FMG Versus EMG: A Comparison of Usability for Real-Time Pattern Recognition Based Control.

OBJECTIVE: Force myography (FMG), which measures the surface pressure profile exerted by contracting muscles, has been proposed as an alternative to electromyography (EMG) for human-machine interfaces. Although FMG pattern recognition-based control systems have yielded higher offline classification accuracy, comparatively few works have examined the usability of FMG for real-time control. In this work, we conduct a comprehensive comparison of EMG- and FMG-based schemes using both classification and regression controllers. METHODS: A total of 20 participants performed a two-degree-of-freedom Fitts' Law-style virtual target acquisition task using both FMG- and EMG-based classification and regression control schemes. Performance was evaluated based on the standard Fitts' law testing metrics throughput, path efficiency, average speed, number of timeouts, overshoot, stopping distance, and simultaneity. RESULTS: The FMG-based classification system significantly outperformed the EMG-based classification system in both throughput (0.902 ± 0.270) versus (0.751 ± 0.309), (ρ < 0.001) and path efficiency (87.2 ± 8.7) versus (83.2 ± 7.8), (ρ < 0.001). Similarly, FMG-based regression significantly outperformed EMG-based regression in throughput (0.871 ± 0.2) versus (0.69 ± 0.3), (ρ < 0.001) and path efficiency (64.8 ± 5.3) versus (58.8 ± 7.1), (ρ < 0.001). CONCLUSIONS: The FMG-based schemes outperformed the EMG-based schemes regardless of which controller was used. This provides further evidence for FMG as a viable alternative to EMG for human-machine interfaces. SIGNIFICANCE: This work describes a comprehensive evaluation of the online usability of FMG- and EMG-based control using both sequential classification and simultaneous regression control.

Electromyography and mechanomyography of elbow agonists and antagonists in Parkinson disease.

The purpose of this study was to assess the electromyographic (EMG) and mechanomyographic (MMG) activities of agonist and antagonist muscles in Parkinson disease patients during maximal isometric elbow contraction in flexion and extension. Ten elderly females with Parkinson disease (average age 75 years) and 10 age-matched healthy females were tested. The torque and the EMG and MMG signals from biceps brachii and triceps brachii were recorded during sustained maximal voluntary isometric contraction of the elbow flexors and extensors. There were no intergroup differences in the EMG and MMG activities of agonist and antagonist muscles or in torque. This might be because the Parkinson subjects were tested during their medication "ON" phase, or perhaps maximal isometric contraction (MVC) induced greater active muscle stiffness that affected the MMG signal. Muscle Nerve 40: 240-248, 2009.

Evidence for intermuscle difference in postactivation potentiation in the human triceps surae: a mechanomyographic study.

This study compared the postactivation potentiation of the medial gastrocnemius (MG) and soleus muscles by using mechanomyography (MMG). Twitch responses were evoked by stimulating the posterior tibial nerve with supramaximal intensity before and after a 10-s maximal voluntary plantar flexion, and potentiation of each muscle was evaluated by peak-to-peak amplitude of the MMG signal. The MG showed greater potentiation than the soleus, reflecting the reported fiber type composition of these muscles. This result points to the possibility that one can delineate contrasting responses of synergist muscles to postactivation potentiation by this method. Muscle Nerve 39: 703-706, 2009.

Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control.

Previous works have resulted in some practical achievements for mechanomyogram (MMG) to control powered prostheses. This work presents the investigation of classifying the hand motion using MMG signals for multifunctional prosthetic control. MMG is thought to reflect the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction. However, external mechanical noise sources such as a movement artifact are known to cause considerable interference to MMG, compromising the classification accuracy. To solve this noise problem, we proposed a new scheme to extract robust MMG features by the integration of the wavelet packet transform (WPT), singular value decomposition (SVD) and a feature selection technique based on distance evaluation criteria for the classification of hand motions. The WPT was first adopted to provide an effective time-frequency representation of non-stationary MMG signals. Then, the SVD and the distance evaluation technique were utilized to extract and select the optimal feature representing the hand motion patterns from the MMG time-frequency representation matrix. Experimental results of 12 subjects showed that four different motions of the forearm and hand could be reliably differentiated using the proposed method when two channels of MMG signals were used. Compared with three previously reported time-frequency decomposition methods, i.e. short-time Fourier transform, stationary wavelet transform and S-transform, the proposed classification system gave the highest average classification accuracy up to 89.7%. The results indicated that MMG could potentially serve as an alternative source of electromyogram for multifunctional prosthetic control using the proposed classification method.