MEDICAL
Understanding Near Field LiDAR and AI
Advancements in technology have always been a cornerstone for progress in the healthcare sector. Each innovation has significantly shaped and improved patient outcomes, from stethoscopes to MRI machines. One such breakthrough, the combination of near-field LiDAR (Light Detection and Ranging) and Artificial Intelligence (AI), holds the promise to usher in a new era for healthcare. Let’s explore how.
L
iDAR is a remote sensing technology that uses laser light to measure distances to objects. Traditionally used in applications such as autonomous vehicles and topographic mapping, LiDAR systems provide detailed and accurate spatial information about the surrounding environment.
Near Field LiDAR, as the name suggests, is designed for short-range applications. Integrating AI’s data processing and pattern recognition capabilities opens up possibilities in various sectors, including healthcare.
Revolutionizing Healthcare: The Potential of Near Field LiDAR AI
1. Remote Patient Monitoring: One of the critical challenges in healthcare is monitoring patients in real-time, especially those with chronic conditions. With Near Field LiDAR AI, monitoring subtle physiological changes or movements from a distance becomes possible, enabling healthcare providers to respond promptly to any anomalies.
2. Fall Detection: Falls among the elderly can lead to severe injuries. Equipping healthcare facilities or homes with Near Field LiDAR AI systems can help detect falls in real time and instantly alert caregivers or medical professionals. 3. Rehabilitation: Proper movement is crucial for patients recovering from surgeries or injuries. LiDAR AI systems can analyse and guide movements during physiotherapy sessions, ensuring that exercises are done correctly. 4. Surgical Assistance: Precision is vital in surgeries. Near Field LiDAR can offer real-time, high-resolution imaging to aid surgeons during procedures, ensuring optimal outcomes and reducing potential
complications. 5. Wound Analysis: Using Near Field LiDAR AI, healthcare professionals can obtain detailed 3D images of wounds, helping inaccurate size and depth assessment. This can guide treatment strategies and monitor healing progress. 6. Patient Interactions: Traditional means of communication can be challenging for patients with communicative disorders or those in intensive care. LiDAR AI systems can interpret minor movements or gestures, enabling a new mode of interaction between patients and caregivers. 7. Crowd Monitoring in Hospitals: Managing patient flow and ensuring social distancing becomes crucial in situations like pandemics. Near Field LiDAR AI can help in real-time crowd monitoring and management in hospitals or clinics.
“ 40 MARCH 2024 | ELECTRONICS FOR ENGINEERS
Traditional means of communication can be challenging for patients with communicative disorders or those in intensive care.
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substantial initial investments for optimal utility.
Challenges and Considerations While the potential is vast, there are challenges to consider: - Data Privacy: The constant monitoring capability of LiDAR AI means a vast amount of data about individuals is collected. Proper measures must be in place to ensure data privacy and security. PreAct excels at this due to their privacy-by-design approach. - Integration with Existing Systems: LiDAR AI systems must integrate seamlessly with existing healthcare infrastructures, requiring
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- Training and Adaptation: Medical professionals need to be trained to understand and utilise the insights provided by LiDAR AI efficiently.
Conclusion
Integrating Near Field LiDAR and AI in healthcare is an exciting frontier, with the potential to reshape patient care, improve outcomes, and streamline operational efficiencies. As with any technological advancement, careful consideration and implementation will be critical to unlocking its full potential. However, with the continued pace of innovation, it’s only a matter of time before we see this technology becoming an integral part of global healthcare systems. Paul Drysch, CEO.
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