System and Method for Crowd-Sourced Ground-Level Wind Speed and Direction Estimation Using Wearable Inertial Measurement Unit Gait Perturbation Analysis with Biomechanical Transfer Function Inversion

Abstract

Disclosed is a system and method for estimating ground-level wind speed and direction at hyperlocal spatial resolution (10 to 50 meters) by analyzing biomechanical gait perturbations measured by inertial measurement units (IMUs) embedded in consumer wearable devices (smartwatches, fitness bands, smart glasses, smartphones carried or pocketed). Wind exerts aerodynamic force on the human body during locomotion, producing measurable perturbations in medio-lateral trunk sway, step width variability, stride-to-stride cadence irregularity, and asymmetric ground reaction force patterns. These perturbations are captured by the 6-axis or 9-axis IMU (tri-axial accelerometer, tri-axial gyroscope, optional tri-axial magnetometer) present in every modern consumer wearable. The system maintains a per-user biomechanical baseline gait model learned during calm-wind conditions (below 2 m/s), then applies a physics-informed neural network that inverts the biomechanical transfer function from observed gait deviations to wind force vector estimates. Wind direction relative to the walking heading is resolved by decomposing gait perturbations into headwind/tailwind (anterior-posterior stride length modulation) and crosswind (medio-lateral sway asymmetry) components, with absolute direction computed by fusing the walking heading from the device's magnetometer and GPS. Individual wind estimates are noisy (expected per-observation RMSE of 2 to 4 m/s in speed, 30 to 60° in direction), but a spatial-temporal Gaussian process fusion layer aggregates observations from thousands of pedestrians across a city into a continuous wind field map with sub-100-meter resolution and sub-1 m/s ensemble uncertainty, updated every 5 to 15 minutes. The system incorporates a 3D building geometry prior from OpenStreetMap and municipal LiDAR datasets to model urban canyon wind acceleration, Venturi channeling, and corner vortex effects that make ground-level winds highly heterogeneous and poorly predicted by rooftop anemometers or mesoscale weather models. Applications include pedestrian safety alerts in urban wind corridors, drone delivery path optimization, building energy model calibration, emergency chemical plume dispersion modeling, outdoor event planning, and insurance risk assessment for wind-sensitive structures.

Field of the Invention

This invention relates to environmental sensing using consumer wearable devices, specifically to methods for estimating ground-level wind conditions by analyzing perturbations in human gait biomechanics measured by inertial measurement units and aggregating individual observations into crowd-sourced wind field maps via spatial-temporal statistical fusion.

Background

Ground-level wind measurement is among the poorest-resolved variables in modern meteorology. Standard weather stations (ASOS, AWOS) are sited at airports per WMO guidelines: open terrain, 10-meter mast height, minimum 300-meter clearance from obstructions (WMO-No. 8, Guide to Instruments and Methods of Observation, 2018). These conditions bear almost no resemblance to the pedestrian environment in cities, where buildings channel, accelerate, deflect, and create turbulent recirculation zones that can amplify or attenuate the free-stream wind speed by factors of 0.2 to 3.0 over distances of 20 to 50 meters (Blocken et al., 2016). The result is that the nearest official wind observation may be 5 to 15 km away and at an elevation and exposure completely unrepresentative of pedestrian-level conditions.

The consequences of this measurement gap are not trivial:

• Pedestrian safety: The Lawson comfort criteria, adopted in building codes in the Netherlands (NEN 8100), the UK, and Australia, classify wind speeds above 15 m/s as dangerous for pedestrians and above 20 m/s as lethal (Lawson, 1978). Yet cities routinely approve tall buildings with no post-construction wind monitoring. London's "Walkie Talkie" (20 Fenchurch Street) created ground-level wind gusts strong enough to knock pedestrians over, discovered only after occupancy. In 2019, a woman in Leeds was killed when wind gusts in a narrow passage between buildings toppled a truck onto her. No wind sensor existed within 800 meters of the site.
• Drone delivery: Amazon Prime Air, Wing (Alphabet), and Zipline operate delivery drones that are highly sensitive to ground-level wind shear. The FAA's Part 135 waiver conditions require real-time wind awareness, but operators rely on mesoscale model forecasts (HRRR, 3-km resolution) that cannot resolve building-scale wind effects. Yeo et al. (2021) showed that urban canyon wind gusts exceed mesoscale forecasts by 40 to 120% at street level, causing unexpected drone trajectory deviations.
• Building energy: Wind-driven infiltration accounts for 25 to 50% of heating energy loss in commercial buildings (Jokisalo et al., 2020). Building energy models (EnergyPlus, eQUEST) use airport wind data with crude terrain correction factors that introduce 30 to 60% error in urban environments.
• Emergency response: Chemical, biological, and radiological plume dispersion models (ALOHA, HPAC) require ground-level wind input. During the 2005 Buncefield explosion, emergency responders used Heathrow airport wind data (8 km away) to model the vapor cloud, producing dispersion estimates that differed from actual ground-truth by factors of 2 to 5 in crosswind extent.

