Dashboard
Welcome to ABI Promedical. Get AI insights into your progress.
Welcome to ABI Promedical!
Your Personalized Path to Recovery, Wellness & Strength
We’re honored to be part of your journey toward better health. Our all-in-one platform integrates cutting-edge physiotherapy, rehabilitation, and health diagnostics — designed by experts, backed by science, and personalized for you.
- ✔Track your progress in real time
- ✔Access expert-guided rehabilitation plans
- ✔Get AI-powered insights and support
- ✔Secure, intuitive, and multilingual
- ✔Wherever you are — your health is in your hands
Let’s get started on building a stronger, healthier you.
ABI Promedical – Where Innovation Meets Healing.
This feature allows you to grant temporary access to selected medical information and your track record. The generated link/key will be for one-time use or time-limited.
Full implementation of secure key generation and third-party access portal is under development.
The ABI Promedical Rehab Assistant platform utilizes a secure, scalable cloud infrastructure (conceptually powered by Firebase services) to manage health data, real-time analytics, user authentication, and automated reporting workflows.
Core Capabilities Powered by Firebase:
- Real-Time Health Data Collection: Sensor readings (e.g., heart rate, gait, posture, joint angles from `/sessions/{session_id}`), mobile phone sensor data, and potentially Microsoft Kinect inputs are securely captured and written to Firestore collections like `/users/{uid}/bio`. This includes your basic health profile, comorbidities, genotype flags, language preference, and detailed timestamped session data incorporating sensor inputs and AI-derived results. Uploaded medical documents (e.g., blood panel OCR data from `/uploads/`) are also managed.
- Secure User Authentication & Session Management: Firebase Authentication supports secure login across devices (potentially including email, Google One-Tap, biometrics, and OAuth2 for health systems). User roles (e.g., patient, clinician, admin) are enforced with fine-grained access controls, with clinician access potentially via private routing.
- AI Model Hosting & Personalization Engine: Firebase Functions (e.g., `evaluateCognitiveStatus`, `mapNutritionToBioSignals`, `generateQuantumDrugRisk`, `launchCrisisProtocol`) host AI models that personalize your experience. These functions analyze data from sessions to, for example, predict motion risks, generate nutritional feedback, provide pharmacogenomic feedback, and trigger alerts for major anomalies. Firestore triggers (via Firebase Extensions or custom functions, potentially using Pub/Sub and Cloud Tasks for async processing) can enable Just-In-Time Adaptive Interventions (JITAI) and smart notifications (e.g., via Telegram/WhatsApp bot integration).
- Data Visualization & Reporting Integration: Automated functions (like `exportWeeklyData`) can prepare weekly data summaries for reporting (stored in `/reports/`) and potential export to tools like Google Sheets, Airtable, or Notion. Firebase Storage securely holds encrypted files you upload (e.g., medical reports in `/medical_reports/`, voice data in `/voice_data/`) or generated patient summaries and intermediate AI model outputs in `/ai_diagnostics/`.
- Clinical & Regulatory Compliance: Full audit logs maintained for all patient events and AI model outputs. HIPAA-compliant configuration via Google Cloud's BAA (Business Associate Agreement). GDPR-compliant data residency and retention options (e.g., 5-year retention with auto-scheduled deletion). Admin override capabilities with AI threshold-based action logs and real-time review consoles are envisioned. Every AI decision is intended to be linked to a model rationale for explainability.
For Clinicians and Regulators:
- Secure clinician dashboards allow HIPAA-safe access to aggregated and individual-level data.
- Firebase security rules are enforced to log every access, modification, and data model trigger.
- Explainability layers (e.g., model confidence, flags, exceptions) are visualized in compliance dashboards.
For Patients:
- Users receive real-time updates about their health changes through Firebase Cloud Messaging (potentially triggered by Pub/Sub events for dashboard syncs).
- All health metrics and reports can be downloaded directly or shared with clinicians via encrypted links.
Firebase ensures ABI Promedical delivers a responsive, privacy-conscious, and audit-friendly experience suitable for both clinical deployments and regulatory approvals.
Each test and assessment within the ABI Promedical app includes:
- Step-by-step video and animation walkthroughs, potentially with voice-guidance.
- A “Why this matters for you” section with condition-specific insights (e.g., stroke, scoliosis, low back pain).
- Results shown with traffic light visuals (green/yellow/red) and plain language insights.
- AI-generated PDF reports ready for download or sharing with a physician.
- A voice-based virtual assistant (conceptually powered by models from Genkit, acting as a triage overlay) guides users through reports and symptom logs.
Empowering Your Journey
The ABI Promedical Rehab Assistant integrates cutting-edge scientific and clinical research into its core diagnostic and therapeutic modules. The app’s AI-driven features are built upon validated findings from peer-reviewed literature in rehabilitation science, biomedical signal processing, wearable sensor development, and behavioral health informatics.
Evidence-Based AI Applications:
- Stroke and Neurorehabilitation: Modules support hemiparetic gait retraining, post-stroke arm function recovery, and balance improvement using validated motion-tracking protocols derived from studies using Kinect and IMU-based assessment tools.
- Psoriasis & Inflammatory Monitoring: Bayesian and causal modeling frameworks are integrated to assess flare-up risk based on lifestyle, stress, skin imaging, and inflammatory marker trends—aligned with clinical studies on obesity-immune interaction.
- Cognitive and Behavioral Surveillance: Eye-tracking fatigue metrics and app engagement patterns are modeled after research on cognitive load, mental fatigue, and digital distraction. Sleep-anxiety-behavior linkages are mapped using SEMs validated in psychiatric and behavioral therapy research.
- Pulmonary and Cardiac Monitoring: Respiratory rate, HRV, and audio-derived breathing models are applied from studies in mobile spirometry, wearable cardiology, and remote pulmonary rehabilitation feasibility trials.
- Ethical and Federated Learning Models: AI model design respects principles of fairness, accountability, and transparency (FAT-AI) and aligns with METRIC standards for healthcare data quality, enabling future MDR readiness and auditability.
AI Training Pipeline Schema:
Our AI models are developed through a rigorous, multi-stage pipeline:
- Input Sources: Diverse data streams including Kinect v2 (depth, IR, skeleton), mobile gyroscope, smartwatch data (HR, SpO2), EEG, ECG, smart insoles, medical uploads (blood tests, genetic panels via OCR/NLP), and structured surveys (mental health scales like PHQ-9/GAD-7, lifestyle, diet).
- Preprocessing & Feature Engineering: Sensor data undergoes normalization (Kalman/Bessel filters), time-series windowing, FFT transforms, and biomechanical vector modeling. Textual data is processed using NLP embeddings (BERT, BioClinicalBERT, Genkit/GPT-based summarization). Dimensionality reduction techniques like PCA and UMAP are applied for sensor fusion.
- Model Branches (Examples):
- Orthopedic Motion: Biomechanical CNN-HMM ensembles for gait analysis, joint degeneration, and rehabilitation scoring.
- Neurological: Attention-tracked eye behavior combined with EEG entropy for early neurodegeneration screening.
- Cardiovascular: LSTM+XGBoost ensembles for HRV, fatigue, and VO2 max trends.
- Metabolic & Nutrition: Variational Autoencoders (VAEs) trained on patient phenotype clusters, enabling generative diet protocols.
- Drug Interaction Risk: Advanced predictors, potentially incorporating Quantum-enhanced (QML-based) methods for ADMET mapping.
- Training Stack: Utilizes frameworks like PyTorch and TensorFlow 2.0. For advanced research, quantum simulator backends (e.g., Qiskit, PennyLane) are explored. Model interpretability is ensured using SHAP & LIME, and the entire process is managed with MLflow, Firebase remote logging, and CI/CD pipelines (GitHub + Firebase Deploy).
Incorporated Research-Backed Improvements:
- Drug repurposing engine using molecular docking + MM-GBSA/MM-PBSA scores.
- Exploration of Quantum ML for early-phase molecule screening (e.g., protein folding, retrosynthesis).
- Integration of safety-critical ML blueprints for real-time system validation.
- Precision medicine linkage via phenotype-genotype mapping, potentially using federated learning.
- AI ensemble methods to improve diagnostic accuracy for rare diseases.
Academic and Clinical Alignment:
- All AI modules follow ISO 13485-aligned software lifecycle protocols.
- Clinical endpoints and analytics are structured around WHO-recommended functional indicators, ICF domains, and FDA-recognized digital biomarkers.
- Publications informing model design are documented and traceable in an internal evidence database.
This direct linkage between scientific literature and functional deployment ensures ABI Promedical is not just technically robust, but also clinically trustworthy, ethically built, and future-proof for use in academic research, regulated healthcare, and commercial wellness settings.
The ABI Promedical Rehab Assistant platform integrates multiple layers of mathematical and AI-driven models that transform raw sensor data into clinically actionable insights. Each physiological or behavioral parameter captured through mobile, wearable, or camera-based input is analyzed using validated statistical or machine learning techniques. These models are critical for ensuring both diagnostic precision and real-time adaptability.
Key Algorithms & Mathematical Methods:
| Function | Inputs | Model/Formula Used | Clinical Outcome |
|---|---|---|---|
| Gait Symmetry & Fall Risk | Kinect + Gyroscope | Gait Index = L-R / (0.5 * (L+R)); HMM classifier | Balance deviation, Parkinson's screening |
| HRV (Heart Rate Variability) | RR Intervals via PPG | RMSSD = √(mean(diff²)); SDNN; LF/HF ratio | Autonomic nervous system stress & fatigue analysis |
| Cognitive Fatigue Detection | Eye tracking, app usage patterns | Fixation/velocity analysis; k-means clustering | Mental fatigue risk & neurocognitive status |
| Inflammation Detection (Skin/Joint) | Infrared camera (thermal variance) | ΔT = T_peak - T_baseline; hotspot region segmentation | Inflammatory hotspot prediction |
| Joint Range of Motion | Kinect or camera angle data | θ = arccos((A•B)/(|A||B|)); spline curve fitting | ROM scores for orthopedics, post-injury recovery |
| Psoriasis/Obesity Flare Modeling | Logs, biomarkers, lifestyle, stress | Bayesian regression & causal chain modeling | Flare-up forecast, inflammation scoring |
| Sleep-Anxiety-Phone Chain | Activity logs + surveys + usage data | Structural Equation Modeling (SEM) | Behaviorally informed rehab plan |
| JITAI Alert Engine (adaptive AI) | Multi-sensor + context data | Random Forest + time-decay weighting | Real-time intervention or alert |
Explainability & Clinical Trust:
- SHAP and LIME used for interpretability of all ML results.
- Every model includes confidence intervals and outlier control.
- Raw and interpreted data stored securely, accessible to clinicians for audit and retraining.
This mathematical foundation ensures the ABI Promedical platform remains both clinically rigorous and dynamically responsive to each user’s evolving rehabilitation profile.
The ABI Promedical Rehab Assistant app is designed with a dual-purpose interface—optimized for both patient usability and clinical precision. It combines intuitive navigation with medically validated outputs to improve user engagement, clinical compliance, and trust.
For Patients:
- The app presents diagnostics using easy-to-understand visuals (e.g., green-yellow-red scoring, trend graphs, body diagrams).
- Results are accompanied by clear explanations such as "Why this matters" and "What to do next," tailored to individual diagnoses.
- Built-in video walkthroughs, potentially with voice-guidance, demonstrate exercises, assessment protocols, and the relevance of each task.
- A voice-based virtual assistant (powered by Genkit interacting with models like Gemini, acting as a triage overlay) guides users through reports and symptom logs.
For Clinicians:
- Clinician dashboards provide timestamped logs, comparative analytics, AI-derived insights, and report export options.
- Explainable AI (XAI) is embedded in all diagnostic outcomes—enabling healthcare providers to understand and trust the algorithmic reasoning using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations).
- Secure patient-specific access portals allow clinicians to adjust rehab plans, monitor adherence, and validate trends.
Engagement & Accessibility:
- Multilingual UI supports over 30 languages.
- ADA-compliant color schemes and text resizing.
- Offline functionality ensures usability in low-bandwidth clinical environments.
Overall, the ABI interface bridges the gap between automated analysis and human understanding—ensuring insights are not just accurate but actionable and personalized.