EMG2Force: EMG-to-Force Mapping

Updated 1 July 2026

EMG2Force is a framework that maps muscle electrical activity from surface and high-density EMG to continuous, fine-grained force trajectories.
It integrates advanced signal processing, deep learning, operator-theoretic models, and neuromorphic decoding to enable real-time, proportional force control.
The methodology is applied in prosthetics, VR/AR interfaces, rehabilitation, and robotic manipulation, achieving high accuracy with minimal latency.

Electromyography-to-Force ("EMG2Force", Editor's term) denotes the class of methodologies for mapping human muscle electrical activity, as recorded via surface or high-density surface electromyography (sEMG/HD-sEMG), to continuous estimates of voluntary force output. EMG2Force systems decode fine-grained force trajectories—at individual digit or multi-joint scales—enabling direct, proportional, low-latency force control signals for applications in prosthetics, human–robot interfaces, rehabilitation, and forceful teleoperation. The state-of-the-art spans deep learning, Koopman operator theory, spike-based neuromorphic inference, and classical pipelines, all rigorously evaluated in both offline and real-time regimes.

1. Signal Acquisition, Preprocessing, and Representation

EMG2Force pipelines begin with the placement and acquisition of sEMG or HD-sEMG. High channel count (e.g., 256–320 electrodes (Rahimi et al., 2024, Baracat et al., 31 Jul 2025)) provides dense spatial coverage, while wearable implementations may use 2–8 wireless sEMG sensors for real-world practicality (Zhang et al., 2022, He et al., 24 Jun 2026, Arbaud et al., 5 May 2025). Specific protocols include:

Sampling: Rates from 1 kHz to 2.048 kHz are standard to preserve the EMG spectral content (0–500 Hz).
Filtering: Bandpass (10–500 Hz), and notch (50/60 Hz) remove motion/line artifacts. Rectification or spectral envelope extraction is applied depending on the feature pipeline.
Feature Windowing: Windows and hops are tailored (e.g., 31.25 ms non-overlapping (Rahimi et al., 2024), 128–256 ms with 16 ms hop (Zhang et al., 2022), or fixed 0.5 s overlap-free (Bazina et al., 2024)).
Input Construction: Channels organized into multidimensional tensors with axes for channel, time, and frequency (Rahimi et al., 2024, He et al., 24 Jun 2026); spectral–temporal representations are constructed via Short-Time Fourier Transform (STFT) or periodogram (Zhang et al., 2022, He et al., 24 Jun 2026, Hajian et al., 2022).

Downstream alignment to ground-truth force is performed via force sensors (load cells, force-sensitive resistors, or instrumented dynamometers) (Rahimi et al., 2024, Zhang et al., 2022, Arbaud et al., 5 May 2025), with temporal resampling to match feature rates.

2. Mapping Architectures for EMG2Force

EMG2Force methods span a range of neural mapping architectures, operator-theoretic models, and neuromorphic schemes:

Deep Learning (CNN/MLP/RNN):
- High-dimensional CNNs (3DCNN-MLP): Used with HD-sEMG, maintaining channel–grid–temporal structure (Rahimi et al., 2024).
- Time-frequency CNNs: Time-domain and spectral branches fused for multimodal embedding, often with transformer decoders (He et al., 24 Jun 2026, Hajian et al., 2022).
- Encoder–Decoder CNNs with joint classification-regression heads for fingerwise probability and force output (Zhang et al., 2022).
- C-LSTM hybrids: Convolution-LSTM sequential estimators enable force tracking from low-channel data (Arbaud et al., 5 May 2025).
Koopman Operator-Based Regression: Nonlinear EMG is lifted via time-delay and indicator observables to high-dimensional linear operator space for static and dynamic prediction (Bazina et al., 2024).
Neuromorphic Decoding: HD-sEMG is decomposed to spike trains via blind source separation (fastICA); motor unit spikes drive leaky-integrate-and-fire (LIF) spiking networks on mixed-signal neuromorphic hardware (Baracat et al., 31 Jul 2025).
Multimodal Fusion: Joint encoding of EMG, time-frequency features, and inertial (IMU) signals explicitly models kinematic/velocity context for robust force prediction under non-isometric conditions (Hajian et al., 2022, He et al., 24 Jun 2026).

Tables below summarize the main methodologies:

Model Type	Input	Target Outputs
3DCNN-MLP (Rahimi et al., 2024)	5×64 HD-sEMG grids (raw+filtered)	20 hand joints, 1 grip force
CNN+STFT (Zhang et al., 2022)	8 sEMG, spectrogram	5 fingertips (force)
C-LSTM (Arbaud et al., 5 May 2025)	2 sEMG (RMS)	1 finger (force)
Koopman (Bazina et al., 2024)	1 sEMG (FFT+plateau detection)	1 grip force (est./pred.)
SNN/Neuromorphic (Baracat et al., 31 Jul 2025)	HD-sEMG → MUs (ICA/spike trains)	5 fingers (force: %MVC)
Multimodal CNN (Hajian et al., 2022)/(He et al., 24 Jun 2026)	28–32 ch. sEMG + IMU	Elbow or hand forces/finger

3. Calibration, Personalization, and Real-Time Adaptation

Calibration to individual force scales is achieved through normalization to maximum voluntary contraction (MVC) during dedicated isometric force ramps (Rahimi et al., 2024, Arbaud et al., 5 May 2025, Bazina et al., 2024). Transfer learning and rapid fine-tuning protocols (e.g., 1–2 minutes or ~15 minutes for user adaptation (Zhang et al., 2022, He et al., 24 Jun 2026)) yield user-independent to personalized performance with minimal labeled data requirements.

Real-time use is enabled by:

Pipeline Latency: Inference latencies of <10–30 ms per prediction are reported, supporting direct device or robotic control at 20–60 Hz (Bazina et al., 2024, Zhang et al., 2022, He et al., 24 Jun 2026).
Sliding Window / Buffer Architecture: Online EMG2Force models ingest continuously-updated EMG feature windows, generating force outputs at fixed rates (Zhang et al., 2022, Arbaud et al., 5 May 2025).
Closed-loop Prosthesis Control: Embedded microcontrollers receive real-time force commands, closing the loop with prosthetic actuators and load sensors (Arbaud et al., 5 May 2025, Rahimi et al., 2024).

Calibration error and user-adaptation are mitigated by normalization and retraining procedures, and the systems are robust to moderate electrode misplacement (Bazina et al., 2024, He et al., 24 Jun 2026).

4. Evaluation Metrics and Empirical Results

EMG2Force systems are evaluated by force regression accuracy, classification metrics (on/off detection), and real-world functional endpoints:

Metric	Definition	Representative Results
MAE/RMSE	$\frac{1}{N}\sum\|\hat{F}-F\|$	0.77–2.09 N (hand/finger) (Rahimi et al., 2024, He et al., 24 Jun 2026)
wMAPE	$\text{wMAPE}=\frac{\sum\|\hat{F}-F\|}{\sum\|F\|}$	5.5% (estimation), 17.9% (prediction) (Bazina et al., 2024)
$R^2$	$1 - \frac{\sum (\hat{F}-F)^2}{\sum (F-\bar{F})^2}$	$>0.9$ (offline) (Arbaud et al., 5 May 2025); $0.81–0.92$ (Hajian et al., 2022)
Pearson $r$	Correlation (PCC)	0.92–0.98 (force, kinematics) (Rahimi et al., 2024, Baracat et al., 31 Jul 2025)
Classification	Accuracy, PR-AUC, ROC-AUC	0.763/0.590 (PR-AUC, ring/pinky) (He et al., 24 Jun 2026)

State-of-the-art models achieve sub-1 N MAE for fingerwise force traces (He et al., 24 Jun 2026), 0.8–2.1 N MAE for full hand, and PR-AUC above 0.7 for challenging digits such as the pinky. Koopman-based single-channel approaches generalize with <6% wMAPE and ~18 ms latency (Bazina et al., 2024). Spiking neuromorphic implementations deliver <10% RMSE (MVC-scaled) at micro-watt power (Baracat et al., 31 Jul 2025).

5. Applications: Prosthetics, VR/AR, Robot Manipulation, and Rehabilitation

EMG2Force enables a wide span of downstream tasks:

Prosthetic Control: Direct command of actuation force in supernumerary and multifunctional prosthetic devices, delivering natural grasp force trajectories and compliant adaptation (Rahimi et al., 2024, Arbaud et al., 5 May 2025). Online error $<1$ N in tracking and targeting; real-time kinematic and kinetic hand state estimation (Rahimi et al., 2024).
VR/AR Interfaces: Natural, finger-resolved force input for lifelike object manipulation in immersive environments. Significant improvement in psychophysical discrimination of virtual material stiffness (threshold improvement 60–70%, p ≪ 0.001) (Zhang et al., 2022).
Robot Demonstration/Policy Learning: Systems such as ForceBand utilize EMG2Force to augment large-scale human demonstration datasets with per-finger force traces, facilitating robot learning of forceful manipulation across diverse objects (He et al., 24 Jun 2026).
Rehabilitation: Hand and arm force tracking for exoskeleton and functional electrical stimulation closed-loop adaptation in stroke and neuro-rehab paradigms. Koopman-lifted single-channel pipelines deliver robust estimation and prediction for grip tasks (Bazina et al., 2024).
Embedded/Low-power Wearables: Neuromorphic event-driven EMG2Force enables ultra-low power, real-time decoding for next-generation wearables, expanding application domain to untethered and mobile assistive devices (Baracat et al., 31 Jul 2025).

6. Challenges, Robustness, and Future Directions

Key considerations for the field include:

Electrode Placement and Channel Count: Dense arrays offer maximal dexterity but with cost/logistical tradeoffs; muscle-aware layout of low–moderate channel counts maximizes marginal benefit (He et al., 24 Jun 2026, Bazina et al., 2024). Robustness to modest electrode misplacement is validated (Bazina et al., 2024).
Kinematic Context: Inclusion of IMU signals increases accuracy under dynamic contraction, motion, and posture changes (Δ $R^2$ up to +138%) (Hajian et al., 2022, He et al., 24 Jun 2026).
Generalization and Calibration: Simple calibration and transfer learning enable rapid user onboarding (Zhang et al., 2022, He et al., 24 Jun 2026); SNN and Koopman approaches offer lightweight subject personalization.
Prediction Horizon: Static (instantaneous) estimation achieves lower error than dynamic-force forecasting; e.g., Koopman lift: 5.5% (est.), 18% (0.5 s prediction) (Bazina et al., 2024).
Unexplored Populations and Joints: Most validation is on healthy adults and hand/elbow tasks; extension to neurologically impaired and diverse joints remains largely open (Hajian et al., 2022, Arbaud et al., 5 May 2025).

Compared to vision-only force inference, EMG2Force achieves >50% lower MAE in manipulandum force regression (He et al., 24 Jun 2026), as vision-based systems (e.g., hand-object contact via ViTs) lack intrinsic access to force-generating neurophysiology. Finger-resolved EMG2Force outperforms proxy gripper-based approaches in robot policy learning, especially in tasks requiring precise contact compliance or dynamic force modulation. Linear regression and simple classic models are dominated by deep or operator-theoretic mappings, especially as degrees of freedom and dynamics increase (Zhang et al., 2022, Rahimi et al., 2024, Bazina et al., 2024, Arbaud et al., 5 May 2025).

EMG2Force thus establishes a scalable, validated computational substrate for proportional, continuous force decoding, with demonstrable utility across prosthetics, teleoperation, VR, rehabilitation, and manipulation learning (Rahimi et al., 2024, Zhang et al., 2022, Baracat et al., 31 Jul 2025, Arbaud et al., 5 May 2025, Bazina et al., 2024, He et al., 24 Jun 2026, Hajian et al., 2022).