Mechanomyography impact interference

Optically pumped magnetometers (OPMs) are quantum sensors enabling novel biomagnetic measurements. Integrating OPM-based magnetomyography (MMG) with conventional surface electromyography (sEMG) offers enhanced functional assessment of voluntary muscle activity (VMA), with promising applications in human-machine interaction, neuromuscular diagnosis, and rehabilitation. However, the synergistic spontaneous muscle signals exhibit highly complex interference phases, while both sEMG and MMG are characterized by nonstationary randomness and temporal variability. These properties pose significant challenges to the quantitative comparison and consistency validation of sEMG and MMG signal features. In this study, for the first time, a high-precision, real-time synchronous measurement system combining sEMG and OPM-MMG was proposed to capture compound muscle action potentials (CMAPs) produced by synergistic skeletal muscle activation. In the proposed system, the magnetically compatible electrode configuration and the modulation-based time-delay compensation technique ensure spatiotemporally aligned conditions for multimodal signal acquisition. Moreover, the multivariate feature analysis method optimized by the Thresholded Gaussian Filtering-Amplitude Probability Distribution Function (TGF-APDF) overcomes the inherent noise and complexity associated with direct comparisons of raw signals. Experimental results reveal a minimal latency difference of 0.0018 (±0.0046) s and amplitude probability distribution deviation under 5% ( $p \lt 0.01$ ) between sEMG and MMG. Meanwhile, significant differences and variability are observed in dominant modal distributions across channels. These findings demonstrate complementary strengths of the two modalities: sEMG excels in temporal resolution, while MMG provides superior spatial resolution. This work advances the multimodal assessment of muscle function, offering new insights for neuromuscular disease evaluation and motivating future applications leveraging OPM-MMG technology.

This study proposes a contactless and real-time hand gesture recognition system suitable for smartwatches. The proposed system adopts inductive proximity sensing to collect mechanomyography (MMG) signals induced by finger-based gestures above the wrist. After the signal processing and feature extraction stages, machine learning models can be built for gesture classification. Compared with the electromyography (EMG)-based method generally deployed in the forearm, inductive MMG applies an electromagnetic field to the body surface and is suitable for use on the wrist. Compared with the existing contact methods, such as EMG and accelerometer-based MMG, the inductive MMG method does not require contact with the body surface to collect muscle action signals and can avoid interference from environmental factors, such as sweat and dust. The main contributions of this study are as follows: 1) a watch-type prototype with inductive MMG sensing technique, including hardware and firmware; 2) a lightweight and efficient signal processing mechanism that can capture the inductive signal characteristics of gestures for the above-mentioned embedded system (i.e., watch-type prototype); and 3) the tempospatial feature extraction method to improve the accuracy of gesture recognition. The experimental results show that for six common machine learning models, the gesture recognition accuracy of the proposed system is above 95%, with a maximum of 97.47%. In the briefing control application for verification purposes, the average accuracy of the inductive gesture recognition system can achieve 98.26%, and the average processing time is 14.37 ms. According to the experimental results, the inductive MMG system can provide a feasible gesture recognition solution for wrist-worn wearable devices.

The growing number of amputees in Iraq with multiple degrees of amputations makes it necessary to provide them with prosthetic hands with an easy to use control system that meets their aspirations. The Mechanomyography (MMG) signal has been proposed as an alternative or assisting method for hand gesture recognition. Electromyography (EMG) which is used as control signal in the commercial prosthetic hands faces many challenges such as electrical interference, non-stationery and electrode displacement. The MMG signal has been presented as a method to deal with the existing challenges of EMG. In this paper, MMG based hand gesture recognition is proposed with Pattern Recognition (PR) system. MMG signal have been collected from six healthy subjects, using accelerometers and microphones, which performed seven classes of hand movements. Classification accuracy of approximately 89% was obtained with PR method, consisting of time domain and Wavelet feature extraction and Linear Discernment Analysis (LDA) for classification. The results showed that the proposed method has a promising way for detecting and classifying hand gestures by low-cost MMG sensors which can be used for the control of prosthetic hand.

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The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05).

INTRODUCTION: The lateral transpsoas approach has become a valuable minimally invasive option in lumbar spine surgery to achieve fusion, decompression, and deformity correction. However, femoral nerve injury during psoas retraction remains a significant concern, especially in the lower spinal levels. A newer alternative to electromyography (EMG), muscle mechanomyography (MMG) measures muscle fiber oscillation during contraction, providing a more specific and quantifiable response with a high signal to noise ratio. These characteristics make MMG an attractive option for intraoperative neuromonitoring (ION) in minimally invasive spine surgery. METHODS: Patients who underwent LLIF between 2018-2021 with MMG ION were included and compared to a historical cohot of patients who underwent LLIF from 2016-2021 with EMG ION. The femoral nerve palsy rate defined by ipsilateral thigh numbness, hip flexor, or quadricep weakness immediately post operatively were collected for both groups. Patients were re-evaluated at 3 month and 1 year follow up intervals. RESULTS: The rate of ipsilateral thigh numbness, hip flexor, and quadricep weakness at hospital discharge were 14.6% (6/41), 9.8% (4/41), and 7.3% (3/41) respectively for the MMG group, comparable to 14.8% (9/61), 23% (14/61), and 14.8% (9/61) observed in the EMG control group. Improvement in thigh numbness and leg weakness was observed for both groups at 3 month and 1 year follow up. No patients with leg weakness had less than antigravity strength. There were no statistically significant difference amongst patient demographics for both cohorts. CONCLUSIONS: In conclusion, MMG appears to be clinically equivalent to EMG as an intraoperative modality for femoral nerve monitoring in LLIF procedures. Further research is needed to evaluate the additional clinical advantages proposed by mechanomyography, such as low signal interference and quantifiable nerve localization.

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