Low Complexity Binarized 2D-CNN Classifier for Wearable Edge AI Devices.

Wearable Artificial Intelligence-of-Things (AIoT) devices exhibit the need to be resource and energy-efficient. In this paper, we introduced a quantized multilayer perceptron (qMLP) for converting ECG signals to binary image, which can be combined with binary convolutional neural network (bCNN) for classification. We deploy our model into a low-power and low-resource field programmable gate array (FPGA) fabric.

An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices.

Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric.

Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition.

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 hybrid-flexible 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.

Continuous ECG Monitoring Trial for Outpatient - Patient Receptiveness and Signal Accuracy.

We have conducted a clinical trial to investigate the receptiveness and signal accuracy of our in-house light-weight single lead wearable wireless ECG device with 20 outpatients, who were suspected of cardiac rhythm issues. The receptiveness was measured via a survey score sheet while the signal accuracy was evaluated by comparing the Holter's hourly heart rate report (the gold-standard) against the ones from our device. In terms of receptiveness, a score of 8.

A 13-Channel 1.53-mW 11.28-mm Electrical Impedance Tomography SoC Based on Frequency Division Multiplexing for Lung Physiological Imaging.

An electrical impedance tomography (EIT) system based on frequency division multiplexing (FDM) is proposed for real-time lung physiological imaging. The FDM technique allows the integration of 13 dedicated voltage sensing channels by combining data on-chip and sharing of ADC to alleviate area penalty caused by multi-channel. The EIT system-on-chip (SoC) is of the following features.

A 74-μW 11-Mb/s Wireless Vital Signs Monitoring SoC for Three-Lead ECG, Respiration Rate, and Body Temperature.

This paper presents a wireless vital signs monitoring system-on-chip including three-lead ECG, bio-impedance (Bio-Z) and body temperature. A Bio-Z readout channel with early demodulation is introduced to detect small body impedance change below 300 mΩ under large baseline resistance while consuming power of 9.8 μW.

A Soft Polydimethylsiloxane Liquid Metal Interdigitated Capacitor Sensor and Its Integration in a Flexible Hybrid System for On-Body Respiratory Sensing.

We report on the dual mechanical and proximity sensing effect of soft-matter interdigitated (IDE) capacitor sensors, together with its modelling using finite element (FE) simulation to elucidate the sensing mechanism. The IDE capacitor is based on liquid-phase GaInSn alloy (Galinstan) embedded in a polydimethylsiloxane (PDMS) microfludics channel. The use of liquid-metal as a material for soft sensors allows theoretically infinite deformation without breaking electrical connections.

A Wearable, Patient-Adaptive Freezing of Gait Detection System for Biofeedback Cueing in Parkinson's Disease.

Freezing of Gait (FoG) is a common motor-related impairment among Parkinson's disease patients, which substantially reduces their quality of life and puts them at risk of falls. These patients benefit from wearable FoG detection systems that provide timely biofeedback cues and hence help them regain control over their gait. Unfortunately, the systems proposed thus far are bulky and obtrusive when worn.

Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values.

Background: Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable.

A 400 MHz Wireless Neural Signal Processing IC With 625 $\times$ On-Chip Data Reduction and Reconfigurable BFSK/QPSK Transmitter Based on Sequential Injection Locking.

An 8-channel wireless neural signal processing IC, which can perform real-time spike detection, alignment, and feature extraction, and wireless data transmission is proposed. A reconfigurable BFSK/QPSK transmitter (TX) at MICS/MedRadio band is incorporated to support different data rate requirement. By using an Exponential Component-Polynomial Component (EC-PC) spike processing unit with an incremental principal component analysis (IPCA) engine, the detection of neural spikes with poor SNR is possible while achieving 625× data reduction.

Broadband Energy Harvester Using Non-linear Polymer Spring and Electromagnetic/Triboelectric Hybrid Mechanism.

Over the years, several approaches have been devised to widen the operating bandwidth, but most of them can only be triggered at high accelerations. In this work, we investigate a broadband energy harvester based on combination of non-linear stiffening effect and multimodal energy harvesting to obtain high bandwidth over wide range of accelerations (0.1 g-2.

A Hybrid Data Compression Scheme for Power Reduction in Wireless Sensors for IoT.

This paper presents a novel data compression and transmission scheme for power reduction in Internet-of-Things (IoT) enabled wireless sensors. In the proposed scheme, data is compressed with both lossy and lossless techniques, so as to enable hybrid transmission mode, support adaptive data rate selection and save power in wireless transmission. Applying the method to electrocardiogram (ECG), the data is first compressed using a lossy compression technique with a high compression ratio (CR).

Large Scale Triboelectric Nanogenerator and Self-Powered Pressure Sensor Array Using Low Cost Roll-to-Roll UV Embossing.

Triboelectric nanogenerators (TENGs) have emerged as a potential solution for mechanical energy harvesting over conventional mechanisms such as piezoelectric and electromagnetic, due to easy fabrication, high efficiency and wider choice of materials. Traditional fabrication techniques used to realize TENGs involve plasma etching, soft lithography and nanoparticle deposition for higher performance. But lack of truly scalable fabrication processes still remains a critical challenge and bottleneck in the path of bringing TENGs to commercial production.

A 2.4 GHz ULP reconfigurable asymmetric transceiver for single-chip wireless neural recording IC.

This paper presents a 2.4 GHz ultra-low-power (ULP) reconfigurable asymmetric transceiver and demonstrates its application in wireless neural recording. Fabricated in 0.

Low-power ultrawideband wireless telemetry transceiver for medical sensor applications.

An integrated CMOS ultrawideband wireless telemetry transceiver for wearable and implantable medical sensor applications is reported in this letter. This high duty cycled, noncoherent transceiver supports scalable data rate up to 10 Mb/s with energy efficiency of 0.35 nJ/bit and 6.