Acoustic Breath Biomarker Tracker
SkillMediaLets your agent analyze call audio timing like pauses between speech and return simple ratio-based labels.
Use Acoustic Breath Biomarker Tracker in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the Acoustic Breath Biomarker Tracker skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
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Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
About this skill
Offline nonclinical phone-workflow demonstration over supplied speech/pause durations. Returns illustrative pause-ratio labels, not medical findings or an actual emergency handoff.
What this skill tells your AI
The instructions your AI receives, as published by calle-ai/awesome-phone-call-agents in skills/call-acoustic-breath-biomarker-tracker/SKILL.md and read by Ahel’s review.
This experimental helper calculates a pause ratio from synthetic, pre-segmented durations. It does not capture audio, run VAD, detect a medical condition, assess patient safety, or execute a handoff. Its DYSPNEA_DETECTED, NORMAL, and action enums are illustrative legacy labels, not clinical conclusions. Do not use this prototype for patient triage or emergency decisions.
Scientific Foundation
| Paper / Framework | Source | Relevance |
|---|---|---|
| Detection of Mild Dyspnea from Pairs of Speech Recordings | IEEE ICASSP (2020) | Provides the acoustic feature extraction models for identifying respiratory variations and abnormal pause mechanics. |
| Biomarkers in respiratory diseases | Breathe editorial (2019) | General background, not validation of pause-ratio clinical inference. |
| COVID-19-related voice disorders: a scoping review | PubMed (2026) | Background on voice disorders, not validation of this helper. |
| Software as a Medical Device (SaMD) | FDA (2023) | Regulatory framework for AI algorithms evaluating biological states. |
How it works
- Supply synthetic
AudioSegmentdurations to the helper. - Audio capture and VAD are not included; any future host would provide its own inputs.
- The
process_call_streamfunction evaluates the extracted segments. - A pause ratio at or above the illustrative threshold selects the legacy
DYSPNEA_DETECTEDenum; it does not establish dyspnea. ESCALATE_TO_HUMANis returned as a demo label only. No workflow is halted and no nurse transfer or emergency handoff occurs.
Decision Matrix
| Pause Ratio | Classification | Recommended Action |
|---|---|---|
>= 0.40 | DYSPNEA_DETECTED | ESCALATE_TO_HUMAN |
< 0.40 | NORMAL | PROCEED_NORMALLY |
Ratio < 0 (no data) | INSUFFICIENT_DATA | INDETERMINATE |
Configuration Reference
| Parameter | Default | Range | Description |
|---|---|---|---|
pause_threshold_ratio | 0.40 | 0.30 - 0.60 | Ratio of pause time over total time to trigger dyspnea flag. |
segment_duration_ms | 500 | 250 - 2000 | Window size for acoustic feature extraction. |
Expected Outcomes & Metrics
The figures below are unvalidated design aspirations, not clinical sensitivity, specificity, or handoff guarantees.
| Metric | Target | Notes |
|---|---|---|
| Escalation Latency | < 1 second | Critical for emergency health response. |
| False Positive Rate (FPR) | < 5% | Legitimate pauses shouldn't trigger an emergency. |
| False Negative Rate (FNR) | < 2% | Must not miss severe respiratory distress. |
Limitations & Known Constraints
- Codec Degradation: Low-bitrate connections may obscure acoustic pauses or falsely introduce silence gaps (packet loss).
- Background Noise: Heavy environmental noise might be misclassified as speech by VAD, lowering the calculated pause ratio.
- Not a Clinical Tool: This is not a diagnostic or patient-triage mechanism. Low scores do not establish that a person is safe.
Possible Future Research Contexts
These contexts require separate clinical evaluation and human-governed systems; they are not supported patient-care uses of this prototype.
- Post-discharge monitoring for COPD or heart failure patients.
- Daily check-in phone calls for patients with severe asthma.
- Triage in automated telehealth intake systems.
Integration
No dialogue-model or telephony integration is included. For a future host, this numeric demonstration must not delay human review or override an explicit report of distress.
Signals
- GitHub stars
- 104
- Forks
- 528
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
call-acoustic-breath-biomarker-tracker- Source
- github.com/calle-ai/awesome-phone-call-agents
github.com/calle-ai/awesome-phone-call-agents