Estimate heart rate (HR) and breathing rate (BR) in bpm from a cleaned 1-D physiological time series such as radar phase, WiFi CSI amplitude, PPG, or a chest-motion signal. Use when Claude needs to choose HR/BR bandpass edges, pick a peak frequency from a noisy band, reject the HR second harmonic that often dominates the fundamental, handle respiration-harmonic leakage into the HR band on slow breathers, or flag low-confidence estimates. Not for arrhythmia / beat-to-beat analysis, multi-subject source separation, or rapidly non-stationary rates.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Given a cleaned 1-D periodic signal (radar phase, WiFi CSI, PPG, piezo), estimate heart rate and breathing rate in bpm.
Upstream ingestion of radar captures is the radar-signal-processing skill. This skill picks up after a 1-D signal is extracted.
butter(4, [0.08, 0.5], btype='band', fs=fs) — 4.8–30 bpmbutter(4, [0.7, 3.0], btype='band', fs=fs) — 42–180 bpmnperseg = min(len(x), int(fs * 25))
f, p = welch(x, fs=fs, nperseg=nperseg, noverlap=nperseg//2,
nfft=8*nperseg, detrend='constant')
in_band = (f >= lo) & (f <= hi)
peak_hz = f[in_band][np.argmax(p[in_band])]
f_sub = f_peak / 2.0
if 0.7 <= f_sub <= 3.0:
p_sub = np.interp(f_sub, f, p)
p_top = np.interp(f_peak, f, p)
if p_sub > 0.5 * p_top:
f_peak = f_sub # the peak was the 2nd harmonic
hr_bpm = f_peak * 60
ac = np.correlate(x - x.mean(), x - x.mean(), mode='full')
ac = ac[len(ac)//2:] / ac[len(ac)//2]
lag = int(fs/f_hi) + np.argmax(ac[int(fs/f_hi):int(fs/f_lo)])
bpm_ac = 60 * fs / lag
abs(bpm_ac - bpm_psd) > 5, flag as low confidence.| If | Then |
|---|---|
f_peak/2 in HR band and p_sub > 0.5 × p_top | Pick sub-harmonic (fundamental) |
| PSD and autocorrelation disagree by > 5 bpm | Flag low confidence; do not commit to one value |
| BR estimate < 10 bpm (slow breather) | Expect HR-band contamination — see references/harmonic-pitfalls.md |
| HR > 150 bpm (tachycardia) | Widen HR band upper to 3.3 Hz, re-estimate |
| Clip < 15 s long | PSD bin spacing > tolerance — prefer autocorrelation or flag inconclusive |
BR < HR. Always true for a live adult at rest — if violated you swapped bands.HR × duration_minutes ≈ peak count in find_peaks(bandpassed_hr). Off by 2× → harmonic error slipped through.See references/band-rationale.md. Short version: textbook bands miss slow breathers (supine clinical subjects breathe 5–10 bpm) and bradycardia (athletes, post-tilt-down).
See references/harmonic-pitfalls.md. These are the two failure modes that cost naïve pipelines the most accuracy on real data.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
The English summary is being prepared. Showing the original description.
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
The English summary is being prepared. Showing the original description.
Redact text from PDF documents for blind review anonymization
The English summary is being prepared. Showing the original description.
Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
The English summary is being prepared. Showing the original description.
Analyze failed GitHub Action jobs for a pull request.
The English summary is being prepared. Showing the original description.
Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
The English summary is being prepared. Showing the original description.