Related Experiment Video
Updated: Jun 9, 2026
Home-Based Monitor for Gait and Activity Analysis
Published on: August 8, 2019
Automated contactless radar-derived movement index for outpatient motor surveillance
Seung Hyun Kim1, Jun Byung Park2, Jae Kyoon Hwang3
1Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Insights
A novel radar-based system accurately identifies infants at neurodevelopmental risk by analyzing movement patterns. This contactless, automated tool supports early detection and serial surveillance for timely intervention.
Area of Science:
- Pediatric neurology
- Developmental pediatrics
- Biomedical engineering
Background:
- Early identification of neurodevelopmental risks in infants is crucial.
- Repeatable outpatient screening tools are needed for timely intervention.
- Current methods may not be sufficient for widespread, early screening.
Purpose of the Study:
- To assess the feasibility of an automated, contactless radar-based movement analysis for infant screening.
- To evaluate the accuracy of a radar-based system in identifying neurodevelopmental risks.
- To determine if radar technology can support serial surveillance in outpatient settings.
Main Methods:
- Frequency-modulated continuous-wave (FMCW) radar was used for contactless movement analysis in 77 infants (37-60 weeks postmenstrual age).
- A pretrained model analyzed movement epochs to compute the NeuroRiskAbility (NRA) index.
- NRA classification was compared against clinical neurological and developmental assessments.
Main Results:
- An automated radar-based index (NRA) achieved 93% concordance with clinical classification after recalibration.
- NRA values did not show a significant association with postmenstrual age.
- Clinical classifications changed in 61% of infants assessed twice, highlighting the need for serial monitoring.
Conclusions:
- A brief, non-contact radar system can generate an automated movement-based index with high accuracy for infant screening.
- This technology supports the feasibility of serial surveillance for identifying infants needing closer follow-up.
- The low-burden, privacy-preserving tool may enable scalable, early identification of motor delays.
Background:
Repeatable outpatient screening tools are needed to support early identification of infants at neurodevelopmental risk. We assessed the feasibility of an automated, contactless radar-based movement analysis for outpatient screening in infants without overt neurological concerns.
Methods:
Infants born at 29-41 weeks of gestation underwent outpatient assessments between 37-60 weeks of postmenstrual age; infants with congenital anomalies, major brain injury, or clinical instability were excluded. Seventy-seven infants contributed 100 assessments. Each visit included a 450-second frequency-modulated continuous-wave (FMCW) radar recording. A pretrained model classified 45 non-overlapping 10-second epochs for asymmetric movements and cramped-synchronized general movements, and the NeuroRiskAbility (NRA) index was computed. NRA-based classification was compared with the outpatient clinical classification based on neurological and developmental assessment, including developmental concerns warranting follow-up.
Results:
With prespecified parameters (α = 0.5, cutoff 20), concordance with clinical classification was 58%. Exploratory within-cohort recalibration (α = 0.4, cutoff 24) increased concordance to 93% (κ = 0.86). NRA values showed no monotonic association with postmenstrual age (ρ = 0.045, p = 0.658). Among 23 infants assessed twice, 14 (61%) changed clinical classification between visits.
Conclusion:
A brief, non-contact radar-based model can generate an automated movement-based index with high concordance to outpatient clinical classification, supporting feasibility for serial surveillance to identify infants requiring closer follow-up in early infancy.
Impact:
Contactless frequency-modulated continuous-wave (FMCW) radar enables brief outpatient recordings to quantify spontaneous infant movements and generate an automated movement-based index with high concordance to routine outpatient clinical classification. Routine outpatient clinical classification often changed over short intervals in infants without overt neurological concerns, highlighting the need for serial surveillance. A low-burden, privacy-preserving, automated tool may enable scalable serial assessment in outpatient follow-up, supporting earlier identification of infants at risk for motor delay who may benefit from closer monitoring or referral for formal assessment.
