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Live breath detection and biofeedback from a phone microphone

Details

External ID
48372036
Source
HN
Company
—
Product
Live breath detection and biofeedback from a phone microphone
Website domain
github.com
Launched
June 2, 2026
Cohort
—
Upvotes
67
Upvotes percentile
0.8763661202185792
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

Hi everyone, I am Felix, a famliy doctor from ZH, Switzerland. A couple of month ago I started this little project called shii • haa, a breathing app that uses the phone`s microphone for live biofeedbackMy prior work in emergency medicine and intensive care was closesly linked to breathing, mostly in critical situations... and let me to reevaluate my own way of breathing. over time one question popped into my mind: can medical knowledge and biofeedback make an app actually promote self-awareness instead of attaching your goals to the award system of the app.it combines signal processing, a breathing state machine and ML. The state machine follows inhale, exhale and transitions in the mic signal. A quality layer rejects noisy or ambiguous windows before signals are used for feedback. All processing is done on-device, no speech or raw audio is uploaded.What I'm trying to avoid is turning breathing into another score or game. The app gives feedback on rhythm, depth and regularity, but the point is more "notice what you are doing" than "perform well".I'd be interested in feedback, especially from people who have worked on signal processing, health UX, or Android/iOS audio issues.

Enrichment

Theme
audio utilities and sound management
Vertical
Healthcare
Function
Hardware & robotics
Audience
B2C
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
breath detection and biofeedback from phone microphone
Manually corrected
False

Could you build this?

No Real-time acoustic breath detection on smartphone microphones requires specialized digital signal processing and machine learning models trained on audio datasets to filter ambient noise and detect respiratory phases.

What it would actually take: Building this requires a native iOS/Android or Web Audio pipeline performing real-time DSP (bandpass filtering, spectral flux, energy thresholding) combined with an on-device lightweight neural network (e.g., TFLite or CoreML) trained on clinical/respiratory audio datasets to distinguish inhalation, exhalation, and ambient noise. It demands deep domain expertise in audio engineering, acoustics, and biomedical signal processing to handle varying microphone characteristics and background noise.

Discussion

20 comments analyzed.

Competitors mentioned: Lungy breathing exercises app

Concerns raised: Signal too weak detection too strict on iPhone/Safari, Microphone normalization difficult across devices and room noise, iOS app blocked in Germany and EU countries due to DSA compliance, Microphone signal ambiguity hard to distinguish from noise

Feature requests: Reduce threshold strictness for silent breathing detection, Support for alternative sensors beyond microphone

Competitors

Other products that read as similar to this one — 123 launches clear the similarity bar, closest 8 shown.

Attention rank: #23 of 124 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 216 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a hardware & robotics tool for Horizontal yet.