A Contactless Edge-AI Prototype for Simulated Apnea-like Respiratory Suppression and Motion Artifact Detection Using 60 GHz FMCW Radar
| dc.contributor.author | Pairoch, Sathit | |
| dc.contributor.author | Phasukkit, Pattarapong | |
| dc.contributor.author | Houngkamhang, Nongluck | |
| dc.date.accessioned | 2026-08-06T10:55:57Z | |
| dc.date.available | 2026-08-06T10:55:57Z | |
| dc.date.issued | 2026-07-01 | |
| dc.description.abstract | Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts. | |
| dc.identifier.citation | Technologies, 14(7), 2026 | |
| dc.identifier.doi | 10.3390/technologies14070388 | |
| dc.identifier.issn | 22277080 | |
| dc.identifier.other | 2-s2.0-105045902030 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/18213 | |
| dc.source | Technologies | |
| dc.subject | 1D-CNN | |
| dc.subject | 60 GHz FMCW radar | |
| dc.subject | contactless respiratory monitoring | |
| dc.subject | edge-AI | |
| dc.subject | low-power inference | |
| dc.subject | MAX78000 | |
| dc.subject | motion artifact | |
| dc.subject | respiratory motion suppression | |
| dc.subject | TinyML | |
| dc.title | A Contactless Edge-AI Prototype for Simulated Apnea-like Respiratory Suppression and Motion Artifact Detection Using 60 GHz FMCW Radar | |
| dc.type | Article |