In this paper we present our results on using electromyographic (EMG) sensor arrays for finger gesture recognition. Sensing muscle activity allows to capture finger motion without placing sensors directly at the hand or fingers and thus may be used to build unobtrusive body-worn interfaces. We use an electrode array with 192 electrodes to record a highdensity EMG of the upper forearm muscles. We present in detail a baseline system for gesture recognition on our dataset, using a naive Bayes classifier to discriminate the 27 gestures. We recorded 25 sessions from 5 subjects. We report an average accuracy of 90% for the within-session scenario, showing the feasibility of the EMG approach to discriminate a large number of subtle gestures. We analyze the effect of the number of used electrodes on the recognition performance and show the benefit of using high numbers of electrodes. Crosssession recognition typically suffers from electrode position changes from session to session. We present two methods to estimate the electrode shift between sessions based on a small amount of calibration data and compare it to a baseline system with no shift compensation. The presented methods raise the accuracy from 59% baseline accuracy to 75% accuracy after shift compensation. The dataset is publicly available.

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In this work we present a wearable input device which enables the user to input text into a computer. The text is written into the air via character gestures, like using an imaginary blackboard. To allow hands-free operation, we designed and implemented a data glove, equipped with three gyroscopes and three accelerometers to measure hand motion. Data is sent wirelessly to the computer via Bluetooth. We use HMMs for character recognition and concatenated character models for word recognition. As features we apply normalized raw sensor signals. Experiments on single character and word recognition are performed to evaluate the end-to-end system. On a character database with 10 writers, we achieve an average writer-dependent character recognition rate of 94.8% and a writer-independent character recognition rate of 81.9%. Based on a small vocabulary of 652 words, we achieve a single-writer word recognition rate of 97.5%, a performance we deem is advisable for many applications. The final system is integrated into an online word recognition demonstration system to showcase its applicability.