editor's blog
Subscribe Now

Muscling Up

We’ve seen gesture recognition before, and the two major modes, if you will, are using cameras (either 2- or 3-D) to “see” and interpret gestures and using inertial sensors to detect hand motion and infer gestures.

Thalmic is about to launch its own gesture control armband, but they rely on a completely different source of information for detecting gestures: muscle movements. Or, more accurately, the electrical signals that govern muscle movement.

The measurement technique is called “electromyography” (EMG), and the device they’re building is called the Myo. While it does contain an inertial sensor, they say that they can detect much more subtle gestures by reading the muscles and cross-referencing that information with that of the IMU, making outsized gesturing less necessary. They claim that the EMG readings are impervious to sweat, dryness, heat, hair, and differences in muscle tone.

Each device contains 8 EMG sensors plus an IMU, some computing capability, and Bluetooth LE. The signals are processed in the armband; the output is an event representing a classified gesture. All of the usable gestures are pre-defined; they’re keeping the number of gestures to a small number.

While the gestures are fixed, their meanings aren’t. Application developers can use their SDK to assign specific semantics for the gestures within their applications. It’s even possible to fuse the events from two different armbands (one on each arm) for more complex two-handed gesturing.

I talked to them in May at the Embedded Vision Summit (ironic); at that time they had alpha samples out for developers. They recently announced the final design, slimming down and changing the look as compared to the alpha armband. In the process, they had to redo some of the electronics to accommodate the shape – and, according to their blog, they’ve improved the electrical performance in the process. Final devices are now expected to ship in September.

Myo_figure.png

 

This doesn’t strike me as something you’d just wear around; it’s still pretty bulky as an accessory. But using it specifically as an input device for things like gaming is an interesting twist. It will also be interesting to see what new roles EMG may provide in future devices.

Leave a Reply

featured blogs
Nov 22, 2024
We're providing every session and keynote from Works With 2024 on-demand. It's the only place wireless IoT developers can access hands-on training for free....
Nov 22, 2024
I just saw a video on YouTube'”it's a few very funny minutes from a show by an engineer who transitioned into being a comedian...

featured video

Introducing FPGAi – Innovations Unlocked by AI-enabled FPGAs

Sponsored by Intel

Altera Innovators Day presentation by Ilya Ganusov showing the advantages of FPGAs for implementing AI-based Systems. See additional videos on AI and other Altera Innovators Day in Altera’s YouTube channel playlists.

Learn more about FPGAs for Artificial Intelligence here

featured paper

Quantized Neural Networks for FPGA Inference

Sponsored by Intel

Implementing a low precision network in FPGA hardware for efficient inferencing provides numerous advantages when it comes to meeting demanding specifications. The increased flexibility allows optimization of throughput, overall power consumption, resource usage, device size, TOPs/watt, and deterministic latency. These are important benefits where scaling and efficiency are inherent requirements of the application.

Click to read more

featured chalk talk

Reliability: Basics & Grades
Reliability is cornerstone to all electronic designs today, but how reliability is implemented and determined can vary widely by different market segments. In this episode of Chalk Talk, Amelia Dalton and Sam Accardo from the YAGEO Group explore the definition of reliability for electronic components, investigate the different grades of reliability offered by the YAGEO Group and the various steps that the YAGEO Group is taking to ensure the greatest reliability of their components.
Aug 15, 2024
53,468 views