Deep Brain Stimulation Controller Using Leg IMU Kinematic Feedback
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Solution Overview
Problem
Deep brain stimulation systems for treating movement disorders like Parkinson's disease are often 'open-loop' and unable to effectively manage sudden and severe symptoms such as freezing of gait, which can be non-responsive to medication and lead to falls, as they lack real-time adaptive capabilities.
Innovation Solution
The integration of inertial measurement units (IMUs) on a patient's legs to capture kinematic data, which is used by a controller to identify abnormal movement events like freezing of gait, and modify deep brain stimulation parameters in real-time using a stimulation map that adjusts intensity and frequency based on predicted probabilities of such events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If open-loop deep brain stimulation is used, then the system is simple to operate, but it cannot effectively manage sudden and severe symptoms such as freezing of gait
Solution Approach 1:
The patent implements closed-loop feedback by using IMUs to detect kinematic data related to gait parameters, processing this data through a controller to identify freezing of gait events, and then adjusting DBS stimulation parameters accordingly. This feedback mechanism enables the system to respond dynamically to patient needs, significantly improving reliability for managing sudden symptoms like freezing of gait while maintaining acceptable device complexity through automated algorithms.
Solution Approach 2:
The patent replaces manual adjustment of stimulation parameters (mechanical/physical adjustment) with automated electronic control based on processed kinematic data. The controller automatically modifies DBS parameters by analyzing IMU data and triggering stimulation changes when freezing events are detected, eliminating the need for continuous manual intervention and reducing the operational complexity burden on patients.
2Measurement precision
If real-time kinematic monitoring is added to detect abnormal movements, then the ability to identify freezing of gait improves, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer between the IMU sensors and the DBS system. The controller acts as a mediator that receives raw kinematic data from IMUs, processes it through algorithms to detect freezing of gait events, and then translates these detections into appropriate stimulation parameter adjustments. This intermediary approach enables precise detection while managing complexity by consolidating processing functions in a dedicated controller unit.
Solution Approach 2:
The system segments the monitoring and control functions into distinct modules: IMU sensors for data collection, controller for processing and detection, and DBS system for stimulation delivery. This segmentation allows each component to be optimized independently, improving measurement precision through specialized sensors and algorithms while managing overall device complexity through modular architecture.
3Stability of the object's composition
If stimulation parameters are modified in real-time based on predicted probabilities, then gait stability improves, but the extent of automation increases
Solution Approach 1:
The patent implements preliminary action by detecting freezing of gait events and adjusting stimulation parameters preemptively, before the patient experiences severe symptoms or falls. The system continuously monitors gait parameters, identifies emerging freezing events through probability calculations, and modifies stimulation in advance to prevent or mitigate the freezing episode, thereby improving gait stability through proactive rather than reactive control.
Solution Approach 2:
The system provides self-service by automatically detecting freezing events and adjusting stimulation parameters without requiring patient intervention or external medical professional input. The automated controller continuously processes IMU data, determines when freezing events are occurring based on predicted probabilities, and modifies DBS parameters autonomously, enabling the system to self-regulate and maintain gait stability independently.
Data Source
AI summary
Systems and methods for deep brain stimulation using kinematic feedback in accordance with embodiments of the invention are illustrated. One embodiment includes a deep brain stimulation system, including an implantable neurostimulator, a first inertial measurement unit (IMU), a second IMU, and a controller, where the controller is communicatively coupled to the implantable neurostimulator, the first IMU, and the second IMU, and where the controller is configured to obtain kinematic data from the first IMU and the second IMU, identify an abnormal movement event based on the kinematic data, and modify deep brain stimulation provided by the implantable neurostimulator based on the identified abnormal movement event.