Wearable Device for Movement Disorder Stimulation
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Solution Overview
Problem
Current wearable devices for targeted peripheral stimulation lack effectiveness in accurately detecting and responding to movement disorder symptoms, such as Parkinson's disease, with limited durability and inconsistent stimulation delivery.
Innovation Solution
A wearable device equipped with sensors and a processor that detects movement disorder symptoms, calculates their stage of onset, and generates stimulation output to apply to the peripheral nervous system, using a combination of sensors, machine learning algorithms, and stimulators to provide tailored vibrational, electrical, or thermal stimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If wearable devices use basic sensors and simple stimulation algorithms, then device complexity is reduced, but effectiveness in detecting and responding to movement disorder symptoms deteriorates
Solution Approach 1:
The device segments the detection and stimulation process into distinct functional modules: sensors for detecting movement parameters, processors for analyzing data and determining symptom stages, and stimulators for delivering targeted peripheral stimulation. This segmentation allows each component to be optimized independently while maintaining overall system effectiveness.
Solution Approach 2:
The device dynamically adapts stimulation parameters based on real-time sensor data and determined symptom stages. The processor continuously monitors movement parameters and adjusts stimulation intensity, frequency, and duration according to the user's current symptom severity and responsiveness, making the system effective without requiring permanently high complexity.
2Reliability
If wearable devices use advanced sensor integration and machine learning algorithms, then effectiveness in symptom management is improved, but device complexity increases
Solution Approach 1:
The device performs preliminary actions by pre-programming multiple symptom stages and corresponding stimulation protocols. The processor compares real-time sensor data against predetermined thresholds and stage criteria, eliminating the need for complex real-time machine learning calculations while achieving effective symptom stage determination and appropriate stimulation delivery.
3Reliability
If wearable devices provide consistent stimulation delivery, then reliability is improved, but adaptability to different symptom stages and user responsiveness decreases
Solution Approach 1:
The device implements feedback mechanisms where sensors continuously monitor movement parameters and user responsiveness to stimulation. The processor uses this feedback to determine symptom stages and adjust stimulation parameters in real-time, ensuring both consistent delivery according to established protocols and adaptation to individual user needs and symptom variations.
Data Source
AI summary
In an aspect, a wearable device for targeted peripheral stimulation is presented. The wearable device includes a sensor configured to receive data and generate sensor output. The wearable device includes a processor in communication with the sensor. The wearable device includes a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive the sensor output from the sensor, the sensor output indicative of one or more movement disorder symptoms of a user. The processor is configured to determine a disease state of the one or more movement disorder symptoms. The processor is configured to generate a stimulation output based on the one or more movement disorder symptoms and the disease state of the movement disorder symptoms.