Jae Keun Lee

Bio: Jae Keun Lee is an academic researcher. The author has contributed to research in topics: Inertial measurement unit & Etching (microfabrication). The author has an hindex of 3, co-authored 3 publications receiving 17 citations.

A Novel Top-Down Fabrication Process for Vertically-Stacked Silicon-Nanowire Array

Abstract: Silicon nanowires are widely used for sensing applications due to their outstanding mechanical, electrical, and optical properties. However, one of the major challenges involves introducing silicon-nanowire arrays to a specific layout location with reproducible and controllable dimensions. Indeed, for integration with microscale structures and circuits, a monolithic wafer-level process based on a top-down silicon-nanowire array fabrication method is essential. For sensors in various electromechanical and photoelectric applications, the need for silicon nanowires (as a functional building block) is increasing, and thus monolithic integration is highly required. In this paper, a novel top-down method for fabricating vertically-stacked silicon-nanowire arrays is presented. This method enables the fabrication of lateral silicon-nanowire arrays in a vertical direction, as well as the fabrication of an increased number of silicon nanowires on a finite dimension. The proposed fabrication method uses a number of processes: photolithography, deep reactive-ion etching, and wet oxidation. In applying the proposed method, a vertically-aligned silicon-nanowire array, in which a single layer consists of three vertical layers with 20 silicon nanowires, is fabricated and analyzed. The diamond-shaped cross-sectional dimension of a single silicon nanowire is approximately 300 nm in width and 20 μm in length. The developed method is expected to result in highly-sensitive, reproducible, and low-cost silicon-nanowire sensors for various biomedical applications.

Wireless Epidermal Six-Axis Inertial Measurement Units for Real-Time Joint Angle Estimation

Abstract: Technological advances in wireless communications, miniaturized sensors, and low-power electronics have made it possible to implement integrated wireless body area networks (WBANs). These developments enable the applications of wireless wearable systems for diagnosis, health monitoring, rehabilitation, and dependency care. Across the current range of commercial wearable devices, the products are not firmly fixed to the human body. To minimize data error caused by movement of the human body and to achieve accurate measurements, it is essential to bring the wearable device close to the skin. This paper presents the implementation of a patch-type, six-axis inertial measurement unit (IMU) with wireless communication technology. The device comprises hard-electronic components on a stretchable elastic substrate for application in epidermal electronics, to collect precise data from the human body. Instead of the commonly used cleanroom processes of implementing devices on a stretchable substrate, a simple and inexpensive “cut-solder-paste” method is adopted to fabricate complex, convoluted interconnections. Thus, the signal distortions in the proposed device can be minimized during various physical activities and skin deformations when used in gait analysis. The inertial sensor data measured from the motion of the body can be sent in real-time via Bluetooth to any processing unit enabled with such a widespread standard wireless interface. For performance evaluation, the implemented device is mounted on a rotation plate in order to compare performance with the conventional product. In addition, an experiment on joint angle estimation is performed by attaching the device to an actual human body. The preliminary results of the device indicate the potential to monitor people in remote settings for applications in mobile health, human-computer interfaces (HCIs), and wearable robots.

Stretchable, Patch-Type, Wireless, 6-axis Inertial Measurement Unit for Mobile Health Monitoring

Abstract: Wearable devices which measure and transfer signals from the human body can provide useful biometric data for various biomedical applications. In this paper, we present an implementation of the advanced Inertial Measurement Unit (IMU) with wireless communication technology for mobile health monitoring. The device consists of rigid silicon-based components on a flexible/stretchable substrate for applications in epidermal electronic devices to collect precise data from the human body. Using the Bluetooth Low Energy (BLE) System-on-a-chip (SoC), the device can be miniaturized and portable, and the collected data can be processed with low power consumption. The dimensions of the implemented system are approximately 40 mm × 40 mm × 100 m. Also, the device can be attached closely to human skin, which results in minimized signal distortion due to body movements or skin deformations. In order to achieve device flexibility and stretch ability, the interconnection wires are designed as serpentine-shaped structures on a stretchable substrate. The previously reported “cut-and-paste” method is utilized to fabricate the device that produces complex, twisty interconnections with thin metal sheets. The implemented patch-type, wireless, 6-axis IMU is expected to have potential in various applications, such as health monitoring, dependency care, and daily lifelogging.

Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis

Abstract: The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields.

Combinatorial Strategy for Studying Biochemical Pathways in Double Emulsion Templated Cell-Sized Compartments.

Abstract: Cells rely upon producing enzymes at precise rates and stoichiometry for maximizing functionalities. The reasons for this optimal control are unknown, primarily because of the interconnectivity of the enzymatic cascade effects within multi-step pathways. Here, an elegant strategy for studying such behavior, by controlling segregation/combination of enzymes/metabolites in synthetic cell-sized compartments, while preserving vital cellular elements is presented. Therefore, compartments shaped into polymer GUVs are developed, producing via high-precision double-emulsion microfluidics that enable: i) tight control over the absolute and relative enzymatic contents inside the GUVs, reaching nearly 100% encapsulation and co-encapsulation efficiencies, and ii) functional reconstitution of biopores and membrane proteins in the GUVs polymeric membrane, thus supporting in situ reactions. GUVs equipped with biopores/membrane proteins and loaded with one or more enzymes are arranged in a variety of combinations that allow the study of a three-step cascade in multiple topologies. Due to the spatiotemporal control provided, optimum conditions for decreasing the accumulation of inhibitors are unveiled, and benefited from reactive intermediates to maximize the overall cascade efficiency in compartments. The non-system-specific feature of the novel strategy makes this system an ideal candidate for the development of new synthetic routes as well as for screening natural and more complex pathways.

Structuring of Si into Multiple Scales by Metal-Assisted Chemical Etching

Abstract: Structuring Si, ranging from nanoscale to macroscale feature dimensions, is essential for many applications. Metal-assisted chemical etching (MaCE) has been developed as a simple, low-cost, and scalable method to produce structures across widely different dimensions. The process involves various parameters, such as catalyst, substrate doping type and level, crystallography, etchant formulation, and etch additives. Careful optimization of these parameters is the key to the successful fabrication of Si structures. In this review, recent additions to the MaCE process are presented after a brief introduction to the fundamental principles involved in MaCE. In particular, the bulk-scale structuring of Si by MaCE is summarized and critically discussed with application examples. Various approaches for effective mass transport schemes are introduced and discussed. Further, the fine control of etch directionality and uniformity, and the suppression of unwanted side etching are also discussed. Known application examples of Si macrostructures fabricated by MaCE, though limited thus far, are presented. There are significant opportunities for the application of macroscale Si structures in different fields, such as microfluidics, micro-total analysis systems, and microelectromechanical systems, etc. Thus more research is necessary on macroscale MaCE of Si and their applications.

Hand Gesture Recognition Using Single Patchable Six-Axis Inertial Measurement Unit via Recurrent Neural Networks

Abstract: Recording human gestures from a wearable sensor produces valuable information to implement control gestures or in healthcare services. The wearable sensor is required to be small and easily worn. Advances in miniaturized sensor and materials research produces patchable inertial measurement units (IMUs). In this paper, a hand gesture recognition system using a single patchable six-axis IMU attached at the wrist via recurrent neural networks (RNN) is presented. The IMU comprises IC-based electronic components on a stretchable, adhesive substrate with serpentine-structured interconnections. The proposed patchable IMU with soft form-factors can be worn in close contact with the human body, comfortably adapting to skin deformations. Thus, signal distortion (i.e., motion artifacts) produced for vibration during the motion is minimized. Also, our patchable IMU has a wireless communication (i.e., Bluetooth) module to continuously send the sensed signals to any processing device. Our hand gesture recognition system was evaluated, attaching the proposed patchable six-axis IMU on the right wrist of five people to recognize three hand gestures using two models based on recurrent neural nets. The RNN-based models are trained and validated using a public database. The preliminary results show that our proposed patchable IMU have potential to continuously monitor people’s motions in remote settings for applications in mobile health, human–computer interaction, and control gestures recognition.