Introduction
As artificial intelligence advances, its applications are expanding into new domains, including sports coaching. Embedded AI solutions can provide real-time feedback and guidance to athletes, helping them improve their technique and performance. However, developing these systems comes with unique challenges, particularly around latency and responsiveness.

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In this post, we'll explore some key considerations and approaches for developing embedded AI products for real-time coaching, drawing inspiration from Sparrow's experience building their Sparrow Coach platform. We'll discuss the importance of low-latency processing, on-device computation, and designing for seamless user experiences.

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The Criticality of Low Latency
In applications like sports coaching, where the AI system needs to provide immediate feedback based on the athlete's movements, minimizing latency is paramount. Even a delay of a second or two can make the feedback feel disconnected from the action, diminishing its effectiveness and degrading the user experience.

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Sparrow faced this challenge in building Sparrow Coach, which tracks a user's golf swing motion in real-time and provides audio feedback. To be effective, the system needs to analyze the motion and deliver guidance within a fraction of a second after the relevant movement occurs.

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To achieve this, embedded AI systems need to be architected for minimal latency at every stage of the pipeline, from data ingestion and preprocessing to model inference and output generation. Techniques like edge processing, model compression, and efficient data handling are crucial.

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On-Device Computation
One key architectural decision for embedded AI is whether to perform computation on-device or offload it to the cloud. For applications demanding real-time responsiveness, like Sparrow Coach, on-device computation is often necessary.

Processing data locally eliminates the latency overhead of sending data to the cloud and waiting for a response. It also provides reliability benefits, as the system can function independently of network connectivity. However, on-device computation introduces constraints around processing power, memory, and storage that must be carefully managed.

Sparrow made the decision to do all processing on-device for Sparrow Coach, accepting the trade-offs and challenges involved. This necessitated careful optimization of their AI models and processing pipelines to fit within the capabilities of the target devices.

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Graceful Performance Degradation
Even with on-device computation, there may be times when the embedded AI system is pushed beyond its processing limits, such as during rapid or complex movements. In these cases, it's important for the system to gracefully degrade its performance rather than failing entirely.

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This could involve strategies like dynamically adjusting the complexity of the AI models based on available resources, prioritizing the most critical aspects of the analysis, or providing slightly delayed feedback in extreme cases. The goal is to maintain a functional, if slightly impaired, user experience even under stress.

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Sparrow likely had to incorporate such techniques into Sparrow Coach to handle scenarios where the user's movements exceed the real-time processing capacity of the device. By designing the system to degrade gracefully, they can ensure that users still receive helpful feedback even in challenging conditions.

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Balancing Accuracy and Latency
In embedded AI applications, there is often a trade-off between the sophistication and accuracy of the AI models and the latency of the system. More complex models may provide more detailed or precise insights, but at the cost of longer processing times.

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For real-time coaching applications like Sparrow Coach, striking the right balance is critical. The AI models need to be accurate enough to provide meaningful and correct feedback, but fast enough to deliver that feedback in real-time.

This may require techniques like model pruning or quantization to reduce model complexity, or the use of specialized lightweight model architectures designed for embedded devices. It may also involve careful feature selection and preprocessing to focus the AI's attention on the most relevant data signals.

 

Conclusion
Developing embedded AI solutions for real-time sports coaching, as exemplified by Sparrow Coach, requires a laser focus on low-latency processing and on-device computation. By architecting the system for speed and designing for graceful performance degradation, developers can create AI coaching tools that provide immediate, relevant feedback to athletes.

As embedded AI continues to evolve and expand into new application areas, the lessons learned from projects like Sparrow Coach will be invaluable. By understanding the unique challenges and trade-offs involved, and by employing techniques to optimize for real-time performance, developers can create embedded AI solutions that deliver seamless, responsive user experiences.