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Air Quality Data provided by: the Turkey National Air Quality Monitoring Network (Ulusal Hava Kalitesi İzleme Ağı) (sim.csb.gov.tr)
Learn more at
| or let us find your nearest air quality monitoring station |
Our GAIA air quality monitors are very easy to set up: You only need a WIFI access point and a USB compatible power supply.
Once connected, your real time air pollution levels are instantaneously available on the maps and through the API.
The station comes with a 10-meter water-proof power cable, a USB power supply,mounting equipment and an optional solar panel.
dhd.vision.gaze , dhd.physio.emg , dhd.signal.feature , dhd.ml.pipeline .
The DHD Toolbox 9: Architecture, Capabilities, and Practical Deployment – A Comprehensive Review dhd toolbox 9 download
¹ Department of Computer Science, University of Cambridge, United Kingdom ² Institute for Systems Engineering, Universidad Politécnica de Madrid, Spain ³ School of Information Technology, Indian Institute of Technology Bombay, India The latest stable tag is v9
All modules expose type hints and docstrings that are automatically rendered in the online documentation (https://dhd-toolbox.org/docs). 5.1 System Requirements | Requirement | Minimum | Recommended | |-------------|---------|-------------| | OS | Windows 10 / Ubuntu 20.04 | Linux (Ubuntu 22.04) or macOS 13 | | Python | 3.10 | 3.11 | | CPU | 4‑core (2 GHz) | 8‑core (3.2 GHz) | | RAM | 8 GB | 32 GB | | GPU | — | NVIDIA RTX 3060 (CUDA 11.8) | | Disk | 5 GB | 20 GB SSD | 5.2 Obtaining the Toolbox The official source distribution is hosted on the public GitHub organization dhd-toolbox (https://github.com/dhd-toolbox). The latest stable tag is v9.0.2 . The recommended acquisition workflow is: Real‑time inference (≈ 30 ms per 200 ms
A recurrent neural network trained on the fused feature set achieved 84 % accuracy in binary workload classification (low vs. high), surpassing the baseline (71 %) reported in the DriverState benchmark (Lee et al., 2022). Real‑time inference (≈ 30 ms per 200 ms window) was achieved using the GPU‑pipeline. 6.3 Affective State Detection in Immersive VR Scenario: Participants navigate a virtual maze while physiological signals (EDA, HR) and head‑mounted display (HMD) telemetry are recorded.
pytest -q tests/ # All tests should pass (≈ 250 tests) git fetch --tags git checkout v9.0.3 # or the latest tag pip install -e .[all] --upgrade 6. Case Studies 6.1 Clinical Gait Analysis Objective: Compute spatiotemporal gait parameters for 30 post‑stroke patients using a 12‑camera motion‑capture system (Vicon) and synchronized inertial measurement units (IMUs).
class DHDModule: @staticmethod def inputs() -> List[SignalSpec]: ... @staticmethod def outputs() -> List[SignalSpec]: ... def configure(self, cfg: dict) -> None: ... def run(self, data: DataSlice) -> DataSlice: ... The modularity permits community contributions (e.g., dhd‑gait , dhd‑driverstate ) without modifying the core codebase. The visual editor is built on Qt 6 and the Node‑Graph library. Users drag‑and‑drop module nodes, connect ports, and execute pipelines either interactively or in headless mode ( dhd flow run pipeline.yaml ). The editor automatically generates reproducible YAML specifications. 4. Core Modules and Capabilities | Category | Module | Description | Example API | |----------|--------|-------------|-------------| | Signal Pre‑processing | dhd.signal.filter | FIR/IIR filters, wavelet denoising, adaptive noise cancellation. | filter.lowpass(data, cutoff=30, order=4) | | Kinematic Reconstruction | dhd.motion.reconstruct | Marker‑gap filling, inverse kinematics (IK) using OpenSim backend. | reconstruct.ik(c3d, model='gait2392') | | Physiological Analysis | dhd.physio.hr | Heart‑rate extraction from ECG, HRV metrics (RMSSD, LF/HF). | hr.compute_hr(ecg, fs=1000) | | Eye‑Tracking | dhd.vision.gaze | Pupil‑center detection, gaze‑vector mapping to 3D scenes. | gaze.map(pupil, calibration) | | Machine Learning | dhd.ml.pipeline | Scikit‑learn and PyTorch wrappers, automated hyper‑parameter search (Optuna). | pipeline.fit(X_train, y_train) | | ROS 2 Bridge | dhd.ros.bridge | Subscribes/publishes DHD topics ( /dhd/imu , /dhd/mocap ). | bridge.subscribe('/imu', callback) | | GPU Accelerated | dhd.gpu.spectra | Real‑time spectrogram computation via CuPy. | spectra.cwt(signal, scales=np.arange(1,128)) |
| AQI | Air Pollution Level | Health Implications | Cautionary Statement (for PM2.5) |
| 0 - 50 | Good | Air quality is considered satisfactory, and air pollution poses little or no risk | None |
| 51 -100 | Moderate | Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution. | Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion. |
| 101-150 | Unhealthy for Sensitive Groups | Members of sensitive groups may experience health effects. The general public is not likely to be affected. | Active children and adults, and people with respiratory disease, such as asthma, should limit prolonged outdoor exertion. |
| 151-200 | Unhealthy | Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects | Active children and adults, and people with respiratory disease, such as asthma, should avoid prolonged outdoor exertion; everyone else, especially children, should limit prolonged outdoor exertion |
| 201-300 | Very Unhealthy | Health warnings of emergency conditions. The entire population is more likely to be affected. | Active children and adults, and people with respiratory disease, such as asthma, should avoid all outdoor exertion; everyone else, especially children, should limit outdoor exertion. |
| 300+ | Hazardous | Health alert: everyone may experience more serious health effects | Everyone should avoid all outdoor exertion |
Celsius |