Current approaches to dense urban wind measurement are prohibitively expensive or impractical:

• Fixed anemometer networks: Ultrasonic anemometers (Gill WindSonic, $2,000 to $5,000 per unit) require mounting at pedestrian level (1.5 to 2 m), power, data connectivity, and weather-proof housings. At the density required to resolve urban canyon effects (one sensor per 50 to 100 meters along each street canyon), a mid-size city would need 10,000 to 50,000 sensors at a total installed cost of $100M to $500M, which no municipality can justify.
• LiDAR wind profilers: Doppler LiDAR (Leosphere WindCube, $150,000 to $300,000) provides vertical wind profiles above rooftop level, but cannot measure at pedestrian height due to minimum range limitations (typically 40 to 100 meters).
• CFD simulation: Computational fluid dynamics can model pedestrian-level wind fields (Tominaga et al., 2008), but requires detailed 3D building geometry, boundary condition specification, and substantial computational resources. A single steady-state RANS simulation of a 500-meter urban domain takes 4 to 24 hours on a 64-core cluster. Transient LES simulations that capture gust statistics require 10 to 100× more compute. CFD is used for building permit compliance, not real-time monitoring.
• Crowd-sourced barometric pressure: Smartphone barometers have been used to improve surface pressure analysis for weather forecasting (Mass and Madaus, 2014). However, barometric pressure provides no directional wind information and its spatial gradient (which relates to wind via the geostrophic approximation) varies on scales of 10 to 100 km, far too coarse for urban canyon wind estimation.

Meanwhile, the global installed base of consumer wearables with IMUs exceeded 1.5 billion devices in 2025 (smartwatches, fitness trackers, smartphones), with approximately 500 million regularly worn during outdoor pedestrian activity (IDC Worldwide Wearables Forecast, 2025). In a dense urban area like Manhattan (population density ~27,000/km²), there are an estimated 3,000 to 10,000 actively-worn IMU devices per square kilometer at any given time during business hours. Each pedestrian wearing such a device is, in effect, an unwitting anemometer whose gait responds to the wind environment. The question is whether the signal can be extracted.

The biomechanical basis for wind-induced gait perturbation is well-established. Hejazi et al. (2019) measured gait parameters using inertial sensors during controlled wind tunnel exposure at 0 to 15 m/s and found statistically significant increases in step width variability (Cohen's d = 0.8 at 10 m/s crosswind), medio-lateral trunk sway amplitude (d = 1.2 at 10 m/s), and stride time variability (d = 0.6 at 10 m/s), with headwind producing measurable stride length reduction (3 to 8% at 10 m/s) and tailwind producing stride length increase (2 to 5% at 10 m/s). Maki (1997) demonstrated that lateral force perturbations as small as 10 N (equivalent to approximately 3 m/s crosswind on a standing human with 0.7 m² frontal area) produce detectable compensatory stepping responses. The signal exists; no system extracts it.

This disclosure describes a system that closes the gap between ubiquitous wearable IMU data and the unmet need for hyperlocal ground-level wind measurement.

Detailed Description

1. Aerodynamic Force Model: Wind-on-Pedestrian

The aerodynamic force exerted on a walking human by wind is modeled as:

F = ½ ρ C_d A v_rel² · û_rel

where ρ is air density (approximately 1.225 kg/m³ at sea level, corrected for altitude and temperature using the device's barometric pressure sensor), C_d is the drag coefficient of the human body (0.9 to 1.3 depending on clothing, posture, and wind angle; Penwarden, 1973), A is the projected frontal or lateral area of the body (0.5 to 0.85 m² frontal, 0.3 to 0.5 m² lateral, estimated from user height and weight stored in the device's health profile), v_rel is the wind velocity relative to the walking pedestrian (vector sum of ambient wind and negative walking velocity), and û_rel is the unit vector of relative wind direction.

For a typical adult (height 1.75 m, mass 75 kg, frontal area 0.65 m²), the aerodynamic forces at representative wind speeds are:

• 5 m/s (light breeze): ~10 N total force. This is 1.3% of body weight. Barely perceptible in steady gait but detectable as a statistical shift in medio-lateral sway over 20 or more strides.
• 10 m/s (fresh breeze): ~40 N. This is 5.4% of body weight. Clearly noticeable to the walker. Produces a measurable lateral lean of 2 to 4° and step width increase of 15 to 25% above baseline.
• 15 m/s (near gale): ~90 N. This is 12% of body weight. Difficult to walk against. Stride length reduction of 15 to 30% into headwind. Frequent stumble corrections visible in accelerometer data.
• 20 m/s (gale): ~160 N. This is 21% of body weight. The Lawson danger threshold. Many pedestrians cannot maintain a straight trajectory.

The key insight is that even at low wind speeds (5 to 8 m/s), where the force is below conscious perception, the biomechanical control system produces compensatory responses that are detectable by a wrist-worn or trunk-mounted IMU with sufficient statistical aggregation. The system does not need to measure wind from a single stride; it estimates wind from the statistical distribution of gait parameters over a walking bout of 30 to 120 seconds (approximately 50 to 200 strides).

2. Biomechanical Transfer Function: Wind Force to IMU Observables

The system models the human body during walking as a multi-segment inverted pendulum with active balance control. The transfer function from external wind force to observable IMU signals operates through four biomechanical pathways:

2.1 Medio-lateral trunk sway (crosswind channel). Crosswind force creates a lateral moment about the center of mass (CoM) that the vestibulospinal and proprioceptive control systems counter by adjusting foot placement, hip abductor torque, and trunk lean. A wrist-worn accelerometer captures the resulting trunk sway as a modulated signal in the medio-lateral (ML) acceleration channel. During baseline (calm) walking, ML acceleration exhibits a quasi-periodic oscillation at step frequency (approximately 1.8 to 2.2 Hz) with standard deviation (SD) of 0.8 to 1.5 m/s² (Menz et al., 2003). Crosswind at 10 m/s increases ML acceleration SD by 20 to 40% and introduces a DC offset (sustained lean) of 0.2 to 0.6 m/s². Critically, the asymmetry of ML sway encodes crosswind direction: wind from the left increases rightward compensatory sway peaks relative to leftward peaks. The system extracts this asymmetry via the skewness of the ML acceleration distribution over each walking bout.

2.2 Anterior-posterior stride modulation (headwind/tailwind channel). Headwind opposes forward motion, increasing the metabolic cost of walking. Pedestrians unconsciously compensate by reducing stride length by 0.3 to 0.8% per 1 m/s of headwind speed (below the conscious perception threshold) and increasing cadence slightly to maintain velocity, or by reducing walking speed entirely. Tailwind produces the inverse: increased stride length and reduced cadence. The AP acceleration channel captures this as a change in the fundamental frequency and harmonic structure of the gait cycle. The system computes stride length from double-integration of AP acceleration with zero-velocity update (ZUPT) corrections during stance phase, and detects systematic stride length shifts relative to the user's calm-wind baseline.

2.3 Step width variability (crosswind turbulence channel). In turbulent crosswind conditions (common in urban canyons), the lateral force varies rapidly, preventing the balance control system from settling into a steady compensatory lean. This produces increased step width variability (the standard deviation of step-to-step lateral foot placement distance), which the system detects via the variability of the ML displacement between successive gait cycles. Bauby and Kuo (2000) demonstrated that step width is actively controlled on a step-by-step basis to regulate lateral CoM position, making it sensitive to lateral perturbation statistics. Increased step width variability distinguishes turbulent wind from steady wind at the same mean speed.

2.4 Vertical acceleration irregularity (gust response channel). Wind gusts produce transient destabilizing forces that elicit rapid balance recovery responses, including abrupt changes in vertical acceleration during the stance-to-swing transition. The system detects gust events as outliers in the vertical acceleration waveform's stride-to-stride consistency, quantified by the sample entropy (SampEn) of the vertical acceleration signal. Elevated SampEn relative to the user's baseline indicates a perturbed gait environment, with the magnitude and frequency of perturbation events encoding gust intensity and frequency.

3. Per-User Baseline Gait Model

The accuracy of wind estimation depends critically on separating wind-induced gait perturbations from the user's intrinsic gait variability, which is affected by age, fitness, footwear, walking surface, fatigue, cognitive load, and pathology. The system constructs a personalized baseline gait model during calm-wind periods:

3.1 Automatic calm-wind identification. Calm-wind walking bouts are identified by cross-referencing two independent signals: (a) the device's barometric pressure sensor, which exhibits low high-frequency variability during calm conditions (pressure fluctuation SD below 0.5 Pa over 60 seconds, corresponding to wind speeds below approximately 2 m/s; Mass and Madaus, 2014); and (b) nearby weather station reports (METAR) indicating sustained wind below 3 m/s. When both conditions are met, the walking bout is tagged as a calm-wind calibration sample.

3.2 Baseline feature extraction. For each calm-wind bout, the system computes 24 gait features from the IMU data:

• ML acceleration: mean, SD, skewness, kurtosis, spectral power at step frequency and harmonics
• AP acceleration: stride length (ZUPT-corrected double integration), cadence, stride length CV, stride time CV
• Vertical acceleration: amplitude at step frequency, SampEn, harmonic ratio
• Step width: mean, SD, coefficient of variation
• Walking speed: mean (from GPS or pedometric integration), coefficient of variation
• Trunk lean angle: mean and SD from gyroscope integration with magnetometer-aided drift correction

3.3 Context-dependent baseline. Gait features vary systematically with walking speed, slope, surface type, and footwear. The baseline model is a Gaussian process regression that predicts each gait feature as a function of these context variables. Walking speed is measured by GPS. Slope is estimated from barometric altitude rate of change. Surface type (concrete, asphalt, grass, gravel) is classified from the high-frequency (50 to 200 Hz) vertical acceleration texture using a random forest classifier trained on labeled data. Footwear is inferred from the impact transient signature at heel strike; the system maintains a library of the user's shoe signatures, each associated with a baseline gait profile. After 7 to 14 days of typical walking, the Gaussian process has sufficient data (200 to 500 calm-wind bouts) to predict baseline gait features with residual SD 20 to 30% below the raw population variance, enabling detection of smaller wind-induced perturbations.

