Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces

By T. Scott Saponas, Desney S. Tan, Dan Morris, Ravin Balakrishnan. International Conference on Human Factors in Computing Systems, 2008

314 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/edefca31bc1a6c725fcba812a0392437bdf41e7f

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In Biomedical Engineering

In Cognitive Neuroscience

In Human-Computer Interaction

Citations in the corpus

Enabling always-available input with muscle-computer interfaces (2009, methodology): “Saponas et al. [ 18 ] used 10 EMG sensors worn in a narrow band around the upper forearm to differentiate position, pressure, tapping, and lifting gestures across five fingers placed on a surface.”
Interactive optimization for steering machine classification (2010, background): “Machine learning has also been used to infer such hidden user states as affect, intentions, and workload and cost of interruption from user activity, context signals, and/or physiological signals [20, 15 , 16, 1216, 17].”
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers (2009, unknown): “This includes work with interpreting explicit intent such as in handwriting and speech recognition as well as musclecomputer interfaces [30].”
ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers (2016, background): “Approaches can be passive, such as electromyography [45, 46], where gestures are classified by measuring the electrical signals caused by muscle activation, or active [9, 41], as in Touché [47] and Tomo [55], where a signal is injected into the body to detect hand gestures.”
3D Gestural Interaction: The State of the Field (2013, methodology): “have experimented with using forearm electromyography to differentiate fingers presses and finger tapping and lifting [20].”
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation (2017, methodology): “In addition to uses in medical applications [4,5], HD-sEMG has also been used in recent years to recognize hand gestures for MCIs [2,6–9].”
Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition (2015, background): “There is also a growing body of research that uses electrical signals generated by muscle activation (i.e., Electromyog-raphy) for gesture recognition [20,21].”