stimulating neurons by at least one of a central nervous system (CNS) stimulation system and a peripheral nervous system (PNS) stimulation system.

Description

BRIEF DESCRIPTION OF DRAWINGS

(1) Further details and advantages of the present invention shall now be disclosed in connection with the drawings.

(2) It is shown in

(3) FIG. 1 a schematic, very simplified representation of a stimulation pulse delivered by a system according to the present invention;

(4) FIG. 2A, B the necessary current and necessary charge to trigger an action potential in a nerve fiber as a function of the pulse width (using a square pulse);

(5) FIG. 3 a table specifying the nerve fiber types, diameters, and function;

(6) FIG. 4 the delay between electrical stimulation of the spinal cord and the evoked muscle response for various leg muscles;

(7) FIG. 5 a table specifying the intended movement and the involved agonist muscle and the involved antagonist muscle;

(8) FIG. 6 discrete sets of functional muscle blocks (FMB) and custom muscle blocks (CMB);

(9) FIG. 7 a general layout of an embodiment of the control system for a movement reconstruction and/or restoration system for a patient according to the present invention;

(10) FIG. 8 the flow of information in the closed-loop system over time, and corresponding worst-case latencies; and

(11) FIG. 9 a schematic diagram of foot pitch/forward acceleration of a patient.

DETAILED DESCRIPTION

(12) Note that in the following we primarily refer to CNS/EES stimulation. The one skilled in the art may transfer the stimulation parameters to PNS/FES stimulation.

(13) The control system may provide stimulation data for movement reconstruction and/or restoration for stimulation of afferent nerve fibers using electrical current pulses. Given this starting point, the following stimulation parameters may be identified:

(14) electrode configuration (which electrodes to use, polarity)

(15) stimulation (Pulse) amplitude

(16) stimulation (Pulse) width

(17) stimulation (Pulse) frequency

(18) FIG. 1 illustrates a schematic, very simplified representation of the stimulation pulse, which illustrates the pulse amplitude, pulse width, and pulse frequency. Each stimulation pulse may be followed by a neutralization pulse or a neutralization period (not depicted) to remove the electric charge from the tissue in order to avoid tissue damage.

(19) The effects of each of the stimulation parameters are described below.

(20) Electrode configuration: Stimulating a specific muscle group requires applying a specific electrical field at a specific location on the spinal cord. Therefore, in the present control system the electrical stimulation may be delivered to the spinal cord by a lead with multiple electrodes. The location, shape, and direction of the electrical field that is produced may be changed by choosing a different electrode configuration (which electrodes are used, with which polarity and potential) that is used to deliver the current. Hence, the electrode configuration may determine to which spinal roots the stimulation is delivered, and therefore which subsequent muscles or muscle groups activity will be reinforced.

(21) Pulse amplitude and pulse width: In FIG. 2A and FIG. 2B the necessary current and necessary charge to trigger an action potential in a nerve fiber are shown as a function of the pulse width (using a square pulse) (cf. Merrill D R., et al. Electrical Stimulation of excitable tissue: design of efficacious and safe protocols, J Neurosci methods 141(2):171-98 (2005)). FIG. 2A and FIG. 2B also show the rheobase current I.sub.rh, which is the current that is required when using infinitely long pulse widths, and the chronaxie time t.sub.c, which is the required pulse width at a current of 2I.sub.rh.

(22) Although larger currents may be required at smaller pulse widths, the total required charge may decrease with decreasing pulse width, see FIG. 2B. Hence shorter pulses with higher current amplitudes may be energetically beneficial.

(23) For smaller diameter nerves, the current-pulse width curve of FIG. 2A shifts, as smaller diameter fibers may require higher currents. Hence, a higher current may activate more nerve fibers, as also smaller diameter nerve fibers may be activated (until saturation). However, also cross-talk is increased as also more neurons from neighboring roots may be activated. Fortunately, the afferent fibers involved in motor control (fiber types Ia and Ib) may be all relatively large (12-20 μm), while the fibers involved in touch, temperature, and pain feedback (which should not be triggered) may be relatively small (0.5-12 μm), as depicted in FIG. 3. Hence, with increasing pulse width and/or current amplitude, the type Ia and Ib fibers may be the first to be recruited. This may enable recruiting (most of) the relevant fibers while keeping cross-talk and patient discomfort to a minimum.