4. Wind Vector Estimation: Physics-Informed Neural Network

The core estimation engine inverts the biomechanical transfer function to estimate wind speed and direction from observed gait deviations relative to baseline. The system uses a physics-informed neural network (PINN) whose architecture encodes the known aerodynamic and biomechanical constraints:

4.1 Input features. For each walking bout (minimum 30 seconds, approximately 50 strides), the system computes the 24 gait features and their deviations from the context-adjusted baseline (the Gaussian process prediction for the current walking speed, slope, surface, and footwear). These 24 deviation features, plus the walking heading (from magnetometer/GPS), walking speed, air density (from barometric pressure and temperature), and estimated user body parameters (height, weight, clothing insulation estimate from season and temperature) form the input vector.

4.2 Network architecture. The PINN consists of two parallel pathways:

• Crosswind pathway: Takes ML deviation features (sway SD ratio, sway skewness, step width variability ratio) and estimates the crosswind component (perpendicular to walking direction). Architecture: 3-layer MLP with 64 hidden units, batch normalization, and ReLU activation, outputting crosswind speed and a heteroscedastic uncertainty estimate (predicted log-variance). A physics constraint layer enforces that the estimated crosswind force (½ ρ C_d A_lateral v_cross²) is consistent with the observed ML sway asymmetry magnitude via a penalty term in the loss function.
• Headwind/tailwind pathway: Takes AP deviation features (stride length ratio, cadence ratio, walking speed deviation) and estimates the along-wind component (parallel to walking direction). Same architecture. Physics constraint: estimated headwind drag force consistent with observed stride length reduction magnitude.

The two pathway outputs are combined by vector addition (rotated to absolute coordinates using the walking heading) to produce the 2D ground-level wind vector estimate. A final calibration layer applies a learned bias correction for known systematic errors (e.g., the headwind pathway systematically underestimates low wind speeds because stride length modulation has a perception threshold).

4.3 Training data. The PINN is trained on a hybrid dataset:

• Controlled wind tunnel studies: Published and newly-collected data from instrumented treadmill walking in environmental wind tunnels at 0, 3, 5, 8, 10, 13, and 15 m/s from 8 directions (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315° relative to walking direction). 50 subjects spanning ages 20 to 75, BMI 18 to 35, with varied footwear and clothing. IMU data from wrist, hip, and ankle locations.
• Co-located field data: IMU data from 500+ pedestrians walking past research-grade ultrasonic anemometers installed at 2 m height in diverse urban environments (open plazas, narrow street canyons, building corners, waterfront promenades). Each anemometer provides ground-truth 3D wind at 10 Hz; the system timestamps and spatially matches each pedestrian's walking bout to the concurrent anemometer reading.
• Synthetic data augmentation: Simulated gait perturbation data generated by a musculoskeletal simulation model (OpenSim; Delp et al., 2007) with external wind force applied to the trunk segment, generating synthetic IMU waveforms for wind conditions and body types not covered by empirical data.

4.4 Per-observation accuracy. Expected performance for a single 60-second walking bout from a wrist-worn IMU: wind speed RMSE of 2.5 to 4.0 m/s for true wind speeds of 3 to 15 m/s, wind direction RMSE of 30 to 60° for crosswind-dominant conditions and 60 to 120° for along-wind-dominant conditions (direction is harder to resolve when wind is aligned with walking). Below 3 m/s, individual observations are unreliable (signal-to-noise ratio below 1.0). This per-observation accuracy is deliberately modest. The system's value proposition is not individual accuracy but crowd-sourced spatial density.

5. Spatial-Temporal Gaussian Process Fusion

Individual wind estimates are fused into a continuous spatial-temporal wind field using a hierarchical Gaussian process (GP) model:

5.1 Observation model. Each pedestrian's walking bout produces a wind estimate ŵ_i = (v̂_i, θ̂_i) at location (x_i, y_i) and time t_i, with estimated uncertainty (σ_v,i, σ_θ,i) from the PINN's heteroscedastic output. The observation likelihood is:

ŵ_i ~ N(w(x_i, y_i, t_i), diag(σ_v,i², σ_θ,i²))

where w(x, y, t) is the true wind field being estimated.

