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A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings

View Article: PubMed Central

ABSTRACT

In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed.

No MeSH data available.


Information Fusion by means of the Kalman Filter allows to improve reconstruction accuracy by adaptively balancing the contribution from the two information sources.
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Figure 1: Information Fusion by means of the Kalman Filter allows to improve reconstruction accuracy by adaptively balancing the contribution from the two information sources.

Mentions: As outlined in the previous two subsections, we have two independent sources of information about the state vector. The first endogenous source bases its predictions on the dynamical characteristics of the pen coordinates during the handwriting and yields f1(st/st−1) as the state vector distribution (red distribution in Figure 1). The second source is exogenous and uses externally registered EMG signals to suggest f2(st/zt) as the state vector distribution (blue distribution in Figure 1). In order to reconstruct the state vector, optimally taking into account the predictions from both sources, we perform the statistical fusion of the estimates based on the dynamical and the measurement models. The schematic procedure of the source fusion is illustrated in Figure 1.


A dynamical model improves reconstruction of handwriting from multichannel electromyographic recordings
Information Fusion by means of the Kalman Filter allows to improve reconstruction accuracy by adaptively balancing the contribution from the two information sources.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4624865&req=5

Figure 1: Information Fusion by means of the Kalman Filter allows to improve reconstruction accuracy by adaptively balancing the contribution from the two information sources.
Mentions: As outlined in the previous two subsections, we have two independent sources of information about the state vector. The first endogenous source bases its predictions on the dynamical characteristics of the pen coordinates during the handwriting and yields f1(st/st−1) as the state vector distribution (red distribution in Figure 1). The second source is exogenous and uses externally registered EMG signals to suggest f2(st/zt) as the state vector distribution (blue distribution in Figure 1). In order to reconstruct the state vector, optimally taking into account the predictions from both sources, we perform the statistical fusion of the estimates based on the dynamical and the measurement models. The schematic procedure of the source fusion is illustrated in Figure 1.

View Article: PubMed Central

ABSTRACT

In recent years, several assistive devices have been proposed to reconstruct arm and hand movements from electromyographic (EMG) activity. Although simple to implement and potentially useful to augment many functions, such myoelectric devices still need improvement before they become practical. Here we considered the problem of reconstruction of handwriting from multichannel EMG activity. Previously, linear regression methods (e.g., the Wiener filter) have been utilized for this purpose with some success. To improve reconstruction accuracy, we implemented the Kalman filter, which allows to fuse two information sources: the physical characteristics of handwriting and the activity of the leading hand muscles, registered by the EMG. Applying the Kalman filter, we were able to convert eight channels of EMG activity recorded from the forearm and the hand muscles into smooth reconstructions of handwritten traces. The filter operates in a causal manner and acts as a true predictor utilizing the EMGs from the past only, which makes the approach suitable for real-time operations. Our algorithm is appropriate for clinical neuroprosthetic applications and computer peripherals. Moreover, it is applicable to a broader class of tasks where predictive myoelectric control is needed.

No MeSH data available.