Advancements in Physical AI: Wi-Fi 7, Generative Models, and Wearable Technologies

Ceva, a US-based technology company, has launched its Wi-Fi 7 1×1 client IP, designed to enhance the capabilities of AI-enabled Internet of Things (IoT) devices and what it refers to as "physical AI systems." This technology aims to provide high-performance, low-latency connectivity essential for compact, battery-operated devices such as wearables and smart home systems. The Wi-Fi 7 standard, based on IEEE 802.11be, is anticipated to facilitate Edge intelligence, enabling real-time data processing in devices that utilize embedded AI. Market analysts project that annual Wi-Fi chipset shipments will surpass 5.5 billion by 2030, with Wi-Fi 7 accounting for over 1.9 billion units. Ceva's solution supports Multi-Link Operation (MLO) and enhanced Multi-Link Single Radio (eMLSR) technologies, which improve reliability and reduce latency.

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The Korea Advanced Institute of Science and Technology (KAIST) has developed a generative AI model named MPMAvatar, which learns movement and interaction in 3D space while adhering to physical laws. This model addresses the limitations of existing 2D-based video AI by reconstructing multi-view images into 3D space using Gaussian Splatting and the Material Point Method (MPM). The technology enables realistic movement and interaction of avatars, significantly reducing the need for traditional motion capture techniques. Professor Tae-Kyun Kim emphasized that this advancement marks a pivotal moment towards achieving Artificial General Intelligence (AGI), as it allows AI to understand the physical world more comprehensively.

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VusionGroup has introduced its EdgeSense Connected Store Platform, integrating AI applications and generative models to create AI-native retail environments. This platform transforms connected stores into intelligent spaces where products and shoppers interact through real-time spatial intelligence. By merging 3D locationing, computer vision, and natural language processing, EdgeSense AI enables stores to respond dynamically to customer needs. Roy Horgan, SEVP of strategy at VusionGroup, stated that this innovation marks a shift from cloud-based AI to spatially aware systems capable of understanding context in real-time.

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Researchers at the University of Michigan have developed a machine learning model that utilizes data from wearable sensors to provide feedback during balance training exercises. This model predicts how physical therapists would rate patients' performance, potentially aiding in remote care and improving patient outcomes. Kathleen Sienko, a senior author of the study, highlighted the model's nearly 90% accuracy in predicting balance ratings, which could enhance rehabilitation for patients with mobility challenges.

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The advancements in physical AI, as demonstrated by Ceva, KAIST, VusionGroup, and the University of Michigan, signify a transformative shift in how AI interacts with the physical world. These technologies not only enhance user experiences across various sectors, including retail and healthcare, but also pave the way for more autonomous and intelligent systems. As the integration of AI into everyday environments continues to evolve, the potential for improved efficiency, personalization, and accessibility becomes increasingly apparent.

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• “Wi-Fi 7 is set to transform IoT by enabling the low-latency, high-throughput connectivity required for real-time edge intelligence and physical AI,” — Andrew Zignani, Senior Research Director, ABI Research

Ceva unveils Wi-Fi 7 client IP to accelerate AI-enabled IoT and physical AI devices

• “> Professor Tae-Kyun (T-K) Kim explained, "This technology goes beyond AI simply drawing a picture; it makes the AI understand 'why' the world in front of it looks the way it does.” — Professor Tae-Kyun Kim, KAIST

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• “With EdgeSense AI, we are making the physical world natively intelligent,” — Roy Horgan, SEVP, VusionGroup

VusionGroup unveils EdgeSense AI: bringing generative intelligence into physical eetail

• “Become a supporter and enjoy The Good Men Project ad free “Understanding what the patient and the therapist need has to be part of the algorithms we put together.” — Kathleen Sienko, University of Michigan

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While the advancements in physical AI are widely recognized, the specific applications and effectiveness of these technologies in real-world scenarios remain to be fully validated. Further research and pilot projects will be essential to assess their impact across various industries.

The Globe and Mail (Toronto)