Wearable Capacitive-based Wrist-worn Gesture Sensing System

Gesture control plays an increasingly significant role in modern human-machine interactions. This paper presents an innovative method of gesture recognition using flexible capacitive pressure sensor attached on user’s wrist towards computer vision and connecting senses on fingers. The method is based on the pressure variations around the wrist when the gesture changes. Flexible and ultrathin capacitive pressure sensors are deployed to capture the pressure variations. The embedding of sensors on a flexible substrate and obtain the relevant capacitance require a reliable approach based on a microcontroller to measure a small change of capacitive sensor. This paper is addressing these challenges, collect and process the measured capacitance values through a developed programming on LabVIEW to reconstruct the gesture on computer. Compared to the conventional approaches, the wrist-worn sensing method offerings a low-cost, lightweight and wearable prototype on the user’s body. The experimental result shows that the potentiality and benefits of this approach and confirms that accuracy and number of recognizable gestures can be improved by increasing number of sensor

Wireless Power Transfer for 3D Printed Unmanned Aerial Vehicle (UAV) Systems

Unmanned aerial vehicles (UAVs) have attracted a lot of attention for various applications such as service delivery, pollution mitigation, farming, and rescue operations over the past few years. However, the short duration of battery and the inconvenience of changing it is always a problem. Basically, small UAVs can only carry very limited payloads otherwise the battery will be drained more frequently. This project presents an automatic and high-efficient wireless power transfer (WPT) to supply a 3D printed UAV. A UAV has been 3D printed with wireless power transfer kit implemented to charge 3S 1500 mAh Li-Po battery with up to 1000 mAh automatically once it is landed, without manual operation. 24V DC is supplied to the transmitting side of WPT with the operating frequency at 180kHz and once the battery is fully charged, the charging process will also stop automatically

Smart Multi-Sensory Ball for Water Quality Monitoring

Electronic sensors and wireless communications have enabled a long-distance and real-time monitoring of water quality. In this paper, we present a smart multi-sensory device, remotely measuring and monitoring physical parameters of the water in real time. The proposed device is a 10 cm-diameter enclosure, consisting of an embedded battery, a voltage regulator, an Inertial Measurement Unit (IMU), and a communication chip with the 3D-printing cases. This smart multi-sensory enclosure or smart "ball" can successfully communicate with a personal computer in the real-time via wireless communication. Finally, the collected data can be directly displayed and post-processed to show real-time changes in the parameters

Design and Implementation of a 3D Printed Sensory Ball for Wireless Water Flow Monitoring

Sensor networks can detect and communicate information regarding the ambient environment using wireless and real-time methods. Consequently, sensor node design is of critical importance for monitoring water quality. This paper describes the design, fabrication and implementation process of a 3D-printed sensory ball that can remotely collect water flow parameters in real-time. A sensory ball that is 10-cm in diameter was used to measure water flow parameters. Data was then captured in real time and sent to a personal computer via wireless communications. Discussions regarding alternative applications of this device are provided in this manuscript

Design and Implementation of Portable Sensory System for Air Pollution Monitoring

Air pollution is becoming an increasingly serious issue, leading to many environmental problems such as the fog-haze weather phenomenon, which can cause great harm to human health. This paper focuses on the design and fabrication of a portable sensory system for air pollution monitoring, which can detect the temperature, humidity and particulate matter (PM). This will be used as a tool to help reduce the harm of air pollution on people. This sensor mainly consists of a microprogrammed control unit, a temperature & humidity sensor DHT11, a dust sensor GP2Y1010AU0F, LCD, keys and, LEDs. Ambient dust concentrations, temperature and humidity values will be displayed on the LCD. The corresponding light alert signals and sound alert signals are sent when the measured values are beyond their safe ranges

Visual Hand Tracking on Depth Image Using 2-D Matched Filter

Hand detection has been the central attention of human-machine interaction in recent researches. In order to track hand accurately, traditional methods mostly involve using machine learning and other available libraries, which requires a lot of computational resource on data collection and processing. This paper presents a method of hand detection and tracking using depth image which can be conveniently and manageably applied in practice without the huge data analysis. This method is based on the two-dimensional matched filter in image processing to precisely locate the hand position through several underlying codes, cooperated with a Delta robot. Compared with other approaches, this method is comprehensible and time-saving, especially for single specific gesture detection and tracking. Additionally, it is friendly-programmed and can be used on variable platforms such as MATLAB and Python. The experiments show that this method can do fast hand tracking and improve accuracy by selecting the proper hand template and can be directly used in the applications of human-machine interaction. In order to evaluate the performance of gesture tracking, a recorded video on depth image model is used to test theoretical design, and a delta parallel robot is used to follow the moving hand by the proposed algorithm, which demonstrates the feasibility in practice

