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Gemini Executive Synthesis

shii • haa, a breathing app providing live biofeedback from a phone microphone.

Technical Positioning
Promotes self-awareness of breathing rhythm, depth, and regularity, explicitly avoiding gamification. Developed by a family doctor.
SaaS Insight & Market Implications
This health-tech application targets personal wellness and stress management. The on-device processing of sensitive biofeedback data (breathing) is a critical security and privacy differentiator, appealing to users and enterprises wary of data upload. The explicit rejection of gamification positions it as a serious self-awareness tool, potentially appealing to clinical or corporate wellness programs seeking non-addictive solutions. The technical stack, involving signal processing and ML on-device, indicates a robust approach to real-time data analysis, addressing a common challenge in mobile health applications. This product demonstrates a focused approach to health data privacy and user-centric design.
Proprietary Technical Taxonomy
signal processing breathing state machine ML on-device processing Android/iOS audio issues

Raw Developer Origin & Technical Request

Source Icon Hacker News Jun 3, 2026
Show HN: Live breath detection and biofeedback from a phone microphone

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.

Developer Debate & Comments

jiangriver66 • Jun 3, 2026
[flagged]
stanleydupreez • Jun 3, 2026
[flagged]
ncr100 • Jun 2, 2026
OT did GitHub change their default fonts? This MD file shows up "differently" than I am used to, today.
maartenh • Jun 2, 2026
Sounds interesting. Unfortunately not available in my country in the Android app store. I live in The Netherlands. If you're looking for feedback, you might want to double check this :)
naeem189 • Jun 2, 2026
[dead]
muhammadusman • Jun 2, 2026
can you please review login requirement?

Frequently Asked Questions

Market intelligence mapped to shii • haa, a breathing app providing live biofeedback from a phone microphone..

What is the technical positioning of shii • haa, a breathing app providing live biofeedback from a phone microphone.?
Based on our AI analysis of the original developer request, its primary technical positioning is: Promotes self-awareness of breathing rhythm, depth, and regularity, explicitly avoiding gamification. Developed by a family doctor.
How is the developer community reacting to shii • haa, a breathing app providing live biofeedback from a phone microphone.?
Yes, we have tracked 10 direct responses and active debates regarding this specific topic originating from Hacker News.
What are the foundational technologies related to shii • haa, a breathing app providing live biofeedback from a phone microphone.?
Our proprietary extraction maps shii • haa, a breathing app providing live biofeedback from a phone microphone. to adjacent architectural concepts including signal processing, breathing state machine, ML, on-device processing.

Engagement Signals

27
Upvotes
10
Comments

Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like ML and signal processing by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.