Acoustomyography, or AMG, differs from the surface electromyography commonly used to control powered prostheses. Rather than measuring electrical potentials at the skin, the forearm-worn system uses microphones to record mechanical and low-frequency acoustic emissions associated with muscle contraction. Its six sensors are distributed circumferentially around the forearm, but they were not intended to isolate particular flexor, extensor or rotator muscle groups. Because acoustic vibrations can propagate mechanically through surrounding tissues, individual channels were not interpreted as measurements of single muscles. The investigators said this transduction principle motivates investigation of AMG as an alternative or complementary motor signal, but the study did not establish an advantage over sEMG because no matched sEMG measurements were collected from the same participants under the same gestures, repetitions and evaluation splits.

The offline dataset comprised 500 recordings from 10 healthy, right-handed adults, five men and five women, with an age of 28 ± 5 years. Each participant performed 10 repetitions of hand close, hand open, fine pinch, tripod grip and index-finger point. Every trial was a pre-cued, sustained gesture lasting approximately three to four seconds at a comfortable moderate contraction level, with three to five seconds of rest between trials. No measurement normalized contraction intensity to a maximum voluntary contraction. The bracelet remained in the same predefined forearm region, with limited proximal–distal adjustment permitted for individual morphology, although bracelet tension, contact pressure and repositioning repeatability were not quantified as independent variables. Signals from the six electret microphones were digitized at 1,000 Hz with a 24-bit laboratory acquisition system. The stored amplitudes were treated as relative acoustic values because no channel-level calibration was performed.

Before classification, each retained gesture interval was divided into 400-millisecond windows with 200-millisecond hops, producing 50% overlap. A fourth-order Butterworth filter digitally band-limited the signals to 1–100 hertz, although the microphones’ documented lower response began at 15 hertz, so this range described the preprocessing rather than the physical analog bandwidth. Each window was converted into a 138-dimensional feature vector. For every channel, the researchers calculated four time-domain statistics—mean, standard deviation, skewness and kurtosis—13 mel-frequency cepstral coefficients, or MFCCs, and six discrete-wavelet-transform statistics. A three-level Daubechies-4 decomposition supplied the wavelet descriptors. Feature-wise z-score normalization used statistics estimated from the relevant training split. Window duration, MFCC dimensionality and model configuration were selected on the same 10-participant development dataset later used for held-out evaluation, a design that the authors said could leave an unquantified optimistic model-selection bias in the leave-one-subject-out results.

The deployed classifier was a one-dimensional convolutional neural network operating on the engineered 138-feature sequence rather than on raw acoustic samples. In subject-mixed testing, in which the same participants could appear in training and test partitions, its accuracy was 95.0%, with 1,174 of 1,236 test windows classified correctly and a 95% Wilson confidence interval of approximately 93.6–96.1%. The more demanding leave-one-subject-out test, in which each participant was entirely excluded from training in turn, produced a mean accuracy of 90.6%, a median of 93.0% and a range of 70–96%. Nine participants achieved at least 89%, while one reached 70%, showing that cross-subject performance was not uniform. A radial-basis-function support vector machine performed better offline, reaching 97.0% in the subject-mixed setting and 94.6% across held-out participants; it outperformed the neural network in nine of ten paired folds, with a two-sided Wilcoxon signed-rank result of W = 1 and p = 0.004. The support vector machine was therefore not the network against which the embedded prototype could be directly compared, because the study did not preserve the trained model’s support-vector count or make a complete memory comparison.

The convolutional model was selected for the embedded prototype not because it was the more accurate offline classifier, but because its fixed feed-forward architecture could be converted through the TensorFlow/Keras-to-STM32Cube.AI workflow. The network contained 82,193 parameters, of which 82,009 were trainable. Its convolution operated on a fixed ordering of heterogeneous engineered descriptors and channel boundaries, so the authors did not interpret its local interactions as a physically homogeneous temporal or anatomical axis. Post-training INT8 quantization used 1,000 representative feature vectors and reduced accuracy by 0.4 percentage points, from 95.0% to 94.6%, while the deployed model occupied approximately 98 KB of flash memory. Feature extraction from 50 representative recordings on the workstation and embedded C implementation differed by less than 2% on average across the 138 dimensions, although that comparison tested the numerical implementations for matched inputs rather than directly comparing the 24-bit laboratory and 12-bit microcontroller acquisition chains.

