An Ultra-Low-Power Area-Efficient Non-Coherent Binary Phase-Shift Keying Demodulator for Implantable Biomedical Microsystems

A novel non-coherent, low-power, area-efficient binary phase-shift keying demodulator for wireless implantable biomedical microsystems is proposed. The received data and synchronized clock signal are detected using a delayed digitized format of the input signal. The proposed technique does not require any kind of oscillator circuit, and due to the synchronization of all circuit signals, the proposed demodulator can work in a wide range of biomedical data telemetry common frequencies in different process/temperature corners. The presented circuit has been designed and post-layout-simulated in a standard 0.18 μm CMOS technology and occupies 17 × 27 μm2 of active area. Post-layout simulation results indicate that with a 1.8 V power supply, power consumption of the designed circuit is 8.5 μW at a data rate of 20 Mbps. The presented demodulation scheme was also implemented on a proof-of-concept circuit board for verifying its functionality.

A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost (∼150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly ( p < 0.05 ) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository.