Client Health Metric Alert
AI → HumanWearable data analyzed by AI to detect abnormal patterns and alert fitness trainers.
5 nodes · 4 edgesfitness
eventagenthumanapi
Visual
Wearable Data Streamevent
Heart rate, HRV, sleep quality, and recovery scores from wearable devices.
↓sequential→ AI Health Metric Analysis
AI Health Metric Analysisagent
Detect overtraining, poor recovery, or abnormal cardiac patterns.
↓conditional→ Abnormal Pattern Flag
Abnormal Pattern Flagsystem
Categorize: overtraining risk, recovery deficit, or medical referral needed.
↓sequential→ Trainer Alert
Trainer Alertapi
Mobile notification with client health summary and flagged metrics.
↓sequential→ Modified Workout Recommendation
Modified Workout Recommendationhuman
Trainer adjusts program intensity, adds rest days, or recommends medical check.
uc-client-health-alert.osop.yaml
osop_version: "1.0"
id: "client-health-alert"
name: "Client Health Metric Alert"
description: "Wearable data analyzed by AI to detect abnormal patterns and alert fitness trainers."
nodes:
- id: "wearable_stream"
type: "event"
name: "Wearable Data Stream"
description: "Heart rate, HRV, sleep quality, and recovery scores from wearable devices."
- id: "health_analysis"
type: "agent"
subtype: "llm"
name: "AI Health Metric Analysis"
description: "Detect overtraining, poor recovery, or abnormal cardiac patterns."
security:
risk_level: "medium"
- id: "abnormal_flag"
type: "system"
name: "Abnormal Pattern Flag"
description: "Categorize: overtraining risk, recovery deficit, or medical referral needed."
- id: "trainer_alert"
type: "api"
name: "Trainer Alert"
description: "Mobile notification with client health summary and flagged metrics."
- id: "workout_modification"
type: "human"
subtype: "review"
name: "Modified Workout Recommendation"
description: "Trainer adjusts program intensity, adds rest days, or recommends medical check."
security:
approval_gate: true
edges:
- from: "wearable_stream"
to: "health_analysis"
mode: "sequential"
- from: "health_analysis"
to: "abnormal_flag"
mode: "conditional"
when: "anomaly.detected == true"
- from: "abnormal_flag"
to: "trainer_alert"
mode: "sequential"
- from: "trainer_alert"
to: "workout_modification"
mode: "sequential"