5.2 Prior wind field model. The GP prior on w(x, y, t) uses a composite kernel that encodes physical knowledge about urban wind behavior:

• Large-scale spatial component: Matérn 5/2 kernel with length scale 500 to 2,000 m, capturing the mesoscale wind gradient across the urban area. This component is anchored by official weather station observations and mesoscale model output (HRRR, 3-km grid).
• Urban canyon component: A learned kernel conditioned on 3D building geometry. The system pre-computes a building morphology feature vector for each 25-meter grid cell from OpenStreetMap and municipal LiDAR data: building height, street width, aspect ratio (H/W), sky view factor, distance to nearest building corner, and local aerodynamic roughness length (z₀). The urban kernel's length scale varies spatially, being short (25 to 50 m) in narrow canyons with rapid wind gradients and long (200 to 500 m) in open areas. This captures the critical urban canyon Venturi effect (wind acceleration in narrow passages between tall buildings), corner acceleration (wind speed increase of 50 to 200% at building corners; Blocken et al., 2016), and recirculation zones (wind speed attenuation to near-zero in the lee of wide buildings).
• Temporal component: Matérn 3/2 kernel with characteristic time scale of 15 to 60 minutes, reflecting the autocorrelation structure of urban wind. This allows the GP to interpolate between observations in time without assuming stationarity over hours.

5.3 Inference. Full GP inference over the observation set (potentially 10,000 to 100,000 observations per city per hour) is computationally prohibitive. The system uses sparse variational GP inference with 500 to 2,000 inducing points placed at a regular grid plus additional points at known wind-critical locations (building corners, canyon throats, open plazas). Inference runs on a cloud backend with 5 to 15 minute update cycles, producing a posterior mean wind field and uncertainty map. Areas with dense pedestrian traffic achieve posterior wind speed uncertainty below 1.0 m/s; areas with no recent pedestrian observations revert to the mesoscale prior (uncertainty of 3 to 5 m/s).

5.4 Observation quality filtering. Not all walking bouts produce usable wind estimates. The system applies quality filters:

• Minimum bout duration: 30 seconds (approximately 50 strides) required for stable feature estimation.
• Non-linear walking exclusion: Bouts where the walking heading changes by more than 30° are excluded, as turning gait produces ML accelerations that confound the crosswind signal.
• Indoor/shelter detection: GPS accuracy degradation (HDOP above 5) or barometric altitude inconsistent with ground level indicates indoor or covered walking, which is excluded from the outdoor wind field.
• Running/cycling exclusion: Activity classification (from the existing health/fitness API on the device) filters non-walking locomotion modes whose biomechanics differ from the trained gait model.
• Anomalous gait exclusion: Bouts where gait deviations exceed 5 standard deviations from baseline (indicating pathology, injury, intoxication, or sensor malfunction rather than wind) are flagged and excluded.
• Crowd density weighting: In locations with many concurrent observations, individual estimates are down-weighted to prevent over-counting. The GP's heteroscedastic observation model handles this naturally, but an additional de-correlation step removes duplicate information from pedestrians walking within 5 meters of each other (shared wind microenvironment).

6. Privacy Architecture

The system processes sensitive location and biomechanical data. Privacy protection is implemented at three levels:

• On-device feature extraction: Raw IMU waveforms never leave the device. The 24-feature gait deviation vector, walking heading, GPS location (quantized to 25-meter grid), and timestamp are the only data transmitted. The feature vector cannot reconstruct the user's identity, gait pattern (which is a potential biometric), or specific trajectory beyond the 25-meter spatial resolution.
• Differential privacy: Each uploaded observation includes calibrated Laplace noise added to the GPS coordinates (noise scale 15 meters), wind estimate (noise scale 0.5 m/s), and timestamp (noise scale 30 seconds). The noise parameters are calibrated to achieve (ε, δ)-differential privacy with ε = 1.0 and δ = 10⁻⁶ per observation, ensuring that no individual's contribution can be identified or removed from the aggregate wind field.
• Aggregation threshold: Wind field estimates are published only for grid cells that have received at least 5 independent observations within the current update window. This prevents identification of a single pedestrian's location from the wind field output.

7. Urban Canyon Wind Model Integration

The GP fusion layer incorporates a physics-based urban canyon wind model as an informative prior, substantially improving estimation accuracy in areas between pedestrian observations:

7.1 Pre-computed wind response database. For each 25-meter grid cell in the coverage area, the system pre-computes a wind response function using the OpenFOAM CFD solver (Tominaga et al., 2008) on a simplified building geometry mesh derived from OpenStreetMap 3D building data. The response function maps the mesoscale free-stream wind vector (speed and direction above rooftop level) to the predicted pedestrian-level wind vector at each grid cell. This involves running 36 steady-state RANS simulations (one per 10° wind direction sector) at a reference wind speed, then scaling by the assumed logarithmic wind profile. The result is a 36-entry lookup table for each grid cell, computed once and updated only when building geometry changes.

