Robotic Hallucination Prediction via Sensorimotor Stimulation
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Solution Overview
Problem
Current methods for diagnosing and managing hallucinations in Parkinson's disease patients are limited by the lack of objective biological markers and reliance on subjective patient reports, making it challenging to predict and treat these symptoms effectively.
Innovation Solution
A robotic system using a master-slave device with controlled sensorimotor stimulation to induce the feeling of presence (FoP) in patients, allowing for quantitative assessment of the likelihood of hallucinations through a 'hallucination stress test', which records and compares subjective responses and physiological data to identify potential hallucinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective patient reports and physician interviews are used for diagnosing hallucinations, then the diagnostic process is simple and non-invasive, but the measurement precision and objectivity are insufficient
Solution Approach 1:
The patent introduces a robotic master-slave system as an intermediary tool between the patient and physician. The system objectively induces and detects hallucinations through controlled sensorimotor stimulation, serving as a mediator that translates subjective experiences into measurable data without requiring complex invasive procedures
Solution Approach 2:
The patent replaces the purely subjective interview-based diagnostic method with a robotic system that uses controlled mechanical stimulation and sensory feedback. This substitution transforms the diagnostic process from subjective reporting to objective measurement while maintaining non-invasiveness
2Reliability
If a robotic master-slave system is used to induce hallucinations, then objective biological markers can be obtained, but the device complexity increases
Solution Approach 1:
The robotic system is designed to be self-regulating, where the master device automatically adjusts stimulation parameters based on real-time feedback from the slave device and patient responses. This self-service capability reduces the need for complex external control systems while maintaining high predictive reliability
Solution Approach 2:
The system incorporates continuous feedback loops where the slave device monitors patient physiological responses and feeds this information back to the master device for real-time adjustment of stimulation parameters. This feedback mechanism ensures reliable hallucination induction without requiring overly complex manual control systems
3Loss of time
If controlled sensorimotor stimulation is applied to induce feeling of presence, then early prediction of hallucinations is enabled, but the ease of operation decreases
Solution Approach 1:
The system performs preliminary actions by pre-programming stimulation protocols and safety parameters before patient interaction. The robotic system automatically executes standardized induction sequences, reducing the operational burden on clinicians while enabling early detection through proactive rather than reactive assessment
Solution Approach 2:
The robotic master-slave system is designed with universal applicability, where a single system can perform multiple functions including hallucination induction, physiological monitoring, and predictive analysis. This multi-functionality consolidates what would otherwise require multiple separate devices and procedures into one unified platform
Data Source
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AI summary
A robotic system and associated method for predicting the likelihood of occurrence of hallucinations in a subject is disclosed. The system comprises a robotic master-slave device and computer means adapted to induce conflicting sensorimotor stimulations to a subject for inducing the feeling of a presence. Comparing subjective response data, combined with data related to the movement of the master-slave device, with reference data, allows to predict the likelihood of occurrence of hallucinations in a tested subject. The system and the method for using thereof can find applications in Parkinson's Disease patients for the prognostic evaluation of psychiatric-associated symptoms.