AI-Driven Elderly Monitoring Platform Development -- 3
Budget / Salary$10,000–20,000
TypeFreelance project
LocationRemote
Posted1 hour ago
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 monitoring
Event retry
Notification retry
Duplicate-event prevention
Offline handling
Alert escalation timers
Audit logs
Alert acknowledgement tracking
Database backups
System monitoring/logging
Role-based access control
High availability for production
We need a clear strategy for what happens when the watch temporarily loses network connectivity.
12. Security & Privacy
Implement:
Encryption in transit and at rest
Secure device authentication
Role-based permissions
Family/caregiver permissions
Monitoring-centre permissions
Audit logs
Secure API authentication
Data retention controls
Consent management
Please identify applicable privacy/data-protection requirements for the countries where this will operate.
13. Development Phases
Phase 1 – MVP
ETA5 integration
Backend/cloud
Database
Monitoring dashboard
Family app
GPS
SOS
Fall events
HR/SpO₂ and available sensor data
Real-time notifications
Phase 2 – AI
Personalized baselines
Activity anomaly detection
Sleep anomaly detection
Vital-sign anomaly detection
Location anomaly detection
Multi-signal analysis
AI explanations
Risk prioritization
False-positive reduction
Phase 3 – Production
High availability
Multiple monitoring centres
Multiple operators/families
Advanced escalation
Analytics/reporting
Security hardening
Production deployment
14. Questions for ETA5 Manufacturer
Please obtain written confirmation regarding:
SDK/API availability
Communication protocol
Real-time data access
GPS/sensor APIs
Fall/SOS APIs
ECG/accelerometer data
Webhook/MQTT support
Device provisioning/authentication
Firmware OTA
Battery/device status
Own-cloud support
Ability to build our own mobile app
Whether manufacturer's app is mandatory
Data ownership
API/data costs
OEM/custom firmware options
Production MOQ
15. Required Deliverables
Please provide:
System architecture
Technology stack
ETA5 integration plan
API/SDK requirements
Database architecture
AI/ML architecture
Monitoring dashboard wireframes
Mobile app wireframes
Security architecture
Cloud architecture
Development timeline and milestones
Development cost
Third-party/API/cloud costs
Testing strategy
Production deployment plan
Maintenance/support plan
The key product requirement is:
The platform should continuously learn what is normal for each elderly person, detect meaningful deviations, explain why an anomaly occurred, prioritize its severity, and immediately notify the monitoring centre and appropriate loved ones when human intervention may be required.
This should be developed as a real-time safety monitoring platform, not simply as a smartwatch health-tracking application.
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 monitoring
Event retry
Notification retry
Duplicate-event prevention
Offline handling
Alert escalation timers
Audit logs
Alert acknowledgement tracking
Database backups
System monitoring/logging
Role-based access control
High availability for production
We need a clear strategy for what happens when the watch temporarily loses network connectivity.
12. Security & Privacy
Implement:
Encryption in transit and at rest
Secure device authentication
Role-based permissions
Family/caregiver permissions
Monitoring-centre permissions
Audit logs
Secure API authentication
Data retention controls
Consent management
Please identify applicable privacy/data-protection requirements for the countries where this will operate.
13. Development Phases
Phase 1 – MVP
ETA5 integration
Backend/cloud
Database
Monitoring dashboard
Family app
GPS
SOS
Fall events
HR/SpO₂ and available sensor data
Real-time notifications
Phase 2 – AI
Personalized baselines
Activity anomaly detection
Sleep anomaly detection
Vital-sign anomaly detection
Location anomaly detection
Multi-signal analysis
AI explanations
Risk prioritization
False-positive reduction
Phase 3 – Production
High availability
Multiple monitoring centres
Multiple operators/families
Advanced escalation
Analytics/reporting
Security hardening
Production deployment
14. Questions for ETA5 Manufacturer
Please obtain written confirmation regarding:
SDK/API availability
Communication protocol
Real-time data access
GPS/sensor APIs
Fall/SOS APIs
ECG/accelerometer data
Webhook/MQTT support
Device provisioning/authentication
Firmware OTA
Battery/device status
Own-cloud support
Ability to build our own mobile app
Whether manufacturer's app is mandatory
Data ownership
API/data costs
OEM/custom firmware options
Production MOQ
15. Required Deliverables
Please provide:
System architecture
Technology stack
ETA5 integration plan
API/SDK requirements
Database architecture
AI/ML architecture
Monitoring dashboard wireframes
Mobile app wireframes
Security architecture
Cloud architecture
Development timeline and milestones
Development cost
Third-party/API/cloud costs
Testing strategy
Production deployment plan
Maintenance/support plan
The key product requirement is:
The platform should continuously learn what is normal for each elderly person, detect meaningful deviations, explain why an anomaly occurred, prioritize its severity, and immediately notify the monitoring centre and appropriate loved ones when human intervention may be required.
This should be developed as a real-time safety monitoring platform, not simply as a smartwatch health-tracking application.
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