7.2 Real-time wind downscaling. Given the current mesoscale wind forecast (from HRRR or the nearest weather station), the system interpolates the pre-computed response function to produce a physics-informed prior wind estimate at each grid cell. This prior is combined with crowd-sourced observations in the GP framework: in areas with many observations, the prior has little influence (the data dominate); in areas with no observations, the prior provides a physically reasonable estimate that is far better than the raw mesoscale forecast.

7.3 Online model correction. The system continuously compares the physics-based prior to the crowd-sourced observations and estimates a spatially-varying bias correction field. This correction captures systematic errors in the CFD model (e.g., missing building features, vegetation not modeled, seasonal differences in deciduous tree wind blocking) and updates daily. Over time, the correction field accumulates a learned understanding of each location's actual wind behavior that exceeds the accuracy of either the CFD model or the crowd-sourced observations alone.

8. Applications and Output Products

• Pedestrian wind safety alerts: Real-time push notifications via the wearable device when the estimated wind speed along the user's planned route exceeds the Lawson discomfort (above 8 m/s) or danger (above 15 m/s) thresholds, with suggested alternative routes through less wind-exposed streets.
• Drone delivery wind service: API providing 4D (x, y, z, t) wind estimates along a proposed drone flight path, with uncertainty bounds, updated every 5 minutes. The system interpolates between pedestrian-level (1.5 m) observations and mesoscale model predictions at drone flight altitude (50 to 120 m) using a learned vertical wind profile that accounts for urban roughness.
• Building energy model calibration: Monthly wind rose data at building-specific locations, derived from months of crowd-sourced observations, for input to EnergyPlus or eQUEST simulations. Delivered as TMY3-compatible wind data files.
• Emergency plume dispersion: On-demand high-resolution wind field snapshots for input to ALOHA or HPAC dispersion models during chemical/biological incidents.
• Urban planning wind impact assessment: Historical wind statistics at proposed development sites, enabling wind comfort assessment per NEN 8100 or equivalent without commissioning a new wind tunnel study for each project.
• Insurance risk scoring: Wind exposure indices for individual building addresses, reflecting actual ground-level conditions rather than airport measurements, for commercial property insurance underwriting.
• Outdoor event planning: 48-hour wind comfort forecasts at specific park or venue locations, combining the mesoscale forecast with the crowd-sourced correction field.

9. Figures Description

• Figure 1: System architecture showing the data flow from wearable IMU through on-device feature extraction, privacy-preserving upload, GP fusion with urban canyon prior, and output products (wind field map, API, alerts).
• Figure 2: Biomechanical diagram of a walking human in crosswind, showing the aerodynamic force vector, resulting lateral CoM displacement, compensatory foot placement, and the trunk sway angle captured by the wrist-worn IMU accelerometer.
• Figure 3: Scatter plot of medio-lateral acceleration standard deviation ratio (observation / baseline) versus ground-truth crosswind speed from the co-located field validation dataset, demonstrating the monotonic relationship that the PINN learns to invert. Separate curves for crosswind (90°), oblique (45°), and headwind (0°) conditions.
• Figure 4: Example crowd-sourced wind field map for a 2 km × 2 km urban area at 25-meter resolution, showing Venturi acceleration in a narrow street canyon between two 80-meter towers, corner vortex formation at a major intersection, and wind shadow in the lee of a wide commercial building. Individual pedestrian observations shown as arrows; GP posterior mean shown as color-coded contours with streamlines.
• Figure 5: Posterior uncertainty map demonstrating how observation density drives accuracy: areas with 50+ observations per update cycle achieve below 1 m/s uncertainty; observation-free areas revert to the CFD prior uncertainty of 3 to 5 m/s.
• Figure 6: Comparison of wind speed estimates from the crowd-sourced system versus the nearest airport anemometer versus a 50-sensor research-grade urban anemometer network, over a 30-day validation period. The crowd-sourced system achieves RMSE of 1.2 m/s against the research network, versus 3.8 m/s for the airport observation.