On the 216-megahertz STM32F769I, the measured processing time for a completed 400-millisecond window was 118.3 milliseconds. With 50% overlap, a new window became available every 200 milliseconds, leaving 81.7 milliseconds before the next update. Spectral processing was the largest expense: fast Fourier transforms across six 512-point inputs took 45.3 milliseconds, the mel filterbank and logarithm took 18.7 milliseconds, and the discrete cosine transform took 13.9 milliseconds. Wavelet decomposition required 15.1 milliseconds, filtering 5.2 milliseconds, time-domain statistics 1.8 milliseconds, feature normalization 0.7 milliseconds and INT8 neural-network inference 17.6 milliseconds. The system used 320 KB of the microcontroller’s 2 MB flash memory and 85 KB of its 512 KB SRAM. The 118.3-millisecond figure covered filtering, feature extraction, normalization and inference; it did not include the 400-millisecond signal-acquisition interval, communication outside the measured code path, actuator activation or the mechanical movement of the hand.

The real-time trial moved beyond replaying stored signals. Five additional right-handed able-bodied adults, three men and two women aged 23–27, produced 250 trials through the STM32’s 12-bit analog-to-digital converters. Each trial followed a gesture instruction, transition, sustained contraction and relaxation. Before classification, a global root-mean-square activity gate rejected windows below an empirically selected threshold of 80 least significant bits, corresponding to 64.45 mV at the analog-to-digital converter input. Active predictions were converted into pulse-width-modulated commands for the 5-DOF prosthetic-hand prototype. Only sustained-contraction windows that passed the gate were scored, with rest and transition periods excluded. Mean accuracy was 95.3%, with a standard deviation of 1.7 percentage points and individual results from 92.7% to 97.3%; fine pinch was the least accurately recognized gesture, with a mean class rate of 92.9%. This online result should not be directly equated with the 90.6% leave-one-subject-out offline result because the scoring conditions and participant groups differed.

Several limitations constrain what the demonstration means. The offline and real-time cohorts contained only 15 able-bodied participants in total, so the findings cannot yet be assumed to apply to people with upper-limb amputation, whose residual-limb anatomy, musculature, socket mechanics and sensor coupling may differ. Participants performed discrete gestures under controlled conditions, while bracelet displacement and tension, arm posture, contraction intensity, fatigue, external vibration, environmental noise and day-to-day variation were not systematically tested. The study also did not quantify false commands during unconstrained rest, performance during transitions, object-grasp success, task-completion time or the mechanical response of the hand. Although a 400-millisecond statistical analysis found that 95.26% of 54,504 channel-wise windows met a predefined combined Augmented Dickey–Fuller and KPSS criterion, those overlapping windows were nested within only 500 recordings from 10 people and were not 54,504 independent observations. The authors consequently described the result as local compatibility with the statistical criterion, not proof that the signals were broadly or spectrally stationary.

Overall, the work shows that acoustic muscle sensing, engineered feature extraction and compact neural-network inference can be integrated into a real-time prosthetic-control pipeline under controlled laboratory conditions. The next steps identified by the investigators are repeated-session measurements, deliberate changes in sensor placement, explicit rest and transition conditions, continuous closed-loop tasks and direct paired AMG–sEMG comparisons using identical participants, gestures, repetitions, training and test partitions, metrics and computational constraints. Evaluation in a larger population including intended prosthesis users will also be necessary before the system can be considered a clinically validated alternative to established myoelectric interfaces.

Subject of Research: Cross-subject acoustomyography and embedded neural-network control of a 5-DOF prosthetic-hand prototype

Article Title: AMG-based real-time prosthetic hand control: Cross-subject evaluation and embedded implementation

Article References: Shili, W. E., Serrestou, Y., Raoof, K., & Yacoub, S. (2026). AMG-based real-time prosthetic hand control: Cross-subject evaluation and embedded implementation. Results in Engineering, 32, Article 113567. https://doi.org/10.1016/j.rineng.2026.113567

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113567

Keywords: acoustomyography, prosthetic hand, gesture recognition, surface muscle sensing, embedded inference, STM32 microcontroller, INT8 quantization, neural networks, cross-subject evaluation, proof of concept, AMG-based, real-time