Low-latency Wearable AI Navigation & Streaming

Overview

Travel Hands pairs Visually Impaired People (VIPs) with trained volunteers for guided outdoor navigation. While their initial system facilitated over 1,500 in-person trips for 400 active users, relying only on physical guides left up to 40% of journey requests unmatched, which prompted the team to move toward remote wearable assistance.

26bitz engineered a multimedia bridge to transition their concept into a functional prototype. By integrating the Meta Wearables SDK with real-time WebRTC video streaming protocols, we established a low-latency architecture that captures live video from smart glasses, streams it to a cloud server, and generates AI voice support for the user.

Live streaming trade-offs

Streaming video continuously from a wearable device forces compromises between speed, mobile bandwidth, and cloud processing costs. Sending non-stop video feeds into an AI model gets too slow for live walking navigation and far too expensive to scale.

We helped Travel Hands resolve a dilemma:

• Speed vs. Quality: Fast responses required for user safety conflict with sending heavy, high-resolution video over mobile networks which causes delays.
• AI bills: Feeding live video into an LLM burns millions of processing tokens per trip, creating unsustainable running costs.

AI integration with wearables

26bitz developed the media streaming pipeline and AI vision app. Along the way, we solved 3 major technical hurdles:

• Hardware-free testing: Built a mock device to simulate camera feeds, enabling software development and testing without physical smart glasses.
• Custom WebRTC: Refined encoding parameters and adjusted our video pipeline to handle different aspect ratios and frame sizes without stretching or distorting.
• Audio quality: Bypassed default Android Bluetooth phone call mode to enforce high-quality audio, maintaining clear environmental sound transmission.

Results

We turned the Travel Hands concept into a wearable app:

• Low-latency & low-cost: Instead of streaming non-stop video, the app takes single frame snapshots when prompted. This delivers fast guidance while cutting AI processing costs.
• Cloud storage: Video recordings upload directly to AWS S3, creating a dataset for AI models training.
• 2-week delivery: Built and deployed the Android app, cloud video server, and AI vision features.

Testimonial

“26bitz delivered on time. The team communicated well all along and was supportive. We had clear communication. I was impressed by the smoothness of the operations. Everything was seamless.”
— Ishan Jha, CEO of Travel Hands

What’s Next

Travel Hands plans to test the prototype with users and build an operator interface. Long term, they aim to add AI-assisted navigation, map integration and connect the remote streaming solution into their Voice Assistant application.