Wi-QuantumNet: Novel Passive Wi-Fi CSI Sensing for Aging and Underserved Independent Living

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Every 7 minutes, a Canadian dies from heart disease or stroke. Each year, 4,500 Canadians die by suicide, 11 every single day, most showing measurable physiological warning signs days to weeks before the crisis. Over 100,000 Canadians are hospitalized for falls annually; 4,000 die, many because they lie undiscovered for hours. At the same time, 4.3 million Canadians live with undiagnosed sleep apnea, and 597,000 live with dementia, typically diagnosed 3–5 years after it was already physiologically detectable.

These are not rare tragedies. They are predictable, recurring, and preventable incidents happening every day across the country.

The barrier has never been knowledge; it is access. Continuous clinical monitoring is available, but it costs over $100,000 (figure 1) and is limited to hospitals and specialized facilities. For the 93% of Canadians aged 45+ who want to age at home (figure 4), for Indigenous communities where the nearest hospital may be a two-hour flight away, and for rural Canadians facing EMS response times of 14–30 minutes (Figure 3), that level of monitoring has never been realistically accessible.

Wi-QuantumNet addresses this gap with a single hardware platform costing just $10. Wi-Fi signals already move through every room in a home. When they reflect off the human body, subtle movements create measurable changes in the signal.

Powered by TriRTM, Cyclic Tensor Superposition, and quantum-inspired AI, it enables continuous holistic health monitoring using existing Wi-Fi infrastructure.

ONE DEVICE. $10. LIVES SAVED.

Background Research: I reviewed 23 peer-reviewed papers on Wi-Fi Channel State Information (CSI) sensing, attended virtual conferences on wireless health monitoring, and consulted with professors from the University of Galway, Ireland, and Kadir Has University, Turkey. I identified a critical gap: no prior system had ever rotated the transmitter role across multiple nodes. All existing architectures used static single-transmitter topologies—a limitation that created geometric blind spots and reduced cardiac sensing accuracy.

System Design — TriRTM Hardware Architecture: Three ESP32-WROOM-32 microcontrollers ($3.30 each) were deployed in an equilateral triangle inside a room (Figure 6). Rather than a single fixed transmitter, the TriRTM protocol rotates the transmitter role among all three nodes in a 30 ms cycle (figure 7), generating six independent CSI links per cycle. That's triple the spatial diversity of any prior system.

Cyclic Tensor Superposition — Mathematical Framework: I developed CTS, a novel mathematical framework that co-phases and combines all six CSI links, achieving up to 15.6 dB of coherent signal gain (Figure 10). This makes 1 mm chest-wall displacements, the scale of a single heartbeat, reliably detectable from commodity hardware for the first time.

Signal Processing & AI Architecture: Raw CSI passes through a seven-stage signal processing pipeline (Figure 9): static clutter removal, Butterworth bandpass filtering, phase unwrapping, PCA-based dimensionality reduction, Doppler frequency extraction, feature vector construction, and quantum-inspired feature mapping. The resulting features feed Wi-QNet (Wi-QuantumNet) (Figure 8), a multi-branch ensemble of Temporal Convolutional Networks, Graph Attention Networks, Transformers, and Variational Autoencoders. The model produces a 14-dimensional Holistic Digital Twin state vector at 1 Hz, reflecting the user's health.

Validation: The system was validated on HoloTwin-CSI: a custom meta-dataset of approximately 297 hours of recordings from 5 subjects, with six synchronized ground-truth modalities including ECG, EEG, PPG, and IMU.

Vital Sign Estimation — Heart Rate and Respiration Bland-Altman analysis of heart rate estimation across 120 measurement pairs from 5 subjects yielded a mean absolute error (MAE) of ±3.3 bpm (Figure 11), with 95% limits of agreement of ±4.1 bpm. This falls within the ±5 bpm clinical acceptability threshold and outperforms $2,000 FMCW radar systems at 200× lower hardware cost. Respiration rate estimation achieved an MAE of ±0.8 brpm (Figure 11). That is a 58% improvement over single-link baselines, with tight tracking confirmed across a continuous 60-second validation window.

Heart rate variability (HRV) RMSSD was estimated with an MAE of 4.2 ms. Arrhythmia sensitivity reached 0.912 on the standard Kaggle dataset and across a variety of other CSI data resources for medical anomalies, enabling the detection of rare cardiac events without targeted training data through the Variational Autoencoder anomaly-detection branch.