(24) Pulse frequency: The pulse frequency may determine the frequency of the action potentials generated in the afferent nerves, assuming sufficient charge is delivered each pulse to trigger the action potentials. As no new action potential can occur in a nerve during the refractory period, the frequency of the triggered action potentials will saturate at high pulse frequencies. This saturation point is generally at around 200 Hz for afferent fibers (Miller J P. et al., Parameters of Spinal Cord Stimulation and Their Role in Electrical Charge Delivery: A Review. Neuromodulation: Technology at the Neural Interface 19, 373-384, (2016)). However, stimulation at frequencies above the saturation point may still be beneficial, as by increasing frequency the total charge delivered per unit time (i.e. charge per second) can be increased without changing current amplitude or pulse width (Miller J P. et al., Parameters of Spinal Cord Stimulation and Their Role in Electrical Charge Delivery: A Review. Neuromodulation: Technology at the Neural Interface 19, 373-384, (2016)).

(25) Pulse positioning: Many tasks, including walking, require simultaneous activation of multiple muscle groups. Hence, to support these Tasks, multiple muscle groups may need to be stimulated simultaneously, each requiring a specific electrical field and pulse frequency. When applied simultaneously, these different electrical fields may interact with each other, potentially leading to unintended and uncontrolled effects. Therefore, to avoid this situation, care should be taken that the individual stimulation pulses and their neutralization periods targeting different muscle groups are not applied simultaneously. This may not be considered a stimulation parameter but does identify a required system feature: a pulse positioning algorithm (PPA).

(26) The previous section describes the effect of the stimulation parameters on triggering action potentials in afferent nerve fibers. Although triggering these action potentials is an essential step in the therapy, in the end the stimulation should enable or support the patient in performing specific lower body motions, which may require the activation of specific muscles or muscle groups. The effect of the triggered action potentials in afferent nerve fibers on muscle activation may be filtered inside the spinal cord through spinal reflex circuits and modulated through the voluntary control of the patient. Hence, the effect of the stimulation parameters on muscle activation may be not perfectly clear and may be affected by intra- and inter-Patient variations. The following aspects may be of relevance here:

(27) different patients may have different levels of voluntary control over their lower body, depending on the type and severity of their SCI lesion level and state of (spontaneous) recovery.

(28) stimulation of afferent nerve fibers may assist or enable activation of the corresponding muscles but may not necessarily enforce motion. The patient may modulate the activation (e.g. make a large or small step without changing the stimulation), or even resist motion of the leg completely. This may vary per patient and may change with increasing recovery.

(29) conjecture: Because the spinal cord floats in the cerebrospinal fluid, the distance between the spinal cord and the lead electrodes may vary (mostly as a function of the patient's posture: prone—large distance, supine—small distance). Another hypothesis may be that due to posture changes, the layer thickness of low conductive epidural fat between the lead electrodes and the dura/cerebrospinal fluid a changing, leading to an impedance change as seen by the electrodes, and resulting in an altered current/voltage delivered stimulation by the electronics. As a result, the effect of the applied stimulation (including muscle onset and saturation) may also vary with the patient's posture. Although this conjecture is not proven, patients may successfully make use of the described effects to modulate the stimulation intensity by varying their posture: bending forward reduces the intensity, bending backward increases it.

(30) pulse frequencies between 40 and 120 Hz may mostly being used, although it may theoretically be possible to stimulate up to 500 Hz as this may have benefits for selectivity in muscle activation and improved voluntary control of the patient.

(31) It may be possible that general increasing the pulse amplitude may not lead to increased recruitment of muscle fibers (with corresponding increased cross-talk), and that increasing the stimulation frequency may lead to increased muscle activation without affecting cross-talk. However, increasing the stimulation frequency may reduce the intensity of natural proprioception and result in a decreased feeling in the leg of the patient. This is probably due to the collision of natural sensory inputs with antidromic action potentials generated by the electrical stimulation. At high frequency (above 100 Hz), patients may even report a complete loss of sensation of the leg and “feel like walking with their legs being absent”. This is a non-comfortable situation requiring the patient to make a leap of faith at each single step, believing that the leg that he/she does not feel anymore will support him/her during the next stance phase. Adjusting the balance between stimulation amplitude and frequency may therefore be necessary to find the optimal compromise between cross-talk limitation and loss of sensation. Simulations suggest that a possible workaround may be to shift the stimulation domain to lower amplitudes and even higher frequency, such that with a minimal number of stimulated fibers the same amount of activity is triggered in the spinal cord. Such hypothesis requires validation via additional clinical data. Finally, it may also be identified that different patients require different stimulation, i.e. that the optimal frequency and amplitude settings may vary highly between patients. Hence, the relation between stimulation amplitude and frequency on muscle activation may be still for a large part unclear. Moreover, the optimal stimulation settings may vary during the day, the assistive device that is used (including but not limited to crutches, walker, etc.), over time with improved recovery, and with the goal of the training or activity.

(32) Timing: Apart from applying the correct electrical field at the right location on the spinal cord, they also may need to be applied at the correct moments in time and correctly sequenced. The relevant timing aspects that are identified are listed below.

(33) There is a delay from stimulation on the spinal cord to muscle activation (typical values in the order of 0-30 ms depending on the muscle, see FIG. 4, LVLat=left vastus lateralis, RVLat=right vastus lateralis, Lll=left iliopsoas, Rll=right iliopsoas, LRF=left rectus femoris, RRF=right rectus femoris, LST=left semitendinosus, RST=right semitendinosus, LTA=left tibialis anterior, RTA=right tibialis anterior, LMG=left medial gastrocnemius, RMG=right medial gastrocnemius, LSol=left soleus, RSol=right soleus, LFHL=left flexor halluces longus, RFHL=right flexor halluces longus).

(34) while EES enables patients to perform motions, the patient may need to be able to predict when the stimulation will occur in order to make the best use of the stimulation. Likewise, suppressing motion while stimulation is provided also requires that the patient knows when to expect the stimulation. Hence, predictability of the stimulation timing is essential.

(35) when the stimulation is not synchronized to the patient's (intended) motion, the patient may not be able to perform a proper movement. Here, this may mean that the stimulation needs to be predictable by the patient, as the patient needs to synchronize to the stimulation.

(36) the duration of the stimulation for leg swing during walking may need to be finely tuned. For some patients, increasing the duration of this stimulation by 100 ms made the patient jump instead of performing a proper step.

(37) 20 ms may be a sufficient resolution for tuning the stimulation timings (i.e. the on/off times of the stimulation for a specific muscle group may not need to be controlled at a precision below 20 ms). Given current data availability, controlling the timings at resolutions below 20 ms may not seem to improve the effectiveness of the stimulation.

(38) Based on the previous sections, the stimulation parameters may be selected to control in the control system. This may determine the control output space that is used, and therefore the complexity of the control problem and the potential effectiveness of the control system.

(39) First it is discussed which parameter spaces can be reduced or eliminated. The remaining control output space is summarized below.

(40) Electrode configuration: Walking, as well as other movements of the lower extremities, may be composed of well-coordinated flexion and extension of lower body joints by contraction of agonist muscles and relaxation of antagonist muscles. The specific set of agonist and antagonist muscles for joint specific flexion and extension may be grouped, and as the number of joints is limited, this means that only a small discrete set of muscle groups may be needed to be stimulated. For each joint flexion and extension, the STP for e.g. for e.g. programming spatial and temporal parameters of the stimulation will support creating the optimal electrode configuration for activation of the agonist muscles while avoiding activation of the antagonist muscles (as well as avoiding activation of muscles on the contralateral side). This may be done in a procedure called the functional mapping. We define the Functional Muscle Blocks (FMB), as the resulting stimulation configurations for each specific muscle group. At least 12 specific FMBs have been identified for using the control system, these are listed in FIG. 5 with their corresponding agonists and antagonists.

(41) As knee flexion and hip extension both involve the semitendinosus, it is physically not possible to target knee flexion and hip extension separately. Therefore, FIG. 5 does not include knee flexion (this could be considered redundant to hip extension).

(42) Next to the 12 FMB listed in FIG. 5, it is also envisioned that the trainer/therapist/physiotherapist may create Custom Muscle Blocks (CMB). Creating CMB may be useful in case the trainer/therapist/physiotherapist wants to apply stimulation that does not specifically target any of the 12 muscle groups targeted by the FMB, or in case the trainer/therapist/physiotherapist wants to use a variant of one of the 12 FMB in a specific task.

(43) Hence, by limiting the electrode configurations to the discrete set of FMB and CMB (versus an infinite number of possible electrode configurations), the control problem complexity may be reduced considerably without significantly affecting the potential effectiveness of the control system. Stimulation for a Task is then reduced to stimulation of (a subset of) the predefined FMB and CMB, see FIG. 6. In this example, the Right Trunk Stability is used in both Task 1 and Task 2.

(44) The functional mapping procedure may require measuring the response of each of the muscles listed in FIG. 5 with EMG sensors. Due to the large number of muscles, this requires attaching many EMG sensors to the patient (which is time consuming) and processing a large amount of data. Moreover, as motion of the patient may induce signal artifacts, the functional mapping may be best performed while the patient is not moving. For these reasons, the functional mapping procedure may be performed in a separate session using the Space Time Programmer for e.g. programming space and time of the stimulation, and not e.g. adaptively within the control system. Hence, the configuration of FMB and CMB may be considered as a given to the control system.

(45) Pulse width: From the viewpoint of triggering action potentials in afferent nerve fibers, the parameters pulse width and pulse amplitude may be tightly linked and may together determine which afferent nerve fibers are recruited. Increasing the pulse width may allow to reduce the amplitudes and decreasing the pulse width may allow reducing energy consumption (as the total required charge for triggering an action potential decreases with decreasing pulse width, see FIG. 2B and stimulating more FMB simultaneously or at higher frequencies. However, from a control perspective the two parameters may be (almost) redundant, as increasing either parameter may lead to the recruitment of more afferent nerve fibers over a larger area.

(46) Pulse widths below chronaxie time t.sub.c may quickly require high currents (and thus high voltages), which is difficult to produce and may lead to patient discomfort. Beyond t.sub.c, the strength-duration curve of FIG. 2A is almost flat, so increasing pulse width beyond t.sub.c has little effect on the required amplitudes while it increases total power consumption. Also considering that having a fixed pulse width simplifies the pulse positioning, the pulse width is chosen to be fixed (at a value near chronaxie time t.sub.c such that both energy consumption and required current amplitudes remain low, where t.sub.c≈200 μs for afferent dorsal root nerve fibers in humans). This reduces the complexity of the control problem by reducing the number of output parameters.

(47) This may leave the following stimulation parameters to be controlled over time by the control system:

(48) which FMBs to stimulate

(49) stimulation amplitude per FMB

(50) stimulation frequency per FMB

(51) The pulse positioning may be considered a lower level problem and may therefore be not a direct output of the control system (system feature). The pulse positioning may be performed by the IPG.

(52) Although combining amplitude and frequency to a single ‘intensity’ parameter has been considered, doing so may not be envisioned for the control system, as these parameters may have very different effects. On triggering action potentials in afferent nerve fibers, the amplitude and frequency may be independent parameters: the amplitude determines in which afferent nerve fibers action potentials are triggered, the frequency determines the rate at which they are triggered. Hence, in principle the amplitude determines which muscle fibers are activated, the frequency determines how hard, although it is unclear if the independence of the two parameters also holds for muscle activation due to the signal processing that occurs in the spinal cord. Moreover, it may be apparent that for some patients changing the amplitude gives the best results, while for other patients the frequency may be the more useful parameter.

(53) As the precise relation between frequency and amplitude is not known in the clinical context it may not be recommended to combine frequency and amplitude to single parameter. Hence, the stimulation frequency and amplitude may be controlled independently from each other.

(54) FIG. 7 shows a general layout of an embodiment of the control system 10 for a movement reconstruction and/or restoration system for a patient according to the present invention.

(55) The control system 10 comprises a sampling module 12.

(56) Additionally, the control system 10 comprises a prediction module 14.

(57) In the shown embodiment, the control system 10 further comprises a controller 16. Furthermore, the control system 10 comprises in the shown embodiment a stimulation system 18.

(58) In this shown embodiment, the stimulation system 18 comprises a pulse generator, in particular an implantable pulse generator.

(59) In this shown embodiment, the stimulation system 18 comprises both a CNS stimulation system for CNS stimulation and a PNS stimulation system for PNS stimulation.

(60) However, the stimulation system 18 could also only comprise a CNS stimulation system for CNS stimulation or a PNS stimulation system for PNS stimulation.

(61) In this embodiment, the control system 10 further comprises a subsystem, in particular a sensor 20.

(62) In this embodiment, the control system 10 further comprises another subsystem, in particular a telemetry module TEL.

(63) The telemetry module TEL could be or could comprise a near field magnetic induction module (NFMI).

(64) Possible embodiments of other subsystems that could be generally comprised in the control system 10 comprise at least one of a controller and/or a pulse generator and/or a sensor network and/or a programmer and/or a communication module COM.

(65) However, the control system 10 could also not comprise any other subsystem.

(66) In this embodiment, the sensor 20 is connected to the sampling module 12.

(67) The sensor 20 is also connected to the prediction module 14.

(68) The connection between the sensor 20 and the prediction module 14 could generally be a bidirectional connection.

(69) Alternatively, and/or additionally, a sensor network could be connected to the prediction module 14.

(70) The connection between the sensor 20 and/or the sensor network and the sampling module 12 is in the shown embodiment a direct connection.

(71) However, also an indirect connection (i.e. with another component of the control system 10 in between) would be generally possible.

(72) The connection between the sensor 20 and the sampling module 12 is established in the shown embodiment via a wireless network WSN.

(73) However, also a cable-bound connection would be generally possible.

(74) The connection between the sensor 20 and the prediction module 14 is in the shown embodiment a direct connection.

(75) This connection could generally be a bidirectional connection.

(76) Alternatively, and/or additionally, a sensor network could be connected to the prediction module.

(77) However, also an indirect connection (i.e. with another component of the control system 10 in between) would be generally possible.

(78) The connection between the sensor 20 and/or the sensor network and the prediction module 14 is established in the shown embodiment via a wireless network WSN.

(79) However, also a cable-bound connection would be generally possible.

(80) The sampling module 12 is connected to the controller 16.

(81) The connection between the sampling module 12 and the controller 16 is in the shown embodiment a direct connection.

(82) However, also an indirect connection (i.e. with another component of the control system 10 in between) would be generally possible.

(83) The connection between the sampling module 12 and the controller 16 is established in the shown embodiment via a wireless network WSN.

(84) However, also a cable-bound connection would be generally possible.

(85) However, the sampling module 12 and the controller 16 could also be implemented in the same system.

(86) The prediction module 14 is connected to the controller 16.

(87) The connection between the prediction module 14 and the controller 16 is in the shown embodiment a direct connection.

(88) However, also an indirect connection (i.e. with another component of the control system 10 in between) would be generally possible.

(89) The connection between the prediction module 14 and the controller 16 is established in the shown embodiment via a wireless network WSN.

(90) However, also a cable-bound connection would be generally possible.

(91) However, the prediction module 14 and the controller 16 could also be implemented in the same system.

(92) In an alternative embodiment, the prediction module 14 could be part of the controller 16.

(93) The controller 16 is connected to the stimulation system 18.

(94) The connection between the controller 16 and the stimulation system 18 is in the shown embodiment a direct connection.

(95) However, also an indirect connection (i.e. with another component of the control system 10 in between) would be generally possible.

(96) The connection between the controller 16 and the stimulation system 18 is established in the shown embodiment via a wireless link, i.e. a telemetry module TEL.

(97) However, also a cable-bound connection would be generally possible.

(98) In the present embodiment, a patient is equipped with the present control system 10.

(99) In this embodiment, the control system 10 is body worn.

(100) By means of one or more sensors 20, signals indicative for a movement, e.g. movement of position of the body and/or parts of the body, including but not limited to the trunk and/or the head and/or a limb, e.g. an arm or leg, and/or a foot or hand, e.g. during walking, cycling, swimming, rowing, stepping, or running, can be sensed and used by the control system 10.

(101) In this embodiment, signals indicative for walking are sensed by the sensor 20.

(102) The sensor signals are transferred to the sampling module 12.

(103) In other words, the sampling module 12 samples data from the sensor 20.

(104) In general, the sampling module 12 samples data at a sampling rate of at least 50 Hz.

(105) In this embodiment, the sampling module 12 samples data at a fixed sampling rate of 100 Hz.

(106) In an alternative embodiment, the sampling module 12 samples data at a fixed sampling rate of 50 Hz.

(107) In another alternative embodiment, the sampling module samples data at a fixed sampling rate of 75 Hz.

(108) However, also every other sampling rate could be generally possible.

(109) In general, the optimal sampling rate could be calculated following the Nyquist-Shannon sampling theorem.

(110) Alternatively, the optimal sampling rate could be at least 5 to 10 times the highest significant frequency present in the analog signal.

(111) The data from the sampling module 12 are transferred to the controller 16 and there processed.

(112) The prediction module predicts motion and/or movement of the patient on the basis of sensor 20 input data, especially to manage and/or monitor latency of the control system 10 in order to stay within an overall allowed latency budget.

(113) The prediction module 14 may compensate for the latency introduced by the control system 10.

(114) Depending on the control algorithm, the prediction module 14 could be able to predict the patient's motion in order to compensate for the latency in the closed-loop.

(115) In other words, the prediction module 14 adds latency to compensate for the nominal part of the latency of the control system 10 and enables real-time, or close to real-time, synchronization of stimulation to the patient's motion.

(116) For instance, for closed-loop walking, the gait phase could be predicted given the current joint angle and angular velocity provided by placing one or more sensors 20 directly or indirectly on one or both feet and/or legs and/or the abdomen and/or trunk of a patient.

(117) The data and/or information from the prediction module 14 are transferred to the controller 16.

(118) The controller 16 provides stimulation control signals to the stimulation system 18 on the basis of the information obtained by the sampling module 12 and the prediction provided by the prediction module 14.

(119) In other words, the controller 16 processes data from the sampling module 12 and the prediction module 14.

(120) By means of the controller 16, the control software is executed. The controller 16 programs the stimulation system 18 comprising the implantable pulse generator to deliver the correct stimulation to the patient via the stimulation system 18, in particular the implantable pulse generator.

(121) In this embodiment, the stimulation system 18 functions as CNS stimulation system, in particular EES-system and as PNS stimulation system, in particular FES-system.

(122) There may be also a programmer (not shown in the figures). The programmer, or also called the clinician programmer, can be used to receive inter alia stimulation parameters, patient data, physiological data, training data etc.

(123) Not shown in FIG. 7 is that the at least one sensor 20 is an inertial measurement unit (IMU) 20.

(124) Said IMU 20 comprises an accelerometer, a gyroscope, and a magnetometer.

(125) Said IMU 20 measures and reports 3D accelerations, 3D angular velocities and 3D orientation using a combination of an accelerometer and a gyroscope.

(126) In an alternative embodiment, an IMU 20 could use a combination of one or more of an accelerometer, one or more gyroscopes, and optionally one or more of a magnetometer.

(127) By integrating the angular velocity assessed by the gyroscope and fusing with data from the accelerometers, a precise measurement of the angle of the foot is obtained.

(128) Based on these measurements the orientation of the IMU 20 with respect to the fixed world is estimated accurately, using standard sensor fusion algorithms.

(129) So, movement is detected and therefrom also a signal derived, which is indicative for an angle, e.g. the foot angle.

(130) Real-time and non-real-time reconstruction of foot trajectories may be done up to a few centimeters accuracy.

(131) In an alternative embodiment, at least one sensor 20 could also be one of an optical sensor, a camera, a piezo element, a velocity sensor, an accelerometer, a magnetic field sensor, a torque sensor, a pressure sensor, a displacement sensor, an EMG measurement unit, a goniometer, a magnetic position sensor, a hall sensor, a gyroscope and/or one or more motion tracking video cameras, or one or more infra-red cameras.

(132) Some sensors 20 could require fixed base station in the environment, including but not limited to magnet sensors or infra-red sensors.

(133) Electromagnetic position sensors, optical sensors and cameras could estimate 3D position and orientation.

(134) Torque sensors could be placed on a bicycle crank for assessing the torque during cycling.

(135) Some sensors 20 could be worn by the patient without acquiring fixed base station, including but not limited to piezo elements, pressure sensors and/or torque sensors.

(136) By directly and/or indirectly attaching one or more sensors 20, e.g. IMUs 20, to the trunk and/or waist and/or at least one limb and/or one or more parts of a limb, including one or more joints, the angular velocity and angle of one or more limbs and/or one or more parts of limbs and/or one or more joints during motion, e.g. gait cycle could be determined to realize the reorganization of the various motion phases, e.g. gait phase.

(137) Thanks to the angle it could be possible to compute the acceleration of the limb and/or part of the limb in the forward direction.

(138) However, also acceleration in any other direction may be determined.

(139) In particular, the angle of the ankle joint varies during gait cycle with different gait events, including but not limited to pre-swing, swing, loading response and stance (and/or toe-off, midswing, heel strike, foot flat and midstance).

(140) The angle of at least one limb and/or part of a limb (including one or more joints) of a patient could be used by the prediction module 14 to predict the intended and/or ongoing motion.

(141) The angle of at least one limb and/or part of a limb can also be used to find out which support the patient really needs from the control system 10.

(142) For open loop walking, a change in limb angle and/or part of a limb angle (including joints, e.g. ankle joint) over a certain threshold could be used to initiate a certain stimulation sequence.

(143) In particular, the gait event heel-off could trigger the stimulation for one or more complete gait cycles.

(144) However, also other gait events, including but not limited to pre-swing, swing, loading response and stance (and/or toe-off, midswing, heel strike, foot flat and midstance) could trigger stimulation for one or more complete gait cycles.

(145) Note that also single events of other periodic movements (including but not limited to cycling, rowing, swimming, stepping, standing up, sitting down) could trigger the stimulation for one or more complete motion cycles.

(146) In other words, the control system 10 is not only applicable for walking/gait cycle, but also for diverse other movements including but not limited to cycling, rowing, swimming, stepping, standing up, sitting down.

(147) Two or more sensors 20 could form a sensor network.

(148) In general, the sensor network could be a wireless sensor network.

(149) However, also a cable-bound connection between the single units of a sensor network could be generally possible.

(150) In an alternative embodiment, the control system 10 could be connected to a training entity via a wireless link.

(151) Note that the prediction and reconstruction of the movement could be relevant for the training entity, including but not limited to a body weight support robot or a bicycle.

(152) Note that the body weight support could be adapted, or the cycling cadence could be adapted based on the movement reconstruction.

(153) Not shown in FIG. 7 is the fact that the one or more sensors 20 could be connected to, inserted and/or integrated in a training entity, included but not limited to an exoskeleton, body weight support, treadmill and/or crutches.

(154) Not shown in FIG. 7 is that for closed-loop cycling, measuring the pedal phase can simply be achieved by attaching a sensor 20, e.g. an IMU, to the crank of the bicycle.

(155) Angles could be reflected in the position of the pedal.

(156) The pedal phase could then be defined as the crank angle, which is directly linked to the IMU orientation.

(157) Note that the pedal phase could also be predicted given the current crank angle and angular velocity (both directly provided by placing an IMU on a bicycle crank).

(158) For closed-loop cycling, the stimulation partiture defines spatial stimulation, stimulation at which pedal phase, amplitudes, and frequencies.

(159) Not shown in FIG. 7 is the total latency budget of the control system 10.

(160) The control system 10 could allow an overall latency budget, the latency budget being distributed to one or more subsystems of the control system 10.

(161) Not shown in FIG. 7 is that the control system 10 could further comprise a latency budget monitoring and/or management system.

(162) The latency budget monitoring and/or management system could be configured and arranged to monitor and manage the overall latency of the control system 10 by monitoring and/or managing latency of subsystems of the control system 10, especially online and/or in real-time.

(163) The total latency budget could be divided over the subsystems and interfaces in the control loop.

(164) In the present embodiment, possible latency sources here include but are not limited to the sensor 20, the controller 16, and the stimulation system 18, as well as one or more wireless or cable-bound connections between these modules.

(165) In particular, the possible sources of latency include but are not limited to: sensor sampling, sensor data processing, sensor data transmission to the controller 16 (here via the sampling module 12), sensor data processing at the controller 16, generation of new stimulation input from by the controller 16, stimulation data transmission to the stimulation system 18, implementation of the stimulation input by the stimulation system 18, cf. FIG. 8.

(166) However, additional subsystems (including but not limited to a controller, a pulse generator, a sensor network, a processor, a communication module and a telemetry module) may be also sources of latency.

(167) To ensure that the total latency is kept within limits, the total allowed latency in the control loop (without latency compensation) may be set to a fixed time.

(168) The allowed overall latency budget is here 100 ms.

(169) In an alternative embodiment, the allowed overall latency budget could less then 100 ms.

(170) In an alternative embodiment, the allowed overall latency budget could be 50 ms.

(171) In another alternative embodiment, the allowed latency budget could also be less than 50 ms.

(172) However, also every other allowed overall latency budget could generally be possible.

(173) The controller 16 could have an allowed latency budget of approx. 10-15 ms, especially approx. 11-13 ms, preferably approx. 12 ms.

(174) Similarly, the possible subsystem controller could have an allowed latency budget of approx. 10-14 ms, especially approx. 11-13 ms, preferably approx. 12 ms.

(175) The subsystem sensor network could have an allowed latency budget of approx. 15-20 ms, especially approx. 16-18 ms, preferably approx. 17 ms.

(176) The stimulation system could have an allowed latency budget of approx. 0.1-3.0 ms, especially approx. 1.5-2.5 ms, preferably approx. 2.0 ms.

(177) Moreover, the telemetry module TEL may have an allowed latency budget of approx. 5-10 ms, especially approx. 6-8 ms and preferably approx. 7 ms, especially wherein out of the 7 ms a larger part of the latency budget may be for waiting time for establishing a telecommunication link and a smaller part of the latency budget may be a buffer to accommodate with the technological natural latency of the near field magnetic induction module (NFMI).

(178) The pulse generator could have an allowed latency budget of approx. 0.1-3.0 ms, especially approx. 1.5-2.5 ms, preferably approx. 2.0 ms.

(179) It is also not shown in FIG. 7 that remote control of the control system 10 could be generally possible.

(180) It is also not shown in FIG. 7 that the control system 10 is a closed-loop system.

(181) However, it could generally also be possible that the control system 10 is an open-loop system.

(182) Not shown in FIG. 7 is that the control system 10 could comprise a pre-warning module, which is configured and arranged to provide a pre-warning signal indicative of providing an upcoming stimulation event.

(183) In particular, the pre-warning signal may act in a sub-motor threshold region at which a sensation is evoked, but not a motor response.

(184) FIG. 8 illustrates the flow of information in the closed-loop system of the control system 10 disclosed in FIG. 7 over time, and corresponding worst-case latencies.

(185) In this embodiment, potential latency sources include but are not limited to: sensor sampling, sensor data processing, sensor data transmission to the controller 16 (here via the sampling module 12), sensor data processing at the controller 16, generation of new stimulation input from by the controller 16, stimulation data transmission to the stimulation system 18, here an IPG, implementation of the stimulation input by the stimulation system 18.

(186) The flow of information over time (ms) is shown.

(187) The dashed marks illustrate the information flow following a heel strike.

(188) In this illustration, the motion data is sampled at 114 Hz and transmitted at the rate of 100 Hz to the controller 16.

(189) However, also other sampling rates and transmission rates of motion data to the controller 16 are generally possible.

(190) FIG. 9 shows a schematical diagram of foot pitch/forward acceleration of a patient equipped with the control system disclosed in FIG. 7.

(191) Here, a patient is equipped with one sensor 20 per foot.

(192) In this embodiment, the sensor 20 is an IMU.

(193) Alternatively, the patient could be equipped with the control system 10 described in FIG. 7 including one IMU and a shoe insole comprising a sensor network for the left or the right foot.

(194) In another embodiment, the patient could be equipped with two or more IMUs per foot.

(195) Further, the IMU and/or the shoe insole comprising the sensor network can be replaced by another type of sensor 20 including but not limited to e.g. a piezo element.

(196) In this embodiment, it could be possible that the piezo element is integrated in wearables like e.g. a sock, a knee sock, tights, a shoe.

(197) The foot pitch (degree) and forward acceleration (meter per s.sup.2) of the right foot of a patient equipped with the control system 10 disclosed in FIG. 7 during walking is shown.

(198) From these signals, clearly the cadence, pre-swing, swing, loading response and stance can be identified.

(199) The same events and parameters can be identified for the left foot.

(200) As walking is a periodic motion, all measured signals are also periodic.

(201) Hence, it is always possible to estimate the cadence by extracting the base frequency of the measured signals.

(202) By combining gait phase and cadence information of both feet of the patient together with the gait phase and cadence of the stimulation input, including the latency prediction, a reliable gait phase and cadence estimate can be provided.

(203) Note that gait can vary a lot between different patients P as well as for a single patient for different walking speeds and different assistive devices (body-weight support, walker, crutches, etc.).

(204) Especially for impaired gait, not all gait events are always present.

(205) Moreover, machine-learning methods can be used to adapt the gait phase estimation to the specific gait of the patient.

(206) The level of agreements and discrepancies between motion of the left and right foot, and the stimulation input, can be used to give an indication of the gait phase estimation reliability, e.g., the measured cadence of the left foot should be equal to the measured cadence of the right foot and the cadence of the provided stimulation, and the left foot and right foot should be (roughly) in anti-phase.

(207) In the control loop also use can be made of the realization that the feet do not move independently from each other but are connected mechanically via the hip and on neural level via the spinal cord.

(208) In particular, inhibitory reflex circuits in the spinal cord modulate neural firing rates (and hence modulate recruitment of motor neurons through EES).

(209) Note that the example control and estimation routines included herein can be used with various system configurations. The control methods and routines disclosed herein may be stored as executable instructions in non-transitory memory and may be carried out by a control system 10 e.g. as a part of the controller 16 in combination with the sampling module 12, the prediction module 14, the stimulation system 18 and the subsystems sensor 20, controller, a pulse generator, a sensor network, a communication module COM, a telemetry module TEL, and other system hardware. The specific routines described herein may represent one or more of any number of processing strategies such as event-driven, interrupt-driven, multi-tasking, multi-threading, and the like. As such, various actions, operations, and/or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Likewise, the order of processing is not necessarily required to achieve the features and advantages of the example embodiments described herein but is provided for ease of illustration and description. One or more of the illustrated actions, operations and/or functions may be repeatedly performed depending on the particular strategy being used. Further, the described actions, operations and/or functions may graphically represent code to be programmed into non-transitory memory of a computer readable storage medium in the controller 16, where the described actions are carried out by executing the instructions in a control system 10 including the various hardware components.

REFERENCES

(210) 12 sampling module 14 prediction module 16 controller 18 stimulation system 20 sensor CMB custom muscle block COM communication module EES epidural electrical stimulation FES functional electrical stimulation FMB functional muscle block IPG implantable pulse generator WSN wireless network, connection TEL connection, telemetry line LVLat left vastus lateralis RVLat right vastus lateralis Lll left iliopsoas Rll right iliopsoas LRF left rectus femoris RRF right rectus femoris LST left semitendinosus RST right semitendinosus LTA left tibialis anterior RTA right tibialis anterior LMG left medial gastrocnemius RMG right medial gastrocnemius LSol left soleus RSol right soleus LFHL left flexor halluces longus RFHL right flexor halluces longus