Fei

Gesture recognition for interactive controllers using MEMS motion sensors

In this paper we present our work on real-time human gesture recognition for multimedia interactive controllers through the use of Microelectromechanical Systems (MEMS) 3 axes acceleration sensors. The changes of accelerations in three perpendicular directions due to different gesture motions are detected in real-time by 3-axes MEMS accelerometer embedded in a wireless micro sensing mote, which exports sensor data to a PC via Bluetooth protocol. In the data collection stage, in order to realize real-time recognition, an “auto-cut” algorithm was developed to gather the start and stop motions of an input gesture automatically. After comparing several different data processing methods, we chose Discrete Cosine Transforms (DCT) to reduce the dimension of the input gestures. Subsequently, a series of experiments were performed to analyze the influence of sensor sampling frequency and the number of dominant frequencies for various gestures, and then the best combination was selected for our recognition experiments. Finally, the Hidden Markov Model (HMM) was employed to achieve real-time gesture recognition. We have shown that the gesture recognition accuracy could reach 95.7% when 20 training samples of each gesture and 70 testing samples were used.

2D Human Gesture Tracking and Recognition by the Fusion of MEMS Inertial and Vision Sensors

In this paper, we present an algorithm for hand gesture tracking and recognition based on the integration of a custom-built microelectromechanical systems (MEMS)-based inertial sensor (or measurement unit) and a low resolution imaging (i.e., vision) sensor. We discuss the 2-D gesture recognition and tracking results here, but the algorithm can be extended to 3-D motion tracking and gesture recognition in the future. Essentially, this paper shows that inertial data sampled at 100 Hz and vision data at 5 frames/s could be fused by an extended Kalman filter, and used for accurate human hand gesture recognition and tracking. Since an inertial sensor is better at tracking rapid movements, while a vision sensor is more stable and accurate for tracking slow movements, a novel adaptive algorithm has been developed to adjust measurement noise covariance according to the measured accelerations and the angular rotation rates. The experimental results verify that the proposed method is capable of reducing the velocity error and position drift in an MEMS-based inertial sensor when aided by the vision sensor. Compensating for the time delay due to the visual data processing cycles, a moving average filter is applied to remove the high frequency noise and propagate the inertial signals. The reconstructed trajectories of the first 10 Arabic numerals are further recognized using dynamic time warping with a direct cosine transform for feature extraction, resulting in an accuracy of 92.3% and individual numeral recognition within 100 ms.

A fluttering-to-electrical energy transduction system for consumer electronics applications

This paper presents a novel type of energy transducer which converts ambient wind power into electrical energy based on electromagnetic induction principle. Different from traditional wind turbine generators, flexible belts were designed and demonstrated to harvesting energy using aerodynamically -induced fluttering. Essentially, airflows are used to drive a specifically designed belt to vibrate periodically during fluttering. Nd-Fe-B magnets and outer coils were implemented with the fluttering belt to induce current in the coils. The parameters of the coil are optimized for power output using a FEM tool. Experiments in controllable lab conditions were conducted to compare the performance of the prototype. We propose that such energy transducer can be used as an alternative or complementary power supply to batteries in low power consumer electronics applications.

Hand-Writing Motion Tracking with Vision-Inertial Sensor Fusion: Calibration and Error Correction

The purpose of this study was to improve the accuracy of real-time ego-motion tracking through inertial sensor and vision sensor fusion. Due to low sampling rates supported by web-based vision sensor and accumulation of errors in inertial sensors, ego-motion tracking with vision sensors is commonly afflicted by slow updating rates, while motion tracking with inertial sensor suffers from rapid deterioration in accuracy with time. This paper starts with a discussion of developed algorithms for calibrating two relative rotations of the system using only one reference image. Next, stochastic noises associated with the inertial sensor are identified using Allan Variance analysis, and modeled according to their characteristics. Finally, the proposed models are incorporated into an extended Kalman filter for inertial sensor and vision sensor fusion. Compared with results from conventional sensor fusion models, we have shown that ego-motion tracking can be greatly enhanced using the proposed error correction model.

Exploring pulse-voltage-triggered optically induced electrohydrodynamic instability for femtolitre droplet generation

We present a multipoint “virtual dispenser” to draw femtolitre droplets from a dielectric fluidic thin film using pulse-voltage-triggered optically induced electrohydrodynamic instability (PVT-OEHI). The “virtual dispenser” generates instability nucleation sites by controlling the optically induced lateral electrical stress and thermocapillary flow inside an optoelectronics chip. A time scale analysis shows that the electrohydrodynamic (EHD) instability phenomenon is present; however, its external manifestation is suppressed by OEHI. We observed two droplet dispensing mechanisms which correspond to different EHD states: Taylor cone formation and optically induced EHD jet. The EHD states transition could be realized by adjusting the pulse voltage parameters to alter the morphology of dispensed micron-scale polymer droplets, which could then be formed into organized arrays of microlenses with controllable diameter and curvature based on surface tension effect.

Theoretical analysis based on particle electro-mechanics for Au Pearl Chain Formation

This paper analyzes the fundamental mechanisms in driving Au pearl chain formation (PCF) based on dielectrophoresis (DEP) force. From experimental results, the PCF process strongly depends on the voltage and the frequency applied on electrodes, but weakly on the sizes of particles, which appears to be contrary to theoretical expectations. To explain the above phenomenon, we estimated the DEP force and the Brownian motion imposed on the Au nanoparticles, and then investigated the AC electro-osmosis force and the electro-thermal force which may possibly affect the PCF rate. Numerical modeling to compare the forces is presented. By matching experimental and numerical results, we validate the scaling laws of the DEP force and electro-mechanics in the PCF of Au nano-particles.

An indoor air duct flow energy conversion system: modeling and experiments

This paper presents an indoor, low-speed airflow, energy harvesting system based on aerodynamic flutter. Due to aerodynamic forces, mechanical vibrations occur when airflow passes across flexible belt-like structures. A linear electromagnetic generator has been designed to transfer this mechanical power into electricity based on Faradays law. Based on a model which couples the aerodynamic flutter with the electromagnetic generator, the output electrical power can be estimated and optimized. The airflow from an air duct with a 0.5m × 0.5m cross-sectional area is used to drive an energy conversion device in experiments. The experimental results show that this prototype flutter energy conversion device (FECD) could provide nearly 2VRMS voltage with a 2.5m/s airflow. A set of super capacitors is used as a temporary storage element. With a power management circuit, the entire energy harvesting device can operate as a stable 3.3V DC power source during a discharging cycle.

An indoor air duct flow energy conversion system for powering Wireless Sensors

In this paper, an indoor low-speed airflow energy harvesting system based on aerodynamic flutter is proposed to power up Wireless Sensor Networks (WSNs). Steady mechanical vibration always can be observed due to the aerodynamic forces while airflows pass through flexible belt-like structure. Oscillating electromagnetic resonator has been designed to covert mechanical power into electricity according Faradays law. Optimizations for both belt-flutter subsystem and electromagnetic resonator are carried out to maximize the conversion efficiency. The airflows from a 0.5m×0.5m square air conduct diffuser are used to stimulate the energy conversion system in experiments, which is widely installed in business office, warehouse and parking, etc. A prototype of flutter energy conversion device for indoor air conduct is demonstrated to power up a commercial wireless sensor. With the power management and super capacitors as buffer, those wireless nodes could transmit for several minutes periodically after charging under ~2 m/s speed airflow.