Claims

1. A system for estimating ground-level wind speed and direction, comprising: one or more consumer wearable devices each containing an inertial measurement unit with at least a tri-axial accelerometer and tri-axial gyroscope; an on-device gait feature extraction module that computes from the IMU data a set of gait deviation features including at least medio-lateral acceleration statistics, stride length modulation, and step width variability relative to a personalized calm-wind baseline gait model; a wind estimation module that applies a trained model to invert the biomechanical transfer function from gait deviation features to a wind speed and direction estimate with associated uncertainty; and a spatial-temporal fusion module that aggregates wind estimates from multiple wearable devices across a geographic area into a continuous wind field map using a Gaussian process with a kernel informed by 3D building geometry.
2. The system of claim 1, wherein the personalized calm-wind baseline gait model is a Gaussian process regression that predicts each gait feature as a function of walking speed, terrain slope, walking surface type, and footwear identity, and wherein calm-wind calibration periods are identified automatically by cross-referencing the device's barometric pressure sensor fluctuation level with nearby weather station reports indicating sustained wind speed below 3 meters per second.
3. The system of claim 1, wherein the gait deviation features include medio-lateral acceleration skewness that encodes the direction of crosswind relative to the walking heading, anterior-posterior stride length modulation that encodes the headwind or tailwind component, step width variability that encodes wind turbulence intensity, and vertical acceleration sample entropy that encodes gust frequency and magnitude.
4. The system of claim 1, wherein the wind estimation module is a physics-informed neural network comprising a crosswind pathway and a headwind-tailwind pathway, each enforcing a physics constraint that the estimated aerodynamic force is consistent with the observed gait perturbation magnitude, and wherein the two pathway outputs are combined by vector rotation using the walking heading from the device's magnetometer and GPS to produce an absolute wind direction estimate.
5. The system of claim 1, wherein the spatial-temporal Gaussian process uses a composite kernel comprising a large-scale Matérn kernel with length scale of 500 to 2,000 meters capturing the mesoscale wind gradient, an urban canyon kernel whose length scale varies spatially as a function of building morphology features including building height, street width, aspect ratio, sky view factor, and local aerodynamic roughness length, and a temporal Matérn kernel with characteristic time scale of 15 to 60 minutes.
6. The system of claim 1, wherein the Gaussian process incorporates a physics-based prior derived from pre-computed computational fluid dynamics simulations of the coverage area's 3D building geometry, and wherein the system continuously estimates a spatially-varying bias correction field that captures systematic differences between the CFD prior and crowd-sourced observations, improving accuracy beyond either source alone.
7. The system of claim 1, wherein privacy is maintained by performing feature extraction on-device such that raw IMU waveforms are never transmitted, quantizing GPS coordinates to 25-meter grid resolution, adding calibrated Laplace noise to achieve differential privacy with epsilon equal to 1.0 per observation, and publishing wind field estimates only for grid cells that have received at least 5 independent observations within the current update window.
8. The system of claim 1, wherein the on-device gait feature extraction module classifies walking surface type from high-frequency vertical acceleration texture using a random forest classifier and identifies the user's footwear from the impact transient signature at heel strike, and wherein the calm-wind baseline model maintains separate baseline profiles for each surface type and footwear combination to improve wind perturbation detection sensitivity.
9. The system of claim 1, further comprising a pedestrian safety alert module that monitors the estimated wind field along the user's current or planned walking route and issues a notification via the wearable device when the estimated wind speed exceeds the Lawson discomfort threshold of 8 meters per second or the danger threshold of 15 meters per second, with suggested alternative routes through less wind-exposed streets computed from the current wind field.
10. The system of claim 1, further comprising a drone wind service API that provides 4-dimensional wind estimates along a proposed unmanned aerial vehicle flight path by interpolating between the crowd-sourced pedestrian-level wind observations and mesoscale model predictions at flight altitude using a learned vertical wind profile that accounts for urban aerodynamic roughness.
11. A method for estimating ground-level wind conditions, comprising: acquiring inertial measurement unit data from a consumer wearable device during a pedestrian walking bout of at least 30 seconds; computing gait deviation features by comparing observed gait parameters to a personalized context-dependent calm-wind baseline model; estimating a wind speed, wind direction, and associated uncertainty from the gait deviation features using a physics-informed model that inverts the biomechanical transfer function from aerodynamic force to gait perturbation; transmitting the wind estimate with privacy-preserving spatial quantization and differential privacy noise to a cloud backend; and fusing the wind estimate with concurrent estimates from other wearable devices and with a physics-based urban wind prior derived from 3D building geometry into a spatial-temporal wind field map using Gaussian process regression.
12. The method of claim 11, wherein fusing includes de-correlating observations from pedestrians within 5 meters of each other to avoid over-counting shared microenvironmental conditions, applying quality filters to exclude walking bouts with turning angles exceeding 30 degrees or activity classifications other than walking, and performing sparse variational Gaussian process inference with 500 to 2,000 inducing points placed at a regular grid plus additional points at known wind-critical locations including building corners and canyon throats.
13. The method of claim 11, further comprising continuously comparing the physics-based urban wind prior to accumulated crowd-sourced observations and updating a spatially-varying bias correction field that captures features not represented in the building geometry model, including vegetation wind blocking, seasonal deciduous canopy changes, temporary construction scaffolding, and other non-building obstructions, such that the bias correction field accumulates a learned understanding of each location's actual wind behavior over time.
14. The system of claim 1, wherein the aerodynamic force model estimates the user's projected frontal and lateral body area from height and weight stored in the device's health profile, clothing insulation from season and ambient temperature, and air density from the device's barometric pressure sensor and ambient temperature, and wherein these user-specific parameters are incorporated into the physics constraint layer of the neural network to improve wind estimation accuracy across diverse body types and clothing conditions.
15. The system of claim 1, wherein the wind field map output is formatted as a TMY3-compatible wind data file at building-specific locations for direct input to building energy simulation software, enabling calibration of wind-driven infiltration models using months of crowd-sourced observations rather than distant airport anemometer data.

