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After spending some time thinking on how to use Myo for text input and experimenting a little bit we think we can share some of our findings. Strengths: Myo does have some strengths when compared to other input devices. 1- It is minimally intrusive and can be used for mobile interaction. 2- It can sense hand poses. 3- It has IMU with accelerometers and gyros to detect arm movement. Limitations: Some of them are due to myo's specific sensing technology 1- There are only a few hand poses that the MYO can recognize 2- It seems to be sensitive to the initial calibration. 3- Hand poses can be stressful and difficult to change between them (this is required for proper detection). 4- Many false negatives (generally for the less stressful poses). Some are shared with other 3D interface devices: 1-Live mic problem - hand poses can be activated by accident, though rarely. 2-Lack of spatial frame of reference. Ideas: To overcome the limitations we tried several things in our design: 1-Restrict the set of hand poses used frequently to the ones that offer less false negatives (wave-in and wave-out). 2-Use the initial set of recognized hand poses as primitives to obtain a larger set ( add gesture and gyroscope data). 3-Allow for continuation of gestures. Since most gestures are stressful, allow to user to specify parameters by continuing the motion. 4-Try to use relative hand positions instead of using absolute space positions. 5-Try to transition between gestures that are easy to perform (some gestures are hard to transition e.g. between fist an open hand). We hope that these ideas are useful to others designing interaction techniques using Myo.
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On a previous blog entry I mentioned that Myo could be an interesting device for bare hand text input. After being able to play with it for a while I can offer my first impressions. The armband seems pretty well designed. It has no hard edges and the material feels good to touch. It is somewhat bulky and even though it is not heavy on the arm, the aesthetics might be more appealing to men. It also comes with small clips that you can use to tighten the band if you have thin arms. There are no buttons, just a USB connector for charging and a glowing logo in one of the pods.
The SDK recognizes five different hand poses: palm pointing left, right, spread fingers, fist and thumb to pinky. These poses can be combined with data from the IMU to create more complex patterns and detect movement. The API gives access to the pose detected, orientation data and the vibration motor. As we inferred the poses are fairly independent from the arm orientation, which allows more flexibility for design. The quality of the built in classifier is also good for a first version. We noticed some false negatives and false positives with the former being more common. Sometimes you need to repeat the same gesture several times before it gets acknowledged. In our limited experience the pinky to thumb seems the most difficult to recognize, which also correlates with the fact of it is the less stressful pose. Left and right hand poses are the most reliable, they are almost flawless. People seems to hold different opinions regarding which pose is the most tiresome, but in general all of them seems less fatiguing than we initially thought. In retrospect the engineers at Thalmic made a good choice in selecting the poses and the decision of shipping with only five of them was also wise. However, as a researcher I wish I could have access to more data. This would allow me to play with new algorithms or just select poses that make more sense for my particular task. I hope they change it on the future like Leap Motion did. It does not need to provide all EMG data, just the "tension" values for each group of muscles would suffice: palm, thumb, fingers. I do not believe they have constructed their classifier in this way but I think it can be done. This seems a good tradeoff between flexibility, simplicity and battery life.
As we spend more time with the armband we might need to adapt our original ideas for text input. If the gesture pinky-to-thumb end up being unreliable we might be tempted to remove it altogether from our design. The information from the IMU, on the other hand, may open more interesting directions for our work. We experimented with the rotation of the arm (roll), for example, and it seems pretty stable. Summing up, even with the current limitations Myo it is a very interesting device. It is ready for some new cool interaction propositions. Anyone ? |
Wallace Lages
Assistant professor and entrepreneur. Archives
May 2015
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