This paper presents a low-power ECG recording system-on-chip (SoC) with on-chip low-complexity lossless ECG compression for data reduction in wireless/ambulatory ECG sensor devices. The chip uses a linear slope predictor for data compression, and incorporates a novel low-complexity dynamic coding-packaging scheme to frame the prediction error into fixed-length 16 bit format. The proposed technique achieves an average compression ratio of 2.25× on MIT/BIH ECG database. Implemented in a standard 0.35 µm process, the compressor uses 0.565 K gates/channel occupying 0.4 mm for four channels, and consumes 535 nW/channel at 2.4 V for ECG sampled at 512 Hz. Small size and ultra-low-power consumption makes the proposed technique suitable for wearable ECG sensor applications.

As 5G communication technology allows for speedier access to extended information and knowledge, a more sophisticated human−machine interface beyond touchscreens and keyboards is necessary to improve the communication bandwidth and overcome the interfacing barrier. However, the full extent of human interaction beyond operation dexterity, spatial awareness, sensory feedback, and collaborative capability to be replicated completely remains a challenge. Here, we demonstrate a hybridflexible wearable system, consisting of simple bimodal capacitive sensors and a customized low power interface circuit integrated with machine learning algorithms, to accurately recognize complex gestures. The 16 channel sensor array extracts spatial and temporal information of the finger movement (deformation) and hand location (proximity) simultaneously. Using machine learning, over 99 and 91% accuracy are achieved for user-independent static and dynamic gesture recognition, respectively. Our approach proves that an extremely simple bimodal sensing platform that identifies local interactions and perceives spatial context concurrently, is crucial in the field of sign communication, remote robotics, and smart manufacturing.

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This paper presents a low power ECG recording System-on-Chip (SoC) with on-chip low complexity lossless ECG compression for data reduction in wireless/ambulatory ECG sensor devices. The proposed algorithm uses a linear slope predictor to estimate the ECG samples, and uses a novel low complexity dynamic coding-packaging scheme to frame the resulting estimation error into fixed-length 16-bit format. The proposed technique achieves an average compression ratio of 2.25x on MIT/BIH ECG database. Implemented in 0.35 m process, the compressor uses 0.565K gates/channel occupying 0.4 mm 2 for 4channel, and consumes 535nW/channel at 2.4V for ECG sampled at 512 Hz. Small size and ultra-low power consumption makes the proposed technique suitable for wearable ECG sensor application.