BioSage: AI-Integrated Biosensing Platform

• BioSage is a suite of integrated biosensing and AI architectures that enable real-time physiological monitoring, multi-modal data fusion, and precision aging quantification.
• Its compound AI platform leverages large language models, retrieval augmented generation, and agent orchestration to synthesize cross-disciplinary scientific knowledge.
• Validated through rigorous hardware tests, statistical analyses, and clinical benchmarks, BioSage advances applications in HCI, biomedical monitoring, and aging risk assessment.

BioSage encompasses a suite of compound AI architectures and integrated bio-sensing platforms serving cross-disciplinary research in biosensing, human–computer interaction, scientific knowledge retrieval, and biological aging quantification. It comprises: (1) a multi-modal biosensing hardware/software system for HCI and physiological monitoring, (2) a compound AI platform for cross-disciplinary knowledge retrieval and synthesis, and (3) an end-to-end AI-driven framework for biomarker integration and precision aging, each rigorously validated in technical, computational, and applied dimensions (Siddharth et al., 2018, Volkova et al., 23 Nov 2025, Kushner et al., 27 Aug 2025).

1. Multi-Modal Bio-Sensing Platform: Architecture and Validation

BioSage's biosensing platform integrates real-time multi-modal acquisition and synchronized data fusion. The system is organized as two tightly coupled subsystems: a central compute module (Raspberry Pi 3) and a wearable companion headset. Modalities include EEG (14 channels at 128 Hz, Emotiv Epoc+), PPG (ear-lobe sensor at 100 Hz, 3rd-order band-pass), accelerometry (~100 Hz), eye gaze (dual 30 fps CMOS cameras), and optional GSR (∼10 Hz). All data streams are time-stamped and relayed via Lab Streaming Layer (LSL) with empirically bounded maximum inter-stream synchronization error δ_sync ≤ max(1/f_min, t_proc) ≈ 10 ms, enforcing the Nyquist criterion per channel (Siddharth et al., 2018).

Hardware design prioritizes compact 4-layer PCBs, digital noise isolation for analog PPG, form factor (PPG board ≈20 × 15 mm, headset <200 g), and minimal power envelope (~4 W system draw). Real-time processing employs adaptive noise cancellation (ANC) for motion artifact reduction (standard LMS update) and online ICA for EEG (ORICA algorithm). Eye-gaze tracking is based on modified ellipse-fitting, with angular accuracy 1.63° (post-calibration) and RMS jitter 0.14°. Empirical validation by Bland–Altman analysis and statistical tests demonstrated PPG/ECG agreement (resting mean error ≈–0.5 %, walking error reduced from ±18 % to ±6 % with ANC, p<0.01) and eye-gaze performance surpassing commercial trackers (Siddharth et al., 2018).

2. AI Architecture for Cross-Disciplinary Knowledge Synthesis

BioSage's compound AI architecture orchestrates LLMs (LLM core: Llama 3.1 70B, GPT-4o), retrieval-augmented generation (RAG), translation, and reasoning agents. The architecture comprises a user interface, query planning agent, retrieval agent (hybrid semantic/RAG over OpenSearch vectorized corpora), translation agents bridging field terminologies, reasoning agents for micro/macro synthesis, and a response synthesizer generating citation-backed answers (Volkova et al., 23 Nov 2025).

Query planning produces either domain tags T or high-specificity keyword sets K, optimizing P(t|q), then dispatches sub-queries via the retrieval agent. Document scoring uses a convex combination of embedding cosine similarity and TF–IDF:

$$s(d\mid q) = \lambda \cdot \cos(e_q, e_d) + (1-\lambda) \cdot \mathrm{TFIDF}(q, d), \quad \lambda\in[0,1].$$

Agent orchestration routes sub-tasks, merges agent outputs, and enforces evidence traceability.

Evaluation on LitQA2, GPQA, WMDP, HLE-Bio, and a custom cross-modal biology-AI benchmark demonstrates agentic RAG performance improvements of 13–21 percentage points over vanilla LLMs. Causal analysis via Pearl's SEM (NOTEARS + CausalNex) reveals retrieval agents increase structural metrics (e.g., type-token ratio +0.16), readability (Smog Index +2.26), and biomedical performance (WMDP effect +0.22). Transparency is ensured through explicit output inspection, conversational memory, and cited synthesis (Volkova et al., 23 Nov 2025).

3. Biosensor Integration and AI Methods for Biological Aging

BioSage integrates multiplexed biosensors for four key aging biomarkers—C-Reactive Protein (CRP), Insulin-like Growth Factor-1 (IGF-1), Interleukin-6 (IL-6), Growth Differentiation Factor-15 (GDF-15)—across serum, interstitial fluid, and sweat, with detection methodologies including high-sensitivity ELISA, microneedle electrochemical sensors (LOD down to 5 pg/mL), colorimetric sweat patches, and FET/aptamer-based assays (Kushner et al., 27 Aug 2025).

Data integration pipelines encompass time-series filtering (e.g., Butterworth), baseline correction, z-score normalization, imputation, feature engineering (time and frequency domains; raw concentrations; biochemical indices, e.g., IL-6/CRP), and multimodal fusion:

$f = [f_s; f_c],$

where f_s is the sensor-derived vector and f_c is clinical input.

Machine learning ensemble includes linear regression (with L₂ regularization), elastic net, XGBoost, multilayer neural networks, convolutional/recurrent architectures for time series, VAEs for representation learning, and Transformer-based fusion models. Models optimize standardized losses for regression (MSE) and classification (cross-entropy), with Adam/SGD optimizers and regularization.

4. Applications in HCI, Biomedical Monitoring, and Knowledge Discovery

BioSage provides validated workflows for:

• Open-Environment Visual Interaction: Real-time synchronized pupil/world video streaming with YOLO-based event tagging enables linking of EEG epochs to task-relevant fixations (e.g., identifying parietal EEG responses to visual stimuli in simulated store environments) (Siddharth et al., 2018).
• Emotional Response and Neurocardiology: Multimodal EEG and PPG analysis, including HRV metrics (RMSSD, LF/HF ratio), allows detection of transient emotional arousal states. Cross-modal correlation analysis reveals coupling (r=0.45, p<0.005) between frontal EEG alpha suppression and HRV decline during high-arousal visual tasks (Siddharth et al., 2018).
• Brain–Computer Interfaces (BCI) in Naturalistic Settings: Saccade-locked ICA enables event-driven control of robotic devices, leveraging event-related EEG potentials tied to gaze events for device actuation (Siddharth et al., 2018).
• Biological Age and Risk Stratification: Ensemble models output continuous biological age and disease-risk classifications, evaluated via R², RMSE, AUC, and calibration. Typical R² is 0.80–0.90; RMSE is 3–5 years (Kushner et al., 27 Aug 2025).
• Cross-Disciplinary Scientific Discovery: AI agents support summarization, research debate, and brainstorming with explicit agent step outputs, enabling new hypothesis generation and evidence triangulation (Volkova et al., 23 Nov 2025).

5. Validation Strategies and System Performance

BioSage employs cross-validation (k=5,10), independent and longitudinal test cohorts for generalization and drift assessment. Biosensor accuracy is benchmarked against gold-standard reference methods (e.g., ECG at 1 kHz for PPG), with Bland–Altman and paired t-test comparisons (ANC reduces walking error variance by 65%, p<0.01). Eye-gaze precision is benchmarked against commercial standards (RMS jitter 0.14°, accuracy 1.21° post-motion, non-significant drift p=0.12) (Siddharth et al., 2018). AI-driven benchmarks cover biomedical (WMDP), general scientific Q&A (LitQA2, GPQA), and cross-modal biology-AI tasks, with agentic approaches consistently outperforming vanilla LLMs (Volkova et al., 23 Nov 2025).

6. Implementation, Harmonization, and Governance

System-level implementation emphasizes data harmonization across sample types and devices via standard reference calibration, streaming pipeline modularity, and metadata capture (device ID, firmware, batch). Real-time deployment leverages edge computing (on-device regression), cloud microservices (Docker/Kubernetes), and real-time anomaly detection (Kafka). Privacy and regulatory compliance follow TLS/AES-256 encryption, HIPAA/GDPR, and FDA/CE-market requirements. Federated learning and bias audits are incorporated to mitigate demographic model drift. Explainability tools such as SHAP and counterfactual explanations are employed for model transparency (Kushner et al., 27 Aug 2025).

7. Future Directions

Ongoing BioSage extensions target multimodal retrieval/reasoning (embedding tables, figures), creation of cross-disciplinary multi-modal benchmarks, next-generation protein binder design for sensor development (diffusion models, LLMs), adaptive learning for biological clocks, and substantiating clinical endpoints in longevity trials. Human–AI interaction (HAI) studies are planned for domain-expert validation, and governance frameworks are being established to ensure secure, federated, and ethical model deployment (Volkova et al., 23 Nov 2025, Kushner et al., 27 Aug 2025).