Hardware-Aware Emotion Recognition

• Hardware-aware emotion recognition systems are platforms that infer human emotions using multimodal sensors integrated with constrained computing resources.
• They employ efficient feature extraction, dimensionality reduction, and lightweight deep learning models to deliver real-time, energy-efficient performance.
• Deployment strategies focus on sensor fusion, personalization, and privacy-preserving techniques, enabling robust applications in healthcare, HCI, and smart environments.

A hardware-aware emotion recognition system is an engineered framework or platform that infers human affective states from physiological, behavioral, or multimodal sensor input, with particular emphasis on techniques, architectures, and feature representations that maximize robustness and efficiency given resource or deployment constraints typical of embedded, wearable, or edge devices. These systems are distinct from general-purpose cloud-trained affective intelligence in that they focus not only on algorithmic accuracy but also the essential properties of low memory footprint, real-time throughput, energy efficiency, and ease of integration with physically constrained sensing hardware such as biosignal amplifiers, consumer wearables, or battery-powered platforms.

1. Sensing Modalities and Signal Acquisition

Hardware-aware emotion recognition systems span a diverse range of sensing modalities, often determined by application context and the trade-off between intrusiveness and fidelity. The following sensor types are most prevalent:

• Physiological sensors: Examples include EEG (electroencephalogram) electrodes (Ly et al., 19 Jun 2025, Yutian et al., 25 May 2024, Qazi et al., 2019), ECG (electrocardiogram) patches (Yan et al., 2023), PPG (photoplethysmogram) sensors, EDA (electrodermal activity), and GSR (galvanic skin response) modules (Perez-Rosero et al., 2016, Kwon et al., 2019). These capture internal autonomic or central nervous responses correlated with affect.
• Video-based sensors: RGB or infrared cameras capture facial expressions, head pose, and body gesture (McDuff et al., 2019, Gu et al., 2020, Liu, 13 Jul 2024). Face, landmark, and pose detectors are commonly applied for spatial preprocessing.
• Audio sensors: Microphones capture speech, voice prosody, and paralinguistic cues, often using 16 kHz or higher sampling for both speech recognition and direct emotion detection pipelines (McDuff et al., 2019, Chennoor et al., 2020, Mitsis et al., 20 Oct 2025).
• Inertial and wearable sensors: Accelerometers, gyroscopes, and heart rate modules in smartwatches or embedded devices offer a low-energy, unobtrusive means to monitor emotion-linked activity and physiological markers (Limbani et al., 2023).
• Non-contact signals: Emerging platforms exploit WiFi channel state information or RF reflections for passive, device-free emotional inference via modulation of body gestures and physiological patterns (Gu et al., 2020, Khan et al., 2020).

The interconnection and synchronization of these sensors frequently leverage microcontrollers (e.g., ESP32 (Yutian et al., 25 May 2024)), single-board computers (e.g., Raspberry Pi (Gül et al., 21 Jul 2025)), or direct wireless links (e.g., UDP, WiFi, BT/BLE protocols).

2. Feature Extraction and Dimensionality Reduction

Resource constraints motivate the adoption of highly efficient feature pipelines:

• Handcrafted Features: For physiological data, features are extracted across time, frequency, statistical, and spectral domains (e.g. mean, variance, entropy, peaks, HRV, spectral power) (Perez-Rosero et al., 2016, Kwon et al., 2019, Limbani et al., 2023), followed by dimensionality reduction using correlation-based pruning or feature selection algorithms such as ReliefF.
• Deep Feature Learning: Lightweight 1D or 2D CNNs are commonly implemented for automated feature learning from EEG, ECG, and video (Qazi et al., 2019, Ly et al., 19 Jun 2025). Advanced modules exploit multi-scale convolutions, attention mechanisms (CBAM), and fully pre-activated residual blocks (ACPA-ResNet (Yutian et al., 25 May 2024)) to selectively enhance emotion-informative channels and spatiotemporal patterns.
• Spatiotemporal Encoding: Modular architectures extract temporal dynamics using LSTM or RNN units stacked after CNN layers (notably in WiFi/RF and vision systems (Gu et al., 2020, Khan et al., 2020)). Optical flow and attention-based key-frame sub-sampling optimize processing of video sequences for real-time deployment (Nagendra et al., 24 Mar 2024).
• Signal Fusion: Feature- and decision-level fusion are achieved via score-level averaging or majority vote across modalities and channels, commonly enabled by the modular system design (Perez-Rosero et al., 2016, McDuff et al., 2019, Gül et al., 21 Jul 2025). In HDC systems, early sensor fusion via hyperdimensional binding allows ultra-low-latency integration of heterogeneous sensor streams (Menon et al., 2021).

Feature selection and reduction are crucial; in (Perez-Rosero et al., 2016) feature pruning from 27 to 18 by removing correlated dimensions preserved 85% of the full-model accuracy, highlighting efficiency gains necessary for embedded platforms.

3. Classification Algorithms and Model Architectures

The choice of classifier is tightly coupled with hardware constraints and target resource envelope:

• Linear Discriminant Analysis (LDA): Used for weak learners on each modality due to computational simplicity and closed-form inference (Perez-Rosero et al., 2016).
• Support Vector Machines (SVM): Common for both shallow and kernel-based modeling in low-memory settings, with hyperparameter tuning by metaheuristics (X-GWO (Yan et al., 2023)).
• Ensemble Learning: Two-level ensemble frameworks (majority vote across windows/channels) offer robustness and superior accuracy for both EEG and multimodal platforms (Qazi et al., 2019, Gül et al., 21 Jul 2025).
• Deep Learning: Constrained CNN models, sometimes with pyramidal or multi-scale architecture for parameter reduction, are employed for EEG and vision signals (Qazi et al., 2019, Ly et al., 19 Jun 2025). Transformer-based models, quantized and redesigned for microcontroller compatibility, demonstrate real-time capability in speech-text fusion pipelines (Mitsis et al., 20 Oct 2025). Personalized clustering (LOSO+KNN) models maintain low compute and memory requirements for wearables (Gutierrez-Martin et al., 23 Sep 2024).
• Hyperdimensional Computing: Bitwise bundling, XOR binding, and permutation operations over binary hypervectors radically reduce memory access, supporting 98% memory savings and low-power ASIC deployment (Menon et al., 2021).

Many systems utilize modular or ensemble structures in which independent, lightweight learners process single modalities before a final fusion step, maximizing parallelism and simplifying on-device deployment.

4. System-Level Hardware and Deployment Considerations

Hardware-aware implementations exhibit key architectural traits:

• Low Memory Footprint: Hardware-aware CNNs, quantized transformers, and HDC operate in memory regimes well below 2 MB (e.g., (Mitsis et al., 20 Oct 2025)), with models tailored for on-board SRAM or flash.
• Real-Time Throughput: Latencies as low as 21–23 ms for end-to-end pipeline inference on ultra low-power microcontrollers are achieved via architecture compression, MLB pipelines, and efficient pre-processing (Mitsis et al., 20 Oct 2025). Data streaming and sensor fusion leverage time-synchronization protocols and real-time edges (UDP, WiFi, Bluetooth).
• Energy Efficiency: Preference for lightweight classifiers and bit-level operations maximizes energy savings in wearable and battery-powered systems. FPGA, ASIC, or NPU acceleration is leveraged for parallelized inference, pipelined processing, and portable form factors (Palash et al., 2023, Menon et al., 2021).
• Deployment Flexibility: Consumer-grade EEG (dry electrodes), non-intrusive PPG, and wearable IMU integration allow for flexible, real-world setups, with minimal subject preparation or signal artifacts (Ly et al., 19 Jun 2025, Limbani et al., 2023, Kwon et al., 2019). Embeddable solutions are facilitated by use of open-source libraries (PyTorch Lightning, TorchEEG) and edge-to-cloud federated learning for privacy protection and scalability (Gül et al., 21 Jul 2025).
• Privacy and Security: Federated learning (FedAvg, Flower) ensures raw sensor data remain local to the device, while only model weights are shared for collaborative updates (Gül et al., 21 Jul 2025). Personalized clustering further narrows the allocation of models to user groups, reducing exposure of sensitive physiological data (Gutierrez-Martin et al., 23 Sep 2024).

5. Performance Benchmarks and Comparative Results

Hardware-aware emotion recognition systems demonstrate high empirical performance matched to resource allocation:

System/Modality	Main Classifier or Method	Accuracy/F1	Notable Features
Multimodal LDA fusion (Perez-Rosero et al., 2016)	LDA (per-modality) + fusion	88.1% (8-class)	17% better than SVM; feature reduction keeps 85%
EEG pyramidal CNN (Qazi et al., 2019)	Pyramidal 1D-CNN, 2-level ensemble	98.4% (HV/LV/HA/LA)	Only 8,462 params, real-time, ensemble
Consumer EEG multi-scale CNN (Ly et al., 19 Jun 2025)	Multi-scale CNN (5 coefficient sets)	Outperforms TSception (metric-dependent)	Designed for consumer-grade EEG; multi-regional features
Glasses-type wearable (Kwon et al., 2019)	Fisherface + PCA/LDA, multimodal	78% (facial only); 88.1% (with fusion)	Optimal sensor placement, low-discomfort
Federated edge-to-cloud (Gül et al., 21 Jul 2025)	CNN (face), RF (physio), FL fusion	77% (CNN), 74% (RF), 87% (fusion)	<200MB RAM, convergence in 18 rounds, privacy-preserving
Quantized transformer (audio-text) (Mitsis et al., 20 Oct 2025)	Quantized transformer + DSResNet-SE	Macro F1 +6.3% over baselines	1.8MB, 23 ms latency, on-device, MicroFrontend/MLTK
HDC (hypermimetic) (Menon et al., 2021)	Hyperdimensional Computing	87.1%/80.5% (valence/arousal)	98% vector storage reduction, in-situ CA generation
ECG SVM (X-GWO) (Yan et al., 2023)	X-GWO-optimized SVM	95.9% (WESAD), 93.4% (iRealcare)	2–4 ms prediction time embedded

Detailed benchmarks demonstrate that hardware-constrained approaches, when paired with efficient architectures and fusion techniques, can match or exceed general machine learning baselines while enabling real-time, low-latency deployment.

6. Applications, Challenges, and Future Directions

Application areas include adaptive HCIs, healthcare (stress and emotion monitoring), driver state monitoring, mental health diagnostics, and personalized intelligent spaces (IoT integration, smart environments). Notable application-driven features include:

• Multimodal Fusion for Robustness: Combining visual, audio, and physiological streams mitigates failure of any single modality due to environmental or subject-specific variability (McDuff et al., 2019, Gül et al., 21 Jul 2025).
• Personalization and Continual Learning: Subject clustering and semi-personalized models yield increased user-dependent accuracy (Gutierrez-Martin et al., 23 Sep 2024); some systems implement novelty detection and online learning for distributional adaptation (Palash et al., 2023).
• Edge Privacy: Federated and on-device learning strategies address privacy and regulatory requirements for sensitive physiological data (Gül et al., 21 Jul 2025).
• User-Centric Design: Consumer-friendly EEG and unobtrusive wearable form factors (smartwatch, glasses) increase adoption and compliance in real-world deployments (Ly et al., 19 Jun 2025, Kwon et al., 2019, Limbani et al., 2023).

Challenges remain in robust operation under real-world variability, scaling nuanced emotion spectra beyond binary/ternary classifications, aligning signal quality across subjects, and further reducing resource consumption for ubiquitous deployment. Directions cited include body-language integration, quantized and mobile-friendly network architectures, adaptive system personalization, and collaborative, privacy-aware learning paradigms.

7. Summary

Hardware-aware emotion recognition systems represent a convergence of efficient biosignal and behavioral sensing, compact and robust feature extraction, and tailored, low-footprint machine learning architectures. Core advances such as multi-modal weak learner fusion, ensemble-based lightweight deep models, HDC with combinatorial encoding, and real-time federated learning have established platforms capable of >85–98% classification accuracy on resource-constrained, wearable, or embedded hardware. Emerging directions continue to push toward richer multimodal data fusion, personalization, privacy compliance, and seamless integration with the next generation of human-machine interfaces.