Microphone, pulse, and emotion sensors fuse together in real-time to classify threats before they escalate.
Watch real-time threat classification from sensor fusion
"The bracelet listens to sounds around you and feels your heartbeat. If something scary is happening — like yelling or your heart racing — it tells your parents right away, even before you ask for help."
Nine specialized ML models run on-device: voice stress analysis, environmental audio classification (glass break, screaming, gunshots), heart rate variability anomaly detection, galvanic skin response correlation, motion pattern analysis, and multi-sensor fusion scoring.
Each model produces a confidence score (0-100%). The fusion engine weights and combines these scores using a Bayesian network to produce a single threat level: Safe, Caution, Warning, or Danger.
When the threat level exceeds the guardian-configured threshold, the bracelet sends an encrypted alert with GPS coordinates, audio snippet (if enabled), and sensor readings to all registered guardians.
The system distinguishes between play-fighting and real danger, roller coasters and car accidents, swimming and drowning — by cross-referencing multiple sensor streams simultaneously.
| ML Models | 9 on-device neural networks |
| Audio Analysis | Glass break, screaming, gunshots, crying |
| Heart Rate | PPG sensor, ±2 BPM, continuous HRV |
| Skin Response | GSR electrodes, stress correlation |
| Motion Analysis | 6-axis IMU, defensive gesture detection |
| Fusion Engine | Bayesian network, 4-level classification |
| Response Time | <2 seconds from detection to alert |
| False Positive Rate | <3% (multi-sensor validation) |
| Processing | On-device, no cloud dependency |
| Alert Delivery | Push notification + SMS fallback |
Real-time threat classification from sensor fusion
SafeStep's onboard ML model continuously analyzes 7 sensor streams to detect danger before it escalates. Voice stress, heart rate, skin conductance, and ambient audio fuse into a single threat score updated 10 times per second.
Watch how SafeStep's ML model detects threats in real-time
Child playing at a playground with friends, elevated activity but no distress signals
Elevated heart rate consistent with physical play. Voice patterns show excitement, not distress. GSR within normal range for active play. Ambient audio classified as 'playground/children playing'. No threat indicators detected.
Continue passive monitoring. No alerts sent.
Seven sensors sample simultaneously at 10-100Hz: voice stress, heart rate, HRV, galvanic skin response, voice pitch, ambient audio, and a composite emotion index. Each stream is compared against the child's personal baseline calibrated during the first 72 hours of wear.
A Random Forest classifier handles instant pattern matching (response <50ms) while an LSTM neural network analyzes temporal sequences over 30-second windows. The ensemble model cross-validates both outputs, reducing false positives by 73% compared to single-model approaches.
The raw threat score is adjusted by context: time of day, location type (school vs park vs home), recent activity level, and guardian proximity. A child's elevated heart rate at a playground scores differently than the same reading at 2 AM. Only confirmed threats trigger the alert cascade.
SafeStep's danger prediction improves over time, learning your child's unique baseline patterns to reduce false alarms while catching real threats faster.
29-Sensor Intelligence
Twenty-nine sensors work together to distinguish between safe play and real danger — swimming vs drowning, roughhousing vs assault.
Pre-Impact Fall Detection
Predictive algorithms detect free-fall signatures before impact, classifying severity from minor tumbles to critical falls.
Hot Car Detection
Automatic temperature monitoring with escalating alerts and auto-911 dialing when a child is left in a hot vehicle.
Ready to protect your child?