This review presents the adsorptive removal process of hazardous materials onto carbon-based materials comprising activated carbon, graphene, carbon nanotubes, carbon nanofibers, biochar and carbon aerogels.

This paper presents a framework based on multi-layer bi-LSTM network (bidirectional Long Short-Term Memory) for multimodal sensor fusion to sense and classify daily activities’ patterns and high-risk events such as falls. The data collected in this work are continuous activity streams from FMCW radar and three wearable inertial sensors on the wrist, waist, and ankle. Each activity has a variable duration in the data stream so that the transitions between activities can happen at random times within the stream, without resorting to conventional fixed-duration snapshots. The proposed bi-LSTM implements soft feature fusion between wearable sensors and radar data, as well as two robust hard-fusion methods using the confusion matrices of both sensors. A novel hybrid fusion scheme is then proposed to combine soft and hard fusion to push the classification performances to approximately 96% accuracy in identifying continuous activities and fall events. These fusion schemes implemented with the proposed bi-LSTM network are compared with conventional sliding window approach, and all are validated with realistic “leaving one participant out” (L1PO) method (i.e. testing subjects unknown to the classifier). The developed hybrid-fusion approach is capable of stabilizing the classification performance among different participants in terms of reducing accuracy variance of up to 18.1% and increasing minimum, worst-case accuracy up to 16.2%.

Implantable technologies are becoming more widespread for biomedical applications that include physical identification, health diagnosis, monitoring, recording, and treatment of human physiological traits. However, energy harvesting and power generation beneath the human tissue are still a major challenge. In this regard, self‐powered implantable devices that scavenge energy from the human body are attractive for long‐term monitoring of human physiological traits. Thanks to advancements in material science and nanotechnology, energy harvesting techniques that rely on piezoelectricity, thermoelectricity, biofuel, and radio frequency power transfer are emerging. However, all these techniques suffer from limitations that include low power output, bulky size, or low efficiency. Photovoltaic (PV) energy conversion is one of the most promising candidates for implantable applications due to their higher‐power conversion efficiencies and small footprint. Herein, the latest implantable energy harvesting technologies are surveyed. A comparison between the different state‐of‐the‐art power harvesting methods is also provided. Finally, recommendations are provided regarding the feasibility of PV cells as an in vivo energy harvester, with an emphasis on skin penetration, fabrication, encapsulation, durability, biocompatibility, and power management.

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Flexible electronics has huge potential to bring revolution in robotics and prosthetics as well as to bring about the next big evolution in electronics industry. In robotics and related applications, it is expected to revolutionise the way with which machines interact with humans, real-world objects and the environment. For example, the conformable electronic or tactile skin on robot’s body, enabled by advances in flexible electronics, will allow safe robotic interaction during physical contact of robot with various objects. Developing a conformable, bendable and stretchable electronic system requires distributing electronics over large non-planar surfaces and movable components. The current research focus in this direction is marked by the use of novel materials or by the smart engineering of the traditional materials to develop new sensors, electronics on substrates that can be wrapped around curved surfaces. Attempts are being made to achieve flexibility/stretchability in e-skin while retaining a reliable operation. This review provides insight into various materials that have been used in the development of flexible electronics primarily for e-skin applications.

Implantable neural interfacing devices have added significantly to neural engineering by introducing the low-frequency oscillations of small populations of neurons known as local field potential as well as high-frequency action potentials of individual neurons. Regardless of the astounding progression as of late, conventional neural modulating system is still incapable to achieve the desired chronic in vivo implantation. The real constraint emerges from mechanical and physical differences between implants and brain tissue that initiates an inflammatory reaction and glial scar formation that reduces the recording and stimulation quality. Furthermore, traditional strategies consisting of rigid and tethered neural devices cause substantial tissue damage and impede the natural behavior of an animal, thus hindering chronic in vivo measurements. Therefore, enabling fully implantable neural devices requires biocompatibility, wireless power/data capability, biointegration using thin and flexible electronics, and chronic recording properties. This article reviews biocompatibility and design approaches for developing biointegrated and wirelessly powered implantable neural devices in animals aimed at long-term neural interfacing and outlines current challenges toward developing the next generation of implantable neural devices.