AI-Driven Elderly Monitoring Platform Development -- 3

I want to develop an AI-powered remote elderly monitoring platform using the ETA5 4G elderly safety smartwatch as the wearable device. Product reference: https://fitnesstrackerchina.com/products/elderly-safety-gps-smart-watch-4g-sos-eta5 The solution should consist of: ETA5 smartwatch integration Cloud/backend platform AI anomaly-detection engine Monitoring Centre web dashboard Family/loved-one iOS and Android app Real-time emergency alert and escalation system 1. ETA5 Integration Please obtain the manufacturer's SDK/API/protocol documentation and determine whether we can integrate the watch directly with our own cloud platform. We need access, where supported, to: GPS/location SOS events Fall detection Heart rate SpO₂ Blood pressure Temperature ECG/HRV Steps/activity Sleep Movement/accelerometer data Battery level Device online/offline status Timestamped sensor data Device/firmware status Please confirm whether data can be received through API, MQTT, HTTP, SDK, webhook or another real-time protocol. The objective is to avoid depending solely on the manufacturer's existing mobile application. 2. AI Anomaly Detection The most important feature is personalized AI monitoring. The AI should learn each elderly person's normal baseline rather than relying only on fixed thresholds. It should learn patterns such as: Normal resting heart rate Normal activity/steps Normal walking/movement Normal sleep duration and schedule Normal GPS locations Normal nighttime activity Normal SpO₂ Normal temperature Other available sensor patterns The system should continuously compare new data with the person's historical baseline. Example: If a person normally walks 4,000 steps/day but suddenly walks 800, the AI should detect a significant deviation. If reduced activity occurs together with unusual heart rate or SpO₂, the system should increase the priority. Every AI alert should explain why it was generated, e.g.: "Activity is 65% below the individual's 30-day baseline and no significant movement has been detected for 3 hours." The AI should identify potential risks/anomalies and assist human monitoring staff; it should not claim to diagnose medical conditions. 3. Alert Classification Implement four operational levels: Green – Normal No significant anomaly. Yellow – Watch Minor deviation requiring observation. Orange – Attention Significant anomaly requiring monitoring-centre review. Red – Critical Immediate action required. Critical events may include: SOS pressed Serious/suspected fall No response after suspected fall Critical configured sensor event High-risk geofence/wandering event Multiple concerning sensor anomalies 4. Immediate Alerts Critical events must be generated centrally by the backend and immediately sent to: Monitoring Centre Family/loved-one mobile app Configured emergency/care contacts The family app must NOT be responsible for detecting emergencies. For critical events, support: Real-time dashboard alert Push notifications SMS fallback where configured Escalation rules Operator acknowledgement Calling the elderly person's watch Family notification Complete alert history/audit trail 5. Fall Detection When a fall event is received: Create an immediate backend event. Notify monitoring centre. Notify family according to configured rules. If supported, ask the person through the watch: "Are you okay?" Allow a configurable response period. If they confirm they are okay, record/cancel the event. If there is no response, escalate. Allow the monitoring operator to call the watch. Record all actions. An SOS button press should immediately create a critical alert without waiting for AI confirmation. 6. Monitoring Centre Dashboard Create a professional web-based command centre showing: Total residents Green/Yellow/Orange/Red counts Live resident locations Critical alerts Unacknowledged alerts Device online/offline status Recent alerts Alert response times Resident detail should show: Current GPS location Heart rate SpO₂ Temperature Blood pressure where available Activity/steps Sleep Historical trends AI anomalies AI explanation Current alert status Device status Emergency contacts Operator actions: Call resident Contact family View live location Acknowledge Escalate Resolve Add notes View history 7. Family Mobile App Build iOS and Android applications. Main screen: Elderly person's current safety status Current location Heart rate SpO₂ Activity Sleep Last update Current alerts Sections: Home Health Location Alerts Profile/Settings Emergency notification should clearly show: "Emergency Alert – Possible fall detected." It should provide: Location Event time Relevant sensor information Call elderly person View location Call monitoring centre "I'm responding" option 8. Geofencing & Wandering Implement configurable safe zones such as home, hospital or other approved locations. Notify the monitoring centre/family when the person leaves an unexpected area. Eventually, AI should learn normal locations and travel patterns and identify unusual movement. 9. Backend Architecture Please propose a scalable architecture. Suggested components: API Gateway Device integration service MQTT/HTTPS Authentication PostgreSQL Time-series database/TimescaleDB Redis Event-processing service AI/ML service Notification service WebSocket/live-event service Monitoring Centre API Mobile API It must support multiple residents, families, operators and potentially multiple monitoring centres. 10. AI Architecture Use multiple AI components rather than one black-box model: Personalized baseline model Vital-sign anomaly detection Activity anomaly detection Sleep anomaly detection Location anomaly detection Fall-event classification Multi-signal correlation Risk/event prioritization The model should continuously improve the individual's baseline as more data becomes available. Every alert should include an understandable explanation. 11. Reliability & Safety Because this is a safety-related platform, implement: Device connectivity monitorin