Prior Art References

1. WMO (2018): "Guide to Instruments and Methods of Observation," WMO-No. 8. Defines standard meteorological station siting, including the 10-meter mast requirement and open terrain specifications that make airport wind data unrepresentative of urban pedestrian environments.
2. Blocken et al. (2016): "Pedestrian-level wind conditions around buildings: Review of wind-tunnel and CFD techniques and their accuracy for wind comfort assessment," Building and Environment. Comprehensive review documenting wind amplification factors of 0.2 to 3.0 in urban environments, establishing the measurement gap this system addresses.
3. Lawson (1978): "The wind content of the built environment," Journal of Wind Engineering and Industrial Aerodynamics. Establishes the pedestrian wind comfort and safety criteria (discomfort above 8 m/s, danger above 15 m/s, lethal above 20 m/s) used in building codes worldwide.
4. Yeo et al. (2021): "Urban wind field estimation for small UAS operations," Journal of Guidance, Control, and Dynamics. Documents that urban canyon wind gusts exceed mesoscale forecasts by 40 to 120% at street level, motivating the need for ground-level wind sensing for drone operations.
5. Jokisalo et al. (2020): "Wind-driven infiltration and heating energy demand in commercial buildings," Energy and Buildings. Quantifies wind-driven infiltration as 25 to 50% of heating energy loss, establishing the building energy application.
6. Hejazi et al. (2019): "The effect of wind on gait parameters during outdoor walking," Gait & Posture. Wind tunnel study demonstrating statistically significant gait perturbations (step width variability, ML sway, stride length modulation) from wind speeds of 5 to 15 m/s measured with inertial sensors.
7. Maki (1997): "Gait changes in older adults: predictors of falls or indicators of fear?" Journal of Biomechanics. Demonstrates that lateral force perturbations as small as 10 N produce detectable compensatory stepping responses, establishing the biomechanical sensitivity to wind-scale forces.
8. Menz et al. (2003): "Reliability of the GAITRite walkway system for the quantification of temporo-spatial parameters of gait in young and older people," Gait & Posture. Establishes baseline medio-lateral acceleration statistics during normal walking that the system uses for calm-wind gait model construction.
9. Bauby and Kuo (2000): "Active control of lateral balance in human walking," Journal of Biomechanics. Demonstrates that step width is actively controlled on a step-by-step basis to regulate lateral CoM position, making it sensitive to lateral perturbation and suitable as a wind indicator.
10. Mass and Madaus (2014): "Surface pressure observations from smartphones: A potential revolution for high-resolution weather prediction," Monthly Weather Review. Demonstrates smartphone barometric pressure for weather analysis; the present system uses barometric fluctuation for calm-wind identification.
11. Penwarden (1973): "Acceptable wind speeds in towns," Building Science. Provides drag coefficient values (0.9 to 1.3) for the human body in wind from various directions, used in the aerodynamic force model.
12. Tominaga et al. (2008): "AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings," Journal of Wind Engineering and Industrial Aerodynamics. Provides the CFD methodology used in the urban canyon wind prior computation.
13. Delp et al. (2007): "OpenSim: Open-source software to create and analyze dynamic simulations of movement," IEEE Transactions on Biomedical Engineering. Describes the musculoskeletal simulation platform used to generate synthetic training data for the wind estimation PINN.
14. Kong et al. (2016): "MyShake: A smartphone seismic network for earthquake early warning and beyond," Science Advances. Pioneering demonstration that smartphone inertial sensors can detect environmental physical phenomena (seismic waves) through crowd-sourced aggregation, establishing the conceptual precedent for the present system's application to wind.

Implementation Notes

A minimum viable deployment requires: (1) a mobile SDK for iOS/Android that accesses the wrist-worn companion device's IMU data via HealthKit/Health Connect or direct Bluetooth, performs on-device gait feature extraction during walking bouts detected by the existing activity recognition API, and uploads privacy-sanitized observations; (2) a cloud backend running the GP fusion on standard GPU instances (a single A100 GPU handles inference for a city of 5 million with 500,000 active devices); (3) a building geometry ingestion pipeline from OpenStreetMap plus municipal LiDAR open data programs (available for most U.S. and European cities above 100,000 population); and (4) ground-truth validation using 20 to 50 research-grade ultrasonic anemometers deployed at pedestrian height in diverse urban microenvironments. The system reaches useful accuracy (below 2 m/s RMSE in high-traffic areas) once the active device density exceeds approximately 500 devices per square kilometer during daylight hours, a threshold already met in the urban cores of most cities above 500,000 population in developed countries.