This paper presents a low power boost converter for thermoelectric energy harvesting that demonstrates an efficiency that is 15% higher than the state-of-the-art for voltage conversion ratios above 20. This is achieved by utilizing a technique allowing synchronous rectification in the discontinuous conduction mode. A low-power method for input voltage monitoring is presented. The low input voltage requirements allow operation from a thermoelectric generator powered by body heat. The converter, fabricated in a 0.13 μm CMOS process, operates from input voltages ranging from 20 mV to 250 mV while supplying a regulated 1 V output. The converter consumes 1.6 (1.1) μW of quiescent power, delivers up to 25 (175) μW of output power, and is 46 (75)% efficient for a 20 mV and 100 mV input, respectively.

A 20 mV Input Boost Converter With Efficient Digital Control for Thermoelectric Energy Harvesting

An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition. It includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features. A Bayes linear classifier and an improved dynamic time-warping algorithm are utilized in the framework. In addition, a prototype system, including a wearable gesture sensing device (embedded with a three-axis accelerometer and four SEMG sensors) and an application program with the proposed algorithmic framework for a mobile phone, is developed to realize gesture-based real-time interaction. With the device worn on the forearm, the user is able to manipulate a mobile phone using 19 predefined gestures or even personalized ones. Results suggest that the developed prototype responded to each gesture instruction within 300 ms on the mobile phone, with the average accuracy of 95.0% in user-dependent testing and 89.6% in user-independent testing. Such performance during the interaction testing, along with positive user experience questionnaire feedback, demonstrates the utility of the framework.

A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices

This paper presents three different gesture recognition models which are capable of recognizing seven hand gestures, i.e., up, down, left, right, tick, circle, and cross, based on the input signals from MEMS 3-axes accelerometers. The accelerations of a hand in motion in three perpendicular directions are detected by three accelerometers respectively and transmitted to a PC via Bluetooth wireless protocol. An automatic gesture segmentation algorithm is developed to identify individual gestures in a sequence. To compress data and to minimize the influence of variations resulted from gestures made by different users, a basic feature based on sign sequence of gesture acceleration is extracted. This method reduces hundreds of data values of a single gesture to a gesture code of 8 numbers. Finally, the gesture is recognized by comparing the gesture code with the stored templates. Results based on 72 experiments, each containing a sequence of hand gestures (totaling 628 gestures), show that the best of the three models discussed in this paper achieves an overall recognition accuracy of 95.6%, with the correct recognition accuracy of each gesture ranging from 91% to 100%. We conclude that a recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.

MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition

Numerous sensors in modern mobile phones enable a range of people-centric applications. This paper envisions a system called PhonePoint Pen that uses the in-built accelerometer in mobile phones to recognize human writing. By holding the phone like a pen, a user should be able to write short messages or draw simple diagrams in the air. The acceleration due to hand gestures can be translated into geometric strokes, and recognized as characters. We prototype the PhonePoint Pen on the Nokia N95 platform, and evaluate it through real users. Results show that English characters can be identified with an average accuracy of 91.9%, if the users conform to a few reasonable constraints. Future work is focused on refining the prototype, with the goal of offering a new user-experience that complements keyboards and touch-screens.

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Using mobile phones to write in air

Inertial measurement units (IMUs) are widely used in motion measurement. However, the drift of IMUs results in significant accumulated errors for long-term position and orientation measurement. This paper reports a method to compensate the drift of inertial sensors with the assist of ultrasonic sensors and magnetometers. The magnetometer is combined with a three-axis accelerometer as a digital compass for orientation determination in static state by measuring the gravity and the earth's magnetic field vectors. A three-axis gyroscope is used to measure the orientation in dynamic state to complement the digital compass. Displacement determination is implemented using the accelerometer through double integration, and an ultrasonic sensor is employed to periodically calibrate the accumulated errors of the accelerometer. The redundant data from the multi-sensors are fused using extended Kalman filter (EKF), in which the position measured by the ultrasonic sensor and the orientation measured by the digital compass are defined as the observation values, and the position, velocity, and orientation are included in the state vector. Experimental results show that the accumulated errors of inertial sensors are reduced by ultrasonic sensors and magnetometers, and EKF improves the accuracy of orientation and position measurements.

Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

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.

Gesture recognition for interactive controllers using MEMS motion sensors

In this paper, a micro inertial measurement unit (muIMU) based on micro electro mechanical systems (MEMS) sensors is applied to sense the motion information produced by characters written by human subjects. The muIMU is built to record the three-dimensional accelerations and angular velocities of the motions during hand-writing. (Here we write the characters in a plane, so only two accelerations and one angular velocity are taken from muIMU in processing the data discussed in this paper). Then, we compared the effectiveness of data processing methods such as FFT (fast Fourier transform) and DCT (discrete cosine transform) by showing their corresponding experimental results. Subsequently, we gave an analysis of these two methods, and chose DCT as the preferred data processing method. For character recognition (26 English alphabets and 10 numerical digits), unsupervised network self-organizing map (SOM) is applied to classify the characters and comparatively good results are obtained. Our goal is to show the feasibility of character recognition based on selected sensor motion information, and provide a potential technology for human-gesture recognition based on MEMS motion sensors.

Hand-written character recognition using MEMS motion sensing technology

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.

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

Wireless sensor networks (WSNs) have been widely used for intelligent building management applications. Typically, indoor environment parameters such as illumination, temperature, humidity and air quality are monitored and adjusted by an intelligent building management system. However, owing to the short life-span of the batteries used at the sensor nodes, the maintenance of such systems has been labor-intensive and time-consuming. This paper discusses a battery-less self-powering system that converts the mechanical energy from the airflow in ventilation ducts into electrical energy. The system uses a flutter energy conversion device (FECD) capable of working at low airflow speeds while installed on the ventilation ducts inside of buildings. A power management strategy implemented with a circuit system ensures sufficient power for driving commercial electronic devices. For instance, the power management circuit is capable of charging a 1 F super capacitor to 2 V under ventilation duct airflow speeds of less than 3 m/s.

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Shengli Zhou

Papers

MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition

Gesture recognition for interactive controllers using MEMS motion sensors

Hand-written character recognition using MEMS motion sensing technology

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

Development of an Indoor Airflow Energy Harvesting System for Building Environment Monitoring