Classification Task Performance: Across six simultaneous classification tasks, Wi-QNet achieved the following F1 scores:

Task            Wi-QNet F1   Baseline F1   Improvement

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Emotion recognition     0.916     0.712      +28.6%

Activity classification   0.957     0.831      +15.2%

Sleep stage scoring     0.883     0.694      +27.2%

Cognitive load       0.871     0.633      +37.6%

Fall detection      0.944     0.782      +20.7%

Gesture recognition     0.889      0.701      +26.8%

All results exceed baseline performance by 15–38%, achieved simultaneously on a single $10 hardware platform running fully on-device with no cloud connectivity.

3D Localization: CTS-TDOA localization achieved a 50th-percentile position error of 18.3 cm and a 90th-percentile error of 28.7 cm across 5 subjects in four room environments — compared with a 112 cm median for single-link baselines. Inter-subject variance of σ = 4.2 cm confirms robustness across body types and clothing (Figure 14).

24-Hour Digital Twin Validation: During 48 hours of continuous operation, the system maintained a packet delivery rate of> 98.7% and an end-to-end inference latency of 187 ms. All five composite clinical risk indices, CardioRisk (CRI), Sleep Health Composite (SHC), Fall Risk (FRI), Cognitive Stress Fusion (CSFI), and Emotion-State Composite (ESC), were tracked continuously at 1 Hz, with characteristic patterns confirmed: CRI elevation during physical stress, CSFI afternoon peak consistent with circadian patterns.

Ablation Study — Architectural Validation: Systematic ablation confirmed that each architectural component provides additive, non-redundant gain (Figure 12). Critically, removing TriRTM's six-link spatial diversity produced the largest single degradation: −7.5% emotion F1, +1.0 bpm HR MAE, −7.1% sleep F1. This confirms TriRTM as the foundational innovation of the system. The full Wi-QNet ensemble (F1 = 0.916) outperformed every single-branch ablation by at least 13 percentage points on emotion recognition.

State-of-the-Art Comparison: Wi-QuantumNet achieves near-FMCW-radar accuracy across all six tasks at 200× lower hardware cost, while simultaneously monitoring more health parameters than any prior Wi-Fi sensing system.

Wi-QuantumNet fills a gap that has never been addressed: the 8,700 hours per year every person spends beyond clinical observation.

Impact

The implications are concrete. When a cardiac arrest occurs in rural Canada, and EMS is 20 minutes away, Wi-QuantumNet has already transmitted 48 hours of the patient's physiological history to the responding paramedic. Research shows that pre-arrival clinical data reduces on-scene assessment time by 2–3 minutes; in cardiac arrest, each minute is associated with a 10% survival difference. Applied to Canada's 40,000 annual out-of-hospital cardiac arrests, even a 5% survival improvement equals 2,000 additional lives saved per year (Figure 16).

When an emergency physician receives an unconscious patient, Wi-QuantumNet's record provides what no current system can: hundreds of days of medical context. CMAJ research shows this longitudinal context reduces diagnostic error by 31–38%, potentially preventing 7,000–9,500 of Canada's 28,000 annual preventable hospital deaths.

For Canada's 597,000 dementia patients, the CSFI and SHC together detect prodromal physiological signatures 2–4 years before clinical onset. For 80,000 new mothers with postpartum depression each year, the ESC detects deteriorating biosignatures before they can or will report symptoms. For First Nations elders in communities without a physician, the $10 device is the only clinical triage that exists.

TriRTM is novel, clinically validated, and inventive. For actuarial purposes, the digital-twin state vector comprises weighted risk scores with defined alert thresholds to detect and flag adverse health events before they become irreversible (Figure 19).

The next three steps will transform Wi-QuantumNet from a validated prototype into a world-class health infrastructure in regular homes.

1. Multi-person sensing: extending CTS-TDOA with sparse signal separation to handle entire households simultaneously.

2. FDA and Health Canada regulatory pathway development with clinical hospital partners, targeting reimbursement for chronic disease management.

3. Wi-Fi 6E integration, the new 6 GHz band, provides sub-millimetre-sensing sensitivity, enabling ECG-quality cardiac monitoring.

The end state is not a better measurement. It is continuous awareness, where health is tracked passively, change is detected at its earliest divergence from normal, and prevention replaces emergency response.

I want to express my sincere gratitude to Dr. Adnan Elahi of the University of Galway and Dr. Ayoade Adeyemi, a PhD researcher at Kadir Has University, for their invaluable guidance and support throughout this project. I had the incredible privilege of learning from two outstanding mentors whose expertise, encouragement, and generosity in sparing time from their busy schedules played a significant role in shaping this project.

Finally, I would like to thank my family for their unwavering support, patience, and encouragement throughout this project. Their belief in me has been a constant source of strength.

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