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Development of a Modern iOS-Based SDK for Real-Time Contamination Detection and Cleaning Efficiency Assessment

altoroslabs.com

Environmental Services

Healthcare

Facilities Management

Legacy System Limitations Hindering Operational Efficiency

Existing contamination detection system suffers from outdated technology stack incompatible with modern devices, 2-minute processing times for image analysis, frequent crashes due to memory constraints, manual lamp operation errors, and hardcoded parameters limiting detection precision.

About the Client

Provider of UV-based contamination detection systems for auditing cleaning procedures in high-traffic facilities

Key Project Goals

Migrate proprietary algorithm to modern iOS-compatible technology stack
Reduce image analysis processing time to under 5 seconds
Create white-label SDK for standalone or embedded deployment
Automate lamp control sequence to eliminate human error
Implement customizable contamination detection parameters

Core System Capabilities

GPU-accelerated image analysis algorithm
Bluetooth-enabled lamp control interface
Customizable hue parameter configuration
Automatic camera parameter calibration
Memory-optimized image scaling mechanism

Technology Stack Requirements

Swift

Metal Shading Language

Core Bluetooth

AVFoundation

OpenCV

System Integration Needs

iOS camera API
Bluetooth-enabled UV/LED lamp hardware
Memory management framework

Performance Criteria

Sub-5 second processing time for 20-image analysis
Memory-efficient image handling for iOS devices
Cross-device compatibility with iPad models
Precision optimization for contamination detection

Anticipated Business Outcomes

Enables 3-second cleaning efficiency assessments with 95%+ accuracy, reduces hardware dependency errors by 100%, supports scalable SDK distribution model, and validates market viability through university campus deployments.