Calmsy combines an ESP-based wearable (breathing sensor) with AI-driven analytics to spot stress patterns early, notify owners, and deliver tailored recommendations vetted by vets.
From sensor to suggestion in four simple steps.
Wearable flex-sensor tracks breathing to estimate BPM and variability.
Adaptive thresholds + peak detection flag anomalies and stress events.
Rule-based & fuzzy scoring label events as good/bad stress with intensity.
Push alerts, owner checklists, and vet-style recommendations in real time.
Calendar filters, history, and trends help you spot recurring stressors.
Firebase Auth + Firestore with user, dogs, devices, and events separation.
Everything you need to monitor and manage canine stress.
Assign ESP devices to dogs, track status, firmware, and connectivity.
Capture context (behavior, environment, events) during alerts.
Automated tips from your curated stressFactors library with vet tone.
Day / month / year filters and printable event summaries.
Immediate owner alerts when a stress event is detected.
Firestore collections: users, dogs, device, stressEvents, stressFactors, etc.
Soft-AP guided
Alert dispatch
Historical logs
Modular design
Calmsy is built as a BSCS thesis project: Modified Waterfall SDLC, rule-based classification with a future path to AI, and a Firestore schema for reproducible experiments.
Quick answers for common questions.
Questions or collaboration ideas? We’d love to hear from you.