Abstract
Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of for force estimation while reducing the dimensionality by for a subset of three channels.
Highlights
Accurate muscle force estimation enables many applications, including control of powered prostheses, medical rehabilitation, sports medicine, and human–machine interaction [1,2,3,4,5], where electromyogram (EMG) signals have been extensively used
We propose two different approaches: One is based on principal component analysis (PCA) in the frequency domain and the other is based on a novel algorithm that calculates and maximizes an index, which is called the power-correlation ratio (PCR)
We developed a new technique for channel selection in EMG-based force estimation
Summary
Accurate muscle force estimation enables many applications, including control of powered prostheses, medical rehabilitation, sports medicine, and human–machine interaction [1,2,3,4,5], where electromyogram (EMG) signals have been extensively used. EMG signals can be acquired by electrodes located on the skin surface (non-invasive) or inserted into the muscle tissue (invasive) based on the EMG application. Surface electrodes are affixed to the skin and make contact through an electrolyte that can be either a gel or paste, or sweat in the case of dry electrodes. Surface electrodes are easy to use and provide information about muscle activity, with minimum discomfort to the participant. Invasive needle or wire electrodes are usually used to study the motor unit (MU) activities, since they provide localized information about neuromuscular activity [6].
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