IEEE Access Special Section Editorial: Wearable and Implantable Devices and Systems

© 2013 IEEE. Circuit techniques, sensors, antennas and communications systems are envisioned to help build new technologies over the next several years. Advances in the development and implementation of such technologies have already shown us their unique potential in realizing next-generation sensing systems. Applications include wearable consumer electronics, healthcare monitoring systems, and soft robotics, as well as wireless implants. There have been some interesting developments in the areas of circuits and systems, involving studies related to low-power electronics, wireless sensor networks, wearable circuit behaviour, security, real-time monitoring, connectivity of sensors, and Internet of Things (IoT). The direction for the current technology is electronics systems on large area electronics, integrated implantable systems and wearable sensors. So far, the research in the field has focused on materials, new processing techniques and one-off devices, such as diodes and transistors. However, current technology is not sufficient for future electronics to be useful in new applications; a great demand exists to scale up the research towards circuits and systems. Recent developments indicate that, in addition to fabrication technology, special attention should also be given to design, simulation and modeling of electronics, while keeping sensing system integration, power management, and sensors network under consideration

Teaching embedded systems for energy harvesting applications: a comparison of teaching methods adopted in UESTC and KTH

Further to China’s plan that was introduced in 2017 for attracting more students into engineering, many Chinese universities have started to explore new teaching methods that can be adopted into their programs. This shift was geared towards developing student-centred teaching materials rather than traditional teacher centred instruction. In this manuscript, we compare two different methods of instruction for a course on energy harvesting using embedded systems. We describe the learning materials and showcase the impact that project-based learning has had on a cohort of Chinese students that were enrolled in a joint master’s program between the University of Electronic Science and Technology of China (UESTC) and the Royal Institute of Technology (KTH). KTH has made remarkable progress in the teaching of embedded systems technology for energy harvesting applications, with great emphasis on active as well as collaborative learning. We demonstrate two examples of projects that Chinese students have completed in KTH and present evaluative data regarding their experiences. Our results show that KTH’s approach in teaching this module has had a positive impact on student learning, with an average of 80% of students think that teaching in KTH is conducive to students’ independent exploration

High-precision adaptive slope compensation circuit for DC-DC converter in wearable devices

This paper presents a high precision adaptive slope compensation circuit for for DC-DC converter in wearable devices. Compared with the traditional adaptive slope compensation circuit, the comparator is used to sample the output voltage and input voltage, which greatly improves the accuracy.In this paper, the circuit is designed in UMC 0.18-μm CMOS Technology and verified by Virtuoso Spectre Circuit Simulator. The simulation results show that the accuracy of the adaptive slope compensation circuit in this paper can reach more than 96%

Simulation of crystalline silicon photovoltaic cells for wearable applications

Advancements in the semiconductor industry have enabled wearable devices to be used for a wide range of applications, including personalised healthcare. Novel energy harvesting technologies are therefore necessary to ensure that these devices can be used without interruption. Crystalline silicon photovoltaic cells provide high energy density to electronic loads. However, the optimization of these cells is a complex task since the optical performance is coupled to the surroundings, and the electrical performance is influenced by the intrinsic PV characteristics and parasitic losses.Without doubt, accurate simulation tools can provide the necessary insight to PV cell performance before device fabrication. However, the majority of these tools require expensive licensing fees. Thus, the aim of this article is to review the range of non-commercial PV simulation tools that can be used for wearable applications. We provide a detailed procedure for device modelling and we compare the performance of these tools with previously published experimental data, as well as commercial software. According to our findings, non-commercial 3D simulation tools such as PC3D provide accurate results, with only a relative error of ≈2.2% in Jsc after setting off the difference in geometrical modelling due to the software limit

An intelligent implementation of multi-sensing data fusion with neuromorphic computing for human activity recognition

The increasing demand for considering multi-sensor data fusion technology has drawn attention for precise human activity recognition over standalone technology due to its reliability and robustness. This paper presents a framework that fuses data from multiple sensing systems and applies Neuromorphic computing to sense and classify human activities. The data is collected by utilizing Inertial Measurement Unit (IMU) sensors, software-defined radios, and radars and feature extraction and selection are performed on the data. For each of the actions, such as sitting and standing, an activity matrix is generated, which is then fed into a discrete Hopfield neural network as a binary feature pattern for one-shot learning. Following the Hopfield network neurons’ feedback output, the conformity to the standard activity feature pattern is also determined. Following the Hopfield network neurons’ feedback output, the training of neurons is completed after 2 steps under the Hebbian learning law, and the conformity to the standard activity feature pattern is also determined. According to probabilistic statistics on inference predictions, the proposed method that Neuromorphic computing of the three data fused framework achieved the Box-plot for highest lower quartile output of 95.34%, while the confusion matrix classification accuracy of the two activities was 98.98%. The results have shown that Neuromorphic computing is most capable for multi-sensor data fusion-based human activity recognition. Furthermore, the proposed method can be enhanced by incorporating additional hardware signal processing in the system to enable the flexible integration of human activity data

