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This paper describes a novel hand gesture recognition system that utilizes both multi-channel surface electromyogram (EMG) sensors and 3D accelerometer (ACC) to realize user-friendly interaction between human and computers. Signal segments of meaningful gestures are determined from the continuous EMG signal inputs. Multi-stream Hidden Markov Models consisting of EMG and ACC streams are utilized as decision fusion method to recognize hand gestures. This paper also presents a virtual Rubik's Cube game that is controlled by the hand gestures and is used for evaluating the performance of our hand gesture recognition system. For a set of 18 kinds of gestures, each trained with 10 repetitions, the average recognition accuracy was about 91.7% in real application. The proposed method facilitates intelligent and natural control based on gesture interaction.

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Above-device gesture interfaces let people interact in the space above mobile devices using hand and finger movements. For example, users could gesture over a mobile phone or wearable without having to use the touchscreen. We look at how above-device interfaces can also give feedback in the space over the device. Recent haptic and wearable technologies give new ways to provide tactile feedback while gesturing, letting touchless gesture interfaces give touch feedback. In this paper we take a first detailed look at how tactile feedback can be given during above-device interaction. We compare approaches for giving feedback (ultrasound haptics, wearables and direct feedback) and also look at feedback design. Our findings show that tactile feedback can enhance above-device gesture interfaces.