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Development of a Mobile Application for Continuous Parkinson's Disease Symptom Monitoring and Assessment Using Machine Learning

sevencollab

Medical

Health & Fitness

Information Technology

Challenges in Parkinson's Disease Symptom Tracking

Current Parkinson's disease management relies on infrequent, subjective clinical assessments that fail to capture symptom fluctuations. This leads to suboptimal treatment adjustments and limited understanding of disease progression between appointments.

About the Client

A health technology company specializing in digital solutions for neurological disorders

Project Goals

Develop a mobile application for continuous symptom monitoring
Implement machine learning algorithms for objective disease progression analysis
Enable real-time data collection through smartphone sensors
Improve early detection capabilities
Provide clinicians with actionable patient health insights

Core System Capabilities

Accelerometer-based motor function analysis
Reaction time testing through finger tapping exercises
Balance and gait assessment using motion sensors
Machine learning model for symptom progression tracking
Secure patient data storage and clinician reporting dashboard

Technology Stack

Kotlin

Custom accelerometer data analysis tool

REST API

System Integrations

Health data APIs (Apple HealthKit/Google Fit)
Cloud storage for medical data
Clinician portal integration

Quality Attributes

HIPAA-compliant data security
Real-time sensor data processing
High-accuracy algorithm validation
Cross-device compatibility

Expected Outcomes

Enables 24/7 disease monitoring for improved treatment personalization, reduces clinic visit frequency by 40%, enhances early intervention capabilities through objective data collection, and improves patient quality of life through proactive symptom management.