Spread Spectrum Buck Converter

Electromagnetic interference (EMI) is an issue prevalent to DC-DC converters. When a system doesn’t effectively filter out external noise or signals, these signals can cause disturbances to the system at large. The switching technology of DC-DC converters (PWM in particular), lends the system susceptible to EMI because there is a prevalent peaks at the switching frequency, meaning any external signals will not be effectively attenuated at this frequency. This can cause significant issues at the input bus of the DC-DC converters because this bus is likely the input of a multitude of devices; the EMI susceptibility caused by switching technology makes the entire system vulnerable. There are many proposed solutions to mitigate EMI, but our project focuses on spread spectrum frequency modulation (SSFM). SSFM is a way to utilize PWM technology by randomly varying the switching frequency within a set range of 10-20% centered at the desired average switching frequency; this served to eliminate harsh and potentially disastrous peaks at the switching frequency. Our project successfully implemented the spread spectrum technology of the LT8609 IC by using the IC in a 24/12V buck converter. We were able to clearly observe the frequency spectrum with the rectangular behavior characteristic of SSFM. The measured results were even better than the simulated results and our converter has made us confident in the viability of spread spectrum technology as a means to reduce EMI in DC-DC converters

Developing Reactive Distributed Aerial Robotics Platforms for Real-time Contaminant Mapping

The focus of this research is to design a sensor data aggregation system and centralized sensor-driven trajectory planning algorithm for fixed-wing aircraft to optimally assist atmospheric simulators in mapping the local environment in real-time. The proposed application of this work is to be used in the event of a hazardous contaminant leak into the atmosphere as a fleet of sensing unmanned aerial vehicles (UAVs) could provide valuable information for evacuation measures. The data aggregation system was designed using a state-of-the-art networking protocol and radio with DigiMesh and a process/data management system in the ROS2 DDS. This system was tested to consistently operate within the latencies and distances tolerated for the project while being highly extensible to sensor configurations. The problem of creating optimal trajectory planning for exploration has been modelled accurately using partially-observable Markov decision processes (POMDP). Deep Reinforcement learning (DRL) is commonly applied to approximate optimal solutions within a POMDP as it can be analytically intractable for complex state spaces. This research produces a POMDP that describes this exploration problem and applies the state-of-the-art soft actor-critic (SAC) reinforcement learning algorithm to create a policy that produces near-optimal trajectories within this new POMDP. A subset of the spatially relevant inputis used instead of complete state during training and a turn-taking sequential planner is designed for using multiple UAVs to help mitigate scalability problems that come with multi-UAV coordination. The learned policy from SAC can outperform a greedy and fixed trajectory on 1, 2, and 3 UAVs by a 30% margin on average. The turn-taking strategy provides small, but repeatable scaling benefits while the windowed input results in a 50%-60% increase in reward versus trained networks without windowed input. The proposed planning algorithm is effective in dynamic map exploration and has the potential to increase UAV effectiveness in atmospheric contaminant leak monitoring as it is expanded to be integrated on real-world UAVs

Embedded Artificial Intelligence for Tactile Sensing

Electronic tactile sensing becomes an active research field whether for prosthetic applications, robotics, virtual reality or post stroke patients rehabilitation. To achieve such sensing, an array of sensors is used to retrieve human-skin like information, which is called Electronic skin (E-skin). Humans through their skins, are able to collect different types of information e.g. pressure, temperature, texture, etc. which are then passed to the nervous system, and finally to the brain in order to extract high level information from these sensory data. In order to make E-skin capable of such task, data acquired from E-skin should be filtered, processed, and then conveyed to the user (or robot). Processing these sensory information, should occur in real-time, taking in consideration the power limitation in such applications, especially prosthetic applications. The power consumption itself is related to different factors, one factor is the complexity of the algorithm e.g. number of FLOPs, and another is the memory consumption. In this thesis, I will focus on the processing of real tactile information, by 1)exploring different algorithms and methods for tactile data classification, 2)data organization and preprocessing of such tactile data and 3)hardware implementation. More precisely the focus will be on deep learning algorithms for tactile data processing mainly CNNs and RNNs, with energy-efficient embedded implementations. The proposed solution has proved less memory, FLOPs, and latency compared to the state of art (including tensorial SVM), applied to real tactile sensors data. Keywords: E-skin, tactile data processing, deep learning, CNN, RNN, LSTM, GRU, embedded, energy-efficient algorithms, edge computing, artificial intelligence

From the Ground Up: Designerly Knowledge in Human-Drone Interaction

There are flying robots out there — you may have seen and heard them, droning over your head. Drones have expanded our human capacities, lifting our sight to the skies, but not without generating intricate experiences. How are these machines being designed and researched? What design methods, approaches, and philosophies are relevant to the study of the development (or decline) of drones in society? In this thesis, I argue that we must re-frame how drones are studied, from the ground up, through a design stance. I invite you to take a journey with me, with changing lenses from the work of others to my own intimate relationship with this technology. My work relies on exploring the fringes of design research: understudied groups such as children, alternative design approaches such as soma design, and peripheral methods such as autoethnography.This thesis includes four articles discussing perspectives on designerly knowledge, composing a frame surrounding the notion that we may be missing out on some of the aspects of the wicked nature of human-drone interaction (HDI) design. The methods are poised on phenomenology and narratives, and supported by the assumption that any subject of study is a sociotechnical assemblage. Starting through a first-person perspective, I offer a contribution to the gap in research through a longitudinal autoethnographic study conducted with my children. The second paper comes in the form of a pictorial expressing a first-person experience during a design research workshop, and what that meant for my relationship with drones as a research material. The third paper leaps into a Research through Design project, challenging the solutionist drone and offering instead the first steps in a concept-driven design of the unlikely pairing of drones and breathing. The fourth paper returns to the pictorial form, suggesting a method for visual conversations between researchers through the tangible qualities of sketches and illustrations. Central to this thesis, is the argument for designerly approaches in HDI and championing the need for alternative forms of publication and research. To that end, I include two publications in the form of pictorials: a publication format relying on visual knowledge and with growing interest in the HCI community

Lane detection in autonomous vehicles : A systematic review

One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-Alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-The-Art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane