Edge-based multimodal medical monitoring requires models that balance diagnostic accuracy with severe energy constraints. Continuous acquisition of ECG, PPG, EMG, and IMU streams rapidly drains wearable batteries, often limiting operation to under 10 hours, while existing systems overlook the high temporal redundancy present in physiological signals. We introduce Adaptive Multimodal Intelligence (AMI), an end-to-end framework that jointly learns when to sense and how to infer. AMI integrates three components: (1) a lightweight Agentic Modality Controller that uses differentiable Gumbel-Sigmoid gating to dynamically select active sensors based on model confidence and task relevance; (2) a Learned Sigma-Delta Sensing module that applies patch-wise Delta-Sigma operations with learnable thresholds to skip temporally redundant samples; and (3) a Foundation-backed Multimodal Prediction Model built on unimodal foundation encoders and a cross-modal transformer with temporal context, enabling robust fusion even under gated or missing inputs. These components are trained jointly via a multi-objective loss combining classification accuracy, sparsity regularization, cross-modal alignment, and predictive coding. AMI is hardware-aware, supporting dynamic computation graphs and masked operations, leading to real energy and latency savings. Across MHEALTH, HMC Sleep, and WESAD datasets, it reduces sensor usage by 48.8% while improving state-of-the-art accuracy by 1.9% on average.
Figures & tables
Figure 1. Illustrative battery life for uni-modal vs. multi-modal sensing (300 mWh battery). Low-power IMU-only monitoring can last for many days, but adding ECG, PPG, and EMG rapidly reduces battery life to tens of hours.
Figure 2. Our Unified Agentic Multimodal Sensing and Inference Framework for Efficient, High-Accuracy Biomedical AI.
Figure 3. Proposed AMI architecture. The Σ - Δ module performs patch-wise computation and adaptive skipping. Features are fused via early cross-attention and processed by a Tiny-Transformer backbone with context reasoning. A Gumbel-MLP agent dynamically produces per-modality masks for the next window.
Figure 4. Thresholding and skipping in Sigma–Delta Sensing.
Figure 5. Training pipeline with unrolled timesteps optimized via BPTT. Each fused state St produces a prediction loss, gating loss, predictive coding loss, and a contrastive alignment loss computed against a memory bank. The controller’s gating actions At influence future observations, and all losses jointly update the model through temporal backpropagation.
Dataset
Method
Acc. (%)
F1
Sensing (%)
MHEALTH
Almujally et al. (2025) ( Almujally and others, 2025 )
94.67
-
100
Debache et al. ( Debache and others, 2020 )
98.2
-
100
Sharma et al. ( Sharma et al., 2023 )
99.07
99.1
100
AMI (no sensing reduction)
99.10
99.13
100
AMI ( λ2=0.05 )
99.12
99.12
38.19
AMI ( λ2=0.1 )
99.04
99.04
33.3
Table 1. Performance comparison of the proposed method and prior state-of-the-art approaches on MHEALTH, HMC, and WESAD datasets. Accuracy (Acc.), F1 score (F1), and average modality sensing rate (Sensing) are reported. We also show the effect of varying the gating loss coefficient λ2 on the trade-off between predictive performance and sensor efficiency.
Figure 6. Sensing rate heatmap over patches obtained from the proposed method on ( Left ) MHEALTH and ( Right ) HMC.
Configuration
Acc.
F1
Sensing
AMI (full model)
99.12
99.12
38.19
Sensing reduction ablation
w/o AMC
98.89
99.02
78.90
w/o Σ - Δ Sensing
99.09
99.12
41.67
FMPM ablation (w/o any sensing reduction)
w/o cross-modal fusion
98.11
98.12
-
Table 2. Ablation study on MHEALTH. We report metrics for the AMI pipeline (all components turned on) and for variants with individual components removed. Sensing reduction, FMPM, and loss-function contributions are evaluated separately to isolate their effects.
p
Acc. (%)
F1 (%)
0
99.12
99.12
0.2
99.10
99.12
0.5
99.08
99.01
0.8
87.21
88.47
Table 3. (Left) Robustness under random modality dropout, and (Right) Effect of input sampling rate on MHEALTH.
Dataset
Model
Lat. (ms)
Eng. (mJ)
Savings (lat./eng.)
MHEALTH
FMPM (100% sensing)
5.28
254.74
–
AMI (38% sensing)
3.69
200.14
30.01% / 21.40%
HMC
FMPM (100% sensing)
3.17
175.39
–
AMI (73% sensing)
2.87
147.31
9.46% / 16.01%
WESAD
FMPM (100% sensing)
5.68
257.32
–
AMI (42% sensing)
2.48
161.64
56.33% / 37.1%
Table 4. Runtime latency and energy comparison between the baseline and proposed AMI during inference on an ARM CPU.
Figure 7. Per-iteration latency (left) and energy (right) as the sensing rate varies on MHEALTH. Measurements are across ARM CPU, Jetson (TensorRT) and A6000 (PyTorch and TensorRT). Latency is decomposed into AMC and FMPM and values are in of ms.