Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors

Faizan Haque (1) , Mathieu Nancel (1) , Daniel Vogel (1)

1 David R. Cheriton School of Computer Science

We describe a mid-air, barehand pointing and clicking interaction technique using electromyographic (EMG) and inertial measurement unit (IMU) input from a consumer armband device. The technique uses enhanced pointer feedback to convey state, a custom pointer acceleration function tuned for angular inertial motion, and correction and filtering techniques to minimize side-effects when combining EMG and IMU input. By replicating a previous large display study using a motion capture pointing technique, we show the EMG and IMU technique is only 430 to 790 ms slower and has acceptable error rates for targets greater than 48 mm. Our work demonstrates that consumer-level EMG and IMU sensing is practical for distant pointing and clicking on large displays.

Mots clés: pointing; large displays; electromyography

Domaines: Interface homme-machine [cs.HC]

Faizan Haque, Mathieu Nancel, Daniel Vogel. Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors. ACM CHI 2015, Apr 2015, Séoul, South Korea. ⟨10.1145/2702123.2702133⟩. ⟨hal-01523315⟩