Collaborate with MindInventory
Our Team developed advanced AI/ML algorithms to enable the harness to understand and interpret complex contexts within the user’s environment. Our solution focused on context-aware technology that assessed environments (e.g., indoors vs. outdoors, crowded vs. isolated spaces) and adjusted navigational assistance accordingly.
We leveraged AI to predict and recommend optimal paths for navigation in real-time, based on a comprehensive analysis of the user’s environment and historical path data. We used machine learning models that could predict potential hazards and changes in the surroundings before the user reached them, offering proactive alternative routes.
Apart from 3D spatial sounds, we planned to explore and integrate a multimodal feedback system using AI. This allowed us to customize feedback based on the environment and user preferences, potentially incorporating gentle vibrations for silent alerts, enhancing the safety and usability of the harness.
We constantly guided our client starting with the selection of appropriate AI technologies and models for implementing computer vision features. Our design and development team worked with the client to customize the mobile app backed by target user research.
To handle real-time data from the wide-angle cameras and other sensors efficiently, we enhanced the harness’s AI capabilities for immediate data processing and analysis. This involved optimizing algorithms for speed and efficiency, ensuring that users received timely and accurate navigational assistance without delays.
Our Computer Vision Programmers created AI/ML model specialized in enhanced object recognition and predictive analytics. These models are capable of accurately detecting a wider range of obstacles and predicting potential hazards in the path of blind or visually impaired users and hemispatial neglect patients.
In the UI UX design aspect, our designers focused on enhancing the user experience (UX) specifically tailored for blind and visually impaired individuals by considering simplification of navigation, menu structures facilitating easy interaction, and accessibility features.
Our team focused on establishing effective communication between the mobile app and low-frequency Bluetooth hardware components. This connectivity is crucial for enabling users to configure the hardware and access various features through the mobile app.
Improvement in Obstacle Avoidance Efficiency
Reduction in Navigation related Stress
Enhanced User Satisfaction related to GPS Instructions
