Systems and methods for providing neurostimulation therapy according to gait features
The present disclosure provides systems and methods for providing neurostimulation therapy according to gait features. The gait features may be analyzed to develop a patient model between gait features and optimal settings for a neurostimulation therapy using machine learning operations. The model is used to control ongoing neurostimulation therapy for the patient.
1 . A method of providing a neurostimulation therapy to a patient, comprising:
applying electrical pulses to a neural target of a patient according to a plurality of stimulation parameters;
obtaining movement data from one or more sensors implanted in or worn by the patient while electrical pulses are applied to the patient;
processing the movement data to extract a plurality of gait features corresponding to characteristics of the patient's gait while electrical pulses are applied to the patient;
obtaining patient reported pain levels from the patient using one or more patient applications on a patient therapy controller device corresponding to time intervals in which the electrical pulses are applied to the patient;
training a machine learning (ML) model using the plurality of stimulation parameters, the plurality of gait features, and the patient reported pain levels;
after training the ML model, controlling application of electrical pulses to the patient to treat pain of the patient in accordance with extracted gait features of the patient;
determining a sensor-driven score associated with the patient;
determining an evaluator-driven score associated with the patient based on patient reported outcomes;
determining a rigidity score associated with the patient based on the sensor-driven score and the evaluator-driven score;
modifying the plurality of stimulation parameters based on the rigidity score; and
applying electrical pulses to the neural target of the patient according to the modified plurality of stimulation parameters.
2 . The method of claim 1 , wherein the neurostimulation therapy is selected from the list consisting of: spinal cord stimulation and dorsal root ganglion stimulation.
3 . The method of claim 1 , wherein movement data is obtained by one or more sensors of a wearable device of the patient.
4 . The method of claim 1 , wherein movement data is obtained by one or more sensors of an implantable pulse generator (IPG) of the patient.
5 . The method of claim 1 , wherein the training is performed by one or more software applications executed on the patient therapy controller device.
6 . The method of claim 1 , wherein the training is performed by one or software applications executed by one or more processors of an implantable pulse generator (IPG) of the patient.
7 . The method of claim 1 , wherein the training is performed by one or software applications executed by one or more processors of a server platform of a medical device management system.
8 . The method of claim 1 , wherein one or more features associated with the training include at least one feature selected from the list consisting of: step length, stride length, stance phase, swing phase, single support, total double support, load response, pre-swing, step time, gait cycle, cadence, and speed.
9 . The method of claim 1 , wherein:
the sensor-driven score is determined based on the movement data from the one or more sensors implanted in or worn by the patient;
the evaluator-driven score is determined based on the patient reported outcomes and an assessment performed by a clinician, the patient reported outcomes including the patient reported pain levels, patient reported well-being scores, and patient reported activity levels; and
the rigidity score represents a quantified measure of involuntary muscle tone or resistance to movement in one or more regions of the patient's body.
10 . The method of claim 1 , further comprising:
obtaining the patient reported outcomes from the patient using the one or more patient applications on the patient therapy controller device, the patient reported outcomes including the patient reported pain levels, patient reported well-being scores, and patient reported activity levels,
wherein the training the ML model includes training the ML model using the plurality of stimulation parameters, the plurality of gait features, and the patient reported outcomes.