UWB and AI overview
A general view on the current UWB and AI products and the potentiality of UWB with generative AI developments and use cases
Here above the AI and UWB improving area and GPT related improvements
In 2013, initial research emerged on UWB machine learning, focusing on addressing limitations in identifying non-line-of-sight (NLOS) conditions. The initial approaches introduced a machine learning-based method that surpasses traditional methods reliant on IEEE802.15.4a channel models, which often exhibit significant discrepancies from actual environments, resulting in lower accuracy when applied in real settings.
As UWB tracking continues to evolve, several emerging trends are shaping its future. One trend is the optimization of the process node of UWB chips. With advancements in microelectronics, UWB ICs are improving on power-efficiency and performance. The lower power consumption permits an integration into a wide range of devices and applications avoiding the adoption of more expensive Li-Po batteries, with coin or stylus batteries. This trend opens up possibilities for UWB tracking in wearables, healthcare devices, and even everyday objects such as keys or wallets.
Another emerging trend is the fusion of UWB tracking with machine learning and artificial intelligence (AI). By leveraging the power of AI algorithms, UWB tracking systems can intelligently analyze and interpret the vast amount of ranging data generated by UWB sensors.
For instance, incorporates AI enhance the utilization of UWB location data in various ways:
1. Data Processing and Analysis: AI algorithms can process vast amounts of UWB-generated location data rapidly. These algorithms can clean, analyze, and interpret the data, identifying patterns, anomalies, and correlations that might not be immediately apparent to human analysis, saving time and labor force.
2. Localization and Tracking: AI can improve the accuracy and precision of location tracking using UWB signals. By continuously learning from the data collected, AI models can refine location estimation, enabling more accurate positioning of devices or assets.
3. Predictive Capabilities: Through machine learning, AI can predict future movements or behaviors based on historical UWB location data. This predictive capability might be used in various applications like anticipating the movement of objects or individuals in a given space.
4. Enhanced Security: AI can help in identifying unusual or suspicious patterns in UWB location data, potentially aiding in security applications. It can recognize deviations from normal behavior, alerting operators to potential security threats or breaches.
5. Optimizing Efficiency: AI can analyze UWB data to optimize workflows or resource allocation in various industries. For instance, in logistics, it can streamline inventory management by analyzing UWB location data to predict demand and manage stock levels efficiently.
The synergy between AI and UWB are already available in products, such as:
Kyungwoo Kigis IPAS system involves leveraging AI's capabilities to enhance the precision, reliability, and usability of location data obtained from UWB sensors across a wide array of applications.
IMEC, in 2020, developed a solution by integrating Ultra-Wideband (UWB) technology with machine learning, specifically targeting environments like factories and warehouses where constant movement of people and machinery, alongside metallic obstacles, disrupts the accuracy of UWB localization and distance measurements. Through the deployment of smart anchor selection algorithms driven by machine learning, IMEC has improved a method to detect line-of-sight (LOS) and non-line-of-sight (NLOS) conditions between UWB anchors and mobile devices under tracking. This knowledge is leveraged to evaluate ranging quality and rectify errors that arise during the ranging process.
IMEC's approach incorporates machine learning features enabling dynamic adjustment of the network's physical layer parameters. This adaptive tuning allows for precise mitigation of ranging errors by fine-tuning the radios of UWB anchors. This comprehensive solution aims to significantly enhance the accuracy and reliability of UWB-based localization in dynamic environments, ensuring more dependable tracking in settings of interference and reflective obstacles. This technology was adopted by IMEC’s spin-off Lopos for contact tracing application during Covid-19 social distance measurements in company environments.
Bondzai and Qorvo are teaming up by pairing DavinSy with Qorvo's Ultra-Wideband (UWB) products. Bondzai presents a complete AI+DSP (Artificial intelligence in digital signal processing) solution that leverages the potential of UWB technology. UWB's ability in indoor positioning and gesture recognition would be enhanced by DavinSy's incorporation of advanced deep learning and signal processing AI techniques. This fusion not only amplifies the capabilities of UWB sensors but also enhances customization and accuracy in various use cases, all without relying on cloud or server connectivity.
Bondzai that is developing Artificial Intelligence of Things (AIoT) with its innovative approach to redefining the relationship between humans, machines, and their operating environments. DavinSy is an industry-specific on-device software-defined AIoT system, accompanied by a tool called Maestro that is able to generate a tailored library for your specific hardware, facilitating real-world testing under actual conditions using a real hardware. These tools can enables the development of advanced tracking algorithms that can adapt to changing environments, accurately predict object movements, and optimize resource allocation. The synergy between UWB tracking and AI has the potential to revolutionize various industries, including retail, security, and smart cities.
Truesense has emerged in the competitive UWB landscape in the development of cutting-edge software and hardware modules, offering comprehensive solutions for UWB RF ranging and radar sensing. The Truesense Mate product portfolio showcases remarkable capabilities in delivering precise UWB sensing radar, enriched by AI algorithms, across a spectrum of use cases.
Truesense Mate leverages AI algorithms and the system serves as an intuitive companion, observing people's habits and behaviors discreetly without violating privacy. Other applications span across:
Toilet Visit Monitoring: Enabling discrete monitoring for healthcare and elderly care, ensuring safety and timely assistance.
Fall Detection: Offering an added layer of security by promptly identifying falls and triggering immediate alerts.
Routine Learning: Utilizing AI to understand and adapt to individual routines, optimizing convenience and efficiency.
Room Presence/Absence Detection: Enhancing home automation and security systems by detecting room occupancy.
Automotive In-Cabin Monitoring: Providing intelligent monitoring within vehicle cabins for enhanced safety and personalized experiences.
The Truesense Mate product line unifies AI ability to empower discrete, comprehensive monitoring without compromising privacy signifies a significant step forward in leveraging technology for seamless, more efficient, and personalized experiences.
As a further step beyond there is potentially a strong correlation between UWB and GPT (Generative Pre-training Transformer). By leveraging IoT to connect physical devices and sensors, UWB can achieve larger and more efficient data collection and analysis. This will make it easier for a generative AI to be applied in automation and data-driven decision-making processes across industries, realizing a closed loop from perception, interconnection to intelligent decision-making.
Specifically, the fusion of UWB technology with a generative Artificial Intelligence (AI) could enable an enhancement in the retail landscape, reshaping how businesses optimize customer experiences within retail shops, supermarkets, and shopping malls and smart city environments like: stations, airports and other infrastructures. Generative AI could enhance UWB positioning in three major areas, among others:
Improving location awareness and products location: One of the most pressing challenges in retail involves stagnant products occupancy. UWB and AI collaboration offer a solution by optimizing product placement, ensuring a more engaging shopping experience and curbing the problem of out-of-stock items. Moreover, the capability of showing the products promotions is nearby. This data aids in identifying customer preferences, enabling retailers to strategically position products, create effective displays, and even suggest complementary items in real-time, enhancing customer engagement.
Predictive analytics: delivering targeted promotions, managing checkout queues efficiently, and analyzing customer behavior to tailor store layouts and displays to specific demographics.
Predictive demand forecasting: the adoption of UWB and AI will shift conventional demand forecasting by predicting consumer behavior before their shopping intent surfaces. By harnessing these technologies, retailers gain deeper insights from big data sets, allowing them to anticipate customer preferences swiftly and act proactively.
In a nutshell, the adoption of UWB and AI in retail settings holds tremendous potential. Enabling centimeter accuracy and intelligence capabilities, in order to deliver and enhance: customer satisfaction, sales, and staying competitive.