Wearable Wristworn Gesture Recognition Using Echo State Network

This paper presents a novel gesture sensing system for prosthetic limb control based on a pressure sensor array embedded in a wristband. The tendon movement which produces pressure change around the wrist can be detected by pressure sensors. A microcontroller is used to gather the data from the sensors, followed by transmitting the data into a computer. A user interface is developed in LabVIEW, which presents the value of each sensor and display the waveform in real-time. Moreover, the data pattern of each gesture varies from different users due to the non-uniform subtle tendon movement. To overcome this challenge, Echo State Network (ESN), a supervised learning network, is applied to the data for calibrating different users. The results of gesture recognition show that the ESN has a good performance in multiple dimensional classifications. For experimental data collected from six participants, the proposed system classifies five gestures with an accuracy of 87.3%

Power management using photovoltaic cells for implantable devices

This paper presents a novel inductor-less switched capacitor (SC) DC-DC converter, which generates simultaneous dual-output voltages for implantable electronic devices. Present dual output converters are limited to fixed ratio gain, which degrade conversion efficiency when the input voltage changes. The proposed power converter offers both step-up and step-down conversion with 4-phase reconfigurable logic. With an input voltage of 1 V provided by photovoltaic (PV) cells, the proposed converter achieves step-up, step-down and synchronised voltage conversions in four gain modes. These are 1.5 V and 0.5 V for Normal mode, 2 V and 1 V for High mode, 2 V for Double Boost mode, as well as 3 V and 2 V for Super Boost mode with the ripple variation of 14-59 mV. The converter circuit has been simulated in standard 0.18 μm CMOS technology and the results agree with state-of-the-art SC converters. However, our proposed monolithically integrated PV powered circuit achieves a conversion efficiency of 85.26% and provides extra flexibility in terms of gain, which is advantageous for future implantable applications that have a range of inputs. This research is therefore an important step in achieving truly autonomous implantable electronic devices

Wrist-worn gesture sensing with wearable intelligence

This paper presents an innovative wrist-worn device with machine learning capabilities and a wearable pressure sensor array. The device is used for monitoring different hand gestures by tracking tendon movements around the wrist. Thus, an array of PDMS-encapsulated capacitive pressure sensors is attached to the user to capture wrist movement. The sensors are embedded on a flexible substrate and their readout requires a reliable approach for measuring small changes in capacitance. This challenge was addressed by measuring the capacitance via the switched capacitor method. The values were processed using a programme on LabVIEW to visually reconstruct the gestures on a computer. Additionally, to overcome limitations of tendon’s uncertainty when the wristband is re-worn, or the user is changed, a calibration step based on the Support Vector Machine (SVM) learning technique is implemented. Sequential Minimal Optimization (SMO) algorithm is also applied in the system to generate SVM classifiers efficiently in real-time. The working principle and the performance of the SVM algorithms demonstrate through experiments. Three discriminated gestures have been clearly separated by SVM hyperplane and correctly classified with high accuracy (>90%) during real-time gesture recognition

Design and implementation of a multi-modal sensor with on-chip security

With the advancement of technology, wearable devices for fitness tracking, patient monitoring, diagnosis, and disease prevention are finding ways to be woven into modern world reality. CMOS sensors are known to be compact, with low power consumption, making them an inseparable part of wireless medical applications and Internet of Things (IoT). Digital/semi-digital output, by the translation of transmitting data into the frequency domain, takes advantages of both the analog and digital world. However, one of the most critical measures of communication, security, is ignored and not considered for fabrication of an integrated chip. With the advancement of Moore\u27s law and the possibility of having a higher number of transistors and more complex circuits, the feasibility of having on-chip security measures is drawing more attention. One of the fundamental means of secure communication is real-time encryption. Encryption/ciphering occurs when we encode a signal or data, and prevents unauthorized parties from reading or understanding this information. Encryption is the process of transmitting sensitive data securely and with privacy. This measure of security is essential since in biomedical devices, the attacker/hacker can endanger users of IoT or wearable sensors (e.g. attacks at implanted biosensors can cause fatal harm to the user). This work develops 1) A low power and compact multi-modal sensor that can measure temperature and impedance with a quasi-digital output and 2) a low power on-chip signal cipher for real-time data transfer