Jeen-Shing Wang

Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers

This paper presents a systematic design approach for constructing neural classifiers that are capable of classifying human activities using a triaxial accelerometer. The philosophy of our design approach is to apply a divide-and-conquer strategy that separates dynamic activities from static activities preliminarily and recognizes these two different types of activities separately. Since multilayer neural networks can generate complex discriminating surfaces for recognition problems, we adopt neural networks as the classifiers for activity recognition. An effective feature subset selection approach has been developed to determine significant feature subsets and compact classifier structures with satisfactory accuracy. Experimental results have successfully validated the effectiveness of the proposed recognition scheme.

A fully automated recurrent neural network for unknown dynamic system identification and control

This paper presents a fully automated recurrent neural network (FARNN) that is capable of self-structuring its network in a minimal representation with satisfactory performance for unknown dynamic system identification and control. A novel recurrent network, consisting of a fully-connected single-layer neural network and a feedback interconnected dynamic network, was developed to describe an unknown dynamic system as a state-space representation. Next, a fully automated construction algorithm was devised to construct a minimal state-space representation with the essential dynamics captured from the input-output measurements of the unknown system. The construction algorithm integrates the methods of minimal model determination, parameter initialization and performance optimization into a systematic framework that totally exempt trial-and-error processes on the selections of network sizes and parameters. Computer simulations on benchmark examples of unknown nonlinear dynamic system identification and control have successfully validated the effectiveness of the proposed FARNN in constructing a parsimonious network with superior performance

An Accelerometer-Based Digital Pen With a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition

This paper presents an accelerometer-based digital pen for handwritten digit and gesture trajectory recognition applications. The digital pen consists of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module for sensing and collecting accelerations of handwriting and gesture trajectories. The proposed trajectory recognition algorithm composes of the procedures of acceleration acquisition, signal preprocessing, feature generation, feature selection, and feature extraction. The algorithm is capable of translating time-series acceleration signals into important feature vectors. Users can use the pen to write digits or make hand gestures, and the accelerations of hand motions measured by the accelerometer are wirelessly transmitted to a computer for online trajectory recognition. The algorithm first extracts the time- and frequency-domain features from the acceleration signals and, then, further identifies the most important features by a hybrid method: kernel-based class separability for selecting significant features and linear discriminant analysis for reducing the dimension of features. The reduced features are sent to a trained probabilistic neural network for recognition. Our experimental results have successfully validated the effectiveness of the trajectory recognition algorithm for handwritten digit and gesture recognition using the proposed digital pen.

An Inertial-Measurement-Unit-Based Pen With a Trajectory Reconstruction Algorithm and Its Applications

This paper presents an inertial-measurement-unit-based pen (IMUPEN) and its associated trajectory reconstruction algorithm for motion trajectory reconstruction and handwritten digit recognition applications. The IMUPEN is composed of a triaxial accelerometer, two gyroscopes, a microcontroller, and an RF wireless transmission module. Users can hold the IMUPEN to write numerals or draw simple symbols at normal speed. During writing or drawing movements, the inertial signals generated for the movements are transmitted to a computer via the wireless module. A trajectory reconstruction algorithm composed of the procedures of data collection, signal preprocessing, and trajectory reconstruