Technical Inquiry Regarding Human and Pet Classification Using 77–81 GHz FMCW Radar Hello, My name is Eunjin Kwon, and I am a researcher in the Eco-Friendly Building Research Team at GS Engineering & Construction in South Korea. We are currently exploring the application of mmWave radar technology for non-contact fall detection and activity monitoring in residential environments. One of our key technical requirements is to distinguish humans from pets (such as dogs and cats) to prevent false alarms caused by pet movements while ensuring reliable fall detection for human occupants. We are particularly interested in NXP's 77–81 GHz FMCW radar solutions and would appreciate your technical guidance on the following questions: 1. Does NXP have any existing technologies, research results, or application examples related to human versus pet (Human / Non-human) classification using mmWave radar? 2. Is it technically feasible to distinguish humans from pets using radar data, such as point clouds and Doppler information, through AI-based classification algorithms? 3. Are there any relevant reference designs, technical documents, or development resources available for this application? We would also appreciate the opportunity to discuss this topic with an NXP radar technical specialist or Field Application Engineer (FAE) in South Korea. Thank you for your time and assistance. I look forward to your response. Best regards, Eunjin Re: Technical Inquiry Regarding Human and Pet Classification Using 77–81 GHz FMCW Radar Hello,
Let me look closer if we have done something similar as our RADARs are automotive.
Best regards,
Peter Re: Technical Inquiry Regarding Human and Pet Classification Using 77–81 GHz FMCW Radar Hello,
1. Does NXP have any existing technologies, research results, or application examples related to human versus pet (Human / Non-human) classification using mmWave radar?
NXP does not currently offer a ready-to-use 77–81 GHz reference solution specifically for residential fall detection and human-versus-pet classification. NXP’s 77–81 GHz radar portfolio is primarily intended for automotive radar applications.
2. Is it technically feasible to distinguish humans from pets using radar data, such as point clouds and Doppler information, through AI-based classification algorithms?
Human/pet classification using radar is technically feasible in principle. Machine-learning models can use features such as range, velocity, Doppler or micro-Doppler characteristics, spatial distribution and movement over time. Nevertheless, this would require customer-specific data collection, model training and validation under representative residential conditions. Robust performance cannot be guaranteed based on point-cloud data alone; lower-level range-Doppler or range-angle data may be beneficial.
3. Are there any relevant reference designs, technical documents, or development resources available for this application?
NXP can provide radar evaluation hardware and signal-processing resources based on the SAF85xx platform to support feasibility evaluation and custom development. These resources do not include a finished fall-detection or pet-classification algorithm.
We would also appreciate the opportunity to discuss this topic with an NXP radar technical specialist or Field Application Engineer (FAE) in South Korea.
Please get in touch with NXP representative Sales/FAE assigned to your company or with your distributor so the separate support channel can be established.
Best regards, Peter
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