With the prospect of a smart society in the foreseeable future, humans are experiencing an increased link to electronics in the digital world, which can benefit our life and productivity drastically. In recent decades, advances in the Human Machine Interface (HMI) have improved from tactile sensors, such as touchpads and joysticks, to now include the accurate detection of dexterous body movements in more diversified and sophisticated devices. Advancements in highly adaptive machine learning techniques, neural interfaces, and neuromorphic sensing have generated the potential for an economic and feasible solution for next-generation applications such as wearable HMIs with intimate and multi-directional sensing capabilities. This review offers a general knowledge of HMI technologies beginning with tactile sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on how machine learning, neural interfaces, and neuromorphic electronics can be used to enhance next-generation HMIs in an upcoming 5 G infrastructure and advancements in the internet of things and artificial intelligence of things in the near future. The efficient interactions with kinetic and physiological signals from human body through the fusion of tactile sensor and neural electronics will bring a revolution to both the advanced manipulation and medical rehabilitation.

Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human–machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human–machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human–machine interface in medical robotics. 

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Flexible Electronics and Devices as Human–Machine Interfaces for Medical Robotics

Soft electronics are rising electronic technologies towards applications spanning from healthcare monitoring to medical implants. However, poor adhesion strength and significant mechanical mismatches inevitably cause the interface failure of devices. Herein we report a self-adhesive conductive polymer that possesses low modulus (56.1-401.9 kPa), high stretchability (700%), high interfacial adhesion (lap-shear strength >1.2 MPa), and high conductivity (1-37 S/cm). The self-adhesive conductive polymer is fabricated by doping the poly(3,4-ethylenedioxythiophene): poly(styrene sulfonate) composite with a supramolecular solvent (β-cyclodextrin and citric acid). We demonstrated the solution process-based fabrication of self-adhesive conductive polymer-based electrodes for various soft devices, including alternating current electroluminescent devices, electromyography monitoring, and an integrated system for the visualization of electromyography signals during muscle training with an array of alternating current electroluminescent devices. The self-adhesive conductive polymer-based electronics show promising features to further develop wearable and comfortable bioelectronic devices with the physiological electric signals of the human body readable and displayable during daily activities. 

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Solution-processable, soft, self-adhesive, and conductive polymer composites for soft electronics

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Application of intrinsically conducting polymers in flexible electronics

Devices such as keyboards and touchscreens allow humans to communicate with machines. Neural interfaces, which can provide a direct, electrical bridge between analogue nervous systems and digital man-made systems, could provide a more efficient route to future information exchange. Here we review the development of electronic neural interfaces. The interfaces typically consist of three modules — a tissue interface, a sensing interface, and a neural signal processing unit — and based on technical milestones in the development of the electronic sensing interface, we group and analyse the interfaces in four generations: the patch clamp technique, multi-channel neural interfaces, implantable/wearable neural interfaces and integrated neural interfaces. We also consider key circuit and system challenges in the design of neural interfaces and explore the opportunities that arise with the latest technology This Review Article examines the development of neural interfaces, which can provide a direct, electrical bridge between analogue human nervous systems and digital man-made devices, considering challenges and opportunities created with such technology.

Electronic neural interfaces

This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG). A bias-driven pruning method is proposed together with group convolution, global average pooling (GAP), near-zero pruning, weight clustering and quantization for the model compression, achieving a total compression ratio of 183. 11x. The compressed SaleNet obtains a state-of-the-art subject-independent sustained attention level classification accuracy of 84.2% on the recorded 6-subject EEG database in this work. The SaleNet is implemented on a Artix-7 FPGA with a competitive power consumption of 0.11 W and an energy-efficiency of 8.19 GOps/w.

SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG

This paper proposed a miniature neural interface system. A single chip neural recording SoC was fabricated in 40nm CMOS process with an area of 3mm×3mm. It integrated a 16-channel analog front end (AFE), and a low power constant envelope polar transmitter. The general form of continuous phase modulation was used as the modulation scheme. Algorithms for receiver including frequency offset calibration, frame synchronization, and symbol demodulation were proposed and implemented on a software-defined radio platform. Simulation results showed that a bit error rate of $10^{-4}$ is achieved at the signal to noise ratio of 19 dB at high data rate mode of 971.4 kbps. A graphic user interface was designed for channel decoding and real-time display. Experimental results showed that the input referred noise of the AFE is 2.87$\mu V_{rms}$, and the energy efficiency of the transmitter is 2. 8nJ/bit. The proposed chip consumes 5. 47mW power in total in its maximum workload. The neural signal can be correctly decoded at least at a RSSI (Received Signal Strength Indicator) of -95dBm, and a working distance of 8 m. In-vivo tests on rat have been conducted, showing a good usability of the proposed system.

A 16-Channel Neural Recorder with 2.8 nJ/bit, 971.4 kbps sub-2.4 GHz polar transmitter

This paper proposed a fully integrated MBAN (2360–2400 MHz) continuous phase modulated transmitter (TX) with tunable less than 0dBm output power for medical band. A duty-cycle tuning strategy was proposed for the power amplifier (PA) featuring adaptive optimized efficiency for different output powers. A fully on-chip transformer-based match network was proposed to suppress the 2nd harmonic using a series $LC$ resonator and to suppress the 3rd harmonic by introducing a transformer inter-winding capacitor feedback path. A fractional-N all-digital phase locked loop (ADPLL) with a transformer-based digitally controlled oscillator (DCO) is employed to reduce power consumption as well as improve modulation quality. The transmitter was fabricated in 40-nm CMOS technology, occupying an active area of 0.48mm2. Experimental results show a 26% drain efficiency with −10dBm PA output and 4dB tunable range. A 2mW total power consumption was measured with a TX efficiency of 5% and an energy efficiency of 2nJ/bit. The measured 2nd and 3rd harmonic distortion of the output were −44.3dBm and −57.2dBm, respectively, with on-chip matching network. The measured FSK error of CPM was 2.3% with an M of 2 and 1.57% with an M of 4.

Zijian Tang

Papers

Electronic neural interfaces

SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG

A 16-Channel Neural Recorder with 2.8 nJ/bit, 971.4 kbps sub-2.4 GHz polar transmitter

A 2 nJ/bit, 2.3% FSK Error Fully Integrated Sub-2.4 GHz Transmitter With Duty-Cycle Controlled PA for Medical Band