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AI HEALTH MONITORING · OCCUPATIONAL SAFETY

Intelligent health monitoring built for
large service-industry operations.

An end-to-end occupational health system combining wearables, weak-network sync, AI risk alerts and lifetime health records. We replaced reactive checkups with continuous monitoring and early warnings — running across tens of thousands of employees, with billions of vitals data points and over a thousand alerts.

Book a 30-min consultationSee the architecture
Devices online
0units
Daily ingest
0B records
AI alert accuracy
0%
Weak-network sync success
0%
Live · Health Signals
24/7 · IoT Stream
Live heart rateBPM
78Normal range 60–100
Ingested today
4.8B records
Devices online
12,480
99.9% uptime
AI alerts · today
23events · 78% accuracy
Case highlights · 4 core capabilities
Wearable data capture
24/7 · tens of thousands of devices
AI risk alerts
Early detection · 78% accuracy
Long-term trend analysis
Health records · trend modeling
Active intervention
Alert to health response
Concurrency · 4.8B records/day across tens of thousands of devices/Data loss rate · 10% down to 0.1%/AI risk alerts · 1,287 actionable warnings to date/Alert accuracy · 78%/Project timeline · live in 6 weeks/Health records · 12,480 employees/5G + edge compute · weak-network resume/Guangdong enterprise client · long-term partnership/Concurrency · 4.8B records/day across tens of thousands of devices/Data loss rate · 10% down to 0.1%/AI risk alerts · 1,287 actionable warnings to date/Alert accuracy · 78%/Project timeline · live in 6 weeks/Health records · 12,480 employees/5G + edge compute · weak-network resume/Guangdong enterprise client · long-term partnership/
/ 01 PROJECT BACKGROUND

A large Guangdong enterprise wanted to
actually manage occupational health.

The client has decades of operational experience. Many of their staff work high-intensity, high-repetition, night-shift or outdoor jobs. Sub-clinical issues, chronic conditions, fatigue and mental stress had become real operational risks.

They wanted to use AI and IoT to monitor employee health in real time, build long-running health records, predict occupational disease risk, and cut accident rates at the source.

/ 01 · HIGH-RISK ROLES4 core roles covered
Public transit drivers
Long-haul fatigue risk
Working at height
Real-time vitals monitoring
Long-shift positions
Overnight health tracking
High-repetition labor
Chronic condition trends
/ 02 · CHRONIC RISKCommon health risks in high-load roles
Sub-clinical issues68%
Chronic occupational disease54%
Mental stress47%
Fatigue risk39%
/ 03 · CLIENT GOAL

“An AI monitoring and alerting platform for long-term occupational health.”

  • Monitor employee health in real time
  • Build long-running health records
  • Predict occupational disease risk
  • Reduce accident rates
/ 02 THE PROBLEM

Four real problems the client faced — the 4 problems

Discovery mechanism, data quality, network reliability, storage cost — every one had to be defined before building the system.
P-01 · PAIN POINT

“Health problems only surface after something goes wrong”

Annual checkups and manual surveys can't deliver continuous monitoring. Early chronic issues are easy to miss and compound over time into disease, fatigue driving and sleep disorders.

Checkup interval (legacy)12 months
Incident
CheckupCheckupCheckupCheckupCheckup
P-02 · PAIN POINT

“Plenty of data, no actionable analysis”

Devices generate huge volumes, but formats are inconsistent, signals are noisy and sync is unreliable. Real-time analysis isn't possible, so most of the data never becomes a decision.

Raw signal noiseUncleaned
Noisy raw data Cleaned trend
P-03 · PAIN POINT

“Data drops constantly under weak network”

Outdoor, mobile and industrial zones have poor coverage. Sync lags, packets drop, uploads fail — and the completeness and credibility of the monitoring program collapses with it.

Signal strength (24h)10% data loss
00:0012:0024:00
P-04 · PAIN POINT

“Storage costs keep climbing as data piles up”

Heart rate, sleep, breathing and motion produce continuous time-series at scale. Storage and compute cost spirals quickly and legacy architectures can't keep up.

Cumulative storage (TB)102 TB · 6 months
Jan
Feb
Mar
Apr
May
Jun
/ 03 HOW WE THINK

Not “a wristband plus a backend” — a
long-running health management system.

The hard part is turning health data into long-running, stable, analyzable management capability. So we combined wearables, IoT data capture, AI risk detection, big-data analytics and health records into a single closed loop.

Long-term trend detection
Turn scattered data into trend curves
Risk prediction
Detect fatigue, arrhythmia, abnormal breathing
Active intervention
Alerts auto-route to health response
Health service handoff
Build long-running records and follow-ups
/ ARCHITECTURE · 4 LAYERSSystem layer view
L4Application layer
Health platform · IOC display · Alert center · Records
Health platformIOC displayAlert centerHealth records
L3Algorithm layer
AI alert models · Time-series analysis · Anomaly detection
AI alert modelsTime-series analysisAnomaly detectionTrend modeling
L2Data layer
Health data pool · Cleaning layer · Trend analytics DB
Data platformCleaning engineTime-series DBArchive & tagging
L1Sensing layer
Smartwatches · Wristbands · IoT sensors · 5G devices
SmartwatchWristbandIoT sensor5G gateway
Downstream: business requests / Upstream: vitals dataClosed loop · long-running
/ 04 HOW IT WORKS

A long-running
AI health monitoring pipeline · 5 steps

From capture to weak-network sync to AI analysis to cleaning to visualization — each step is reusable engineering capability.
STEP 01 · PIPELINE

Wearable data capture

Keep health data continuously online

Smartwatches, wristbands and IoT sensors continuously capture heart rate, breathing, sleep quality, activity, stress indicators and overnight vitals — full 24/7 coverage.

Implementation
IoT wearablesBLE5G uplinkEdge compute nodes
Outcome
  • Long-running continuous capture
  • Lower manual recording cost
  • Build personal health records
wavesteam · Wearable data capture
Wearable data capture
/ SYSTEM SHOWCASE

The modules

From personal health records to risk alerts to the enterprise command center — every module runs in the client's live production environment.
IOC · OCCUPATIONAL HEALTH COMMAND CENTER
Live
IOC display
Health monitoring · Dashboard
Live heart rate, sleep, risk distribution and a workforce health heatmap
Health monitoring · Dashboard
AI risk alert center
Fatigue / arrhythmia / breathing anomaly / chronic-condition trend — multi-model
AI risk alert center
Wearables · device network
Smartwatch / wristband / IoT sensor / 5G gateway — full-link online management
Wearables · device network
Personal health records
Vitals trends, health assessment, intervention logs and checkup reports
Personal health records
/ 05 PROJECT RESULTS

After launch, the client built a
long-term occupational health system

From reactive response to proactive management; from fragmented data to a unified health platform. For the first time, frontline staff and managers can actually see the health trend.
/ KPI
AI risk alerts
0events
Actionable alerts to date · 78% accuracy
/ KPI
Data loss rate
0%
Weak-network sync · 10% down to 0.1%
/ KPI
Employees online
0people
Coverage across high-risk roles in Guangdong
Dimension
Before
After
Health monitoring
Reactive checkups
Live continuous monitoring
Data capture
Manual records
Automated IoT capture
Risk detection
Post-hoc discovery
AI early warning
Data management
Fragmented storage
Unified health platform
Health intervention
Reactive response
Long-term trend management
Live health monitoring
24/7 vitals capture and management.
AI risk alerts
Predictive capability for occupational and chronic disease.
Health record management
Long-running continuous health data assets.
Data platform
Unified analysis and visualization of health data.
Workplace safety
Lower probability of incidents in high-risk roles.
/ 06 WHERE IT FITS

The same architecture extends to more
health scenarios

The wearables-plus-AI-analytics capability isn't industry-specific — it's a reusable engineering solution.
REUSABLE
Public transit
Fatigue-driving monitoring
REUSABLE
Working at height
Real-time health risk alerts
REUSABLE
Manufacturing
Long-term occupational disease monitoring
REUSABLE
Elder health management
Daily vitals monitoring
REUSABLE
Healthcare and elder-care facilities
Chronic condition management
REUSABLE
Smart communities
Resident health platform
/ 07 FINAL SUMMARY

AI plus wearables is reshaping how health is managed —
“reactive medicine” → “proactive health management”.

Traditional health management is essentially handling problems after they appear. The real value of an intelligent health system is continuous monitoring, proactive risk detection, trend analysis and early intervention. This isn't just a digital upgrade — it's a shift in the health paradigm.

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Email:contact@boilingwater.cnOffice:10F, South Tower, Kingkey Yujing Times, Longgang District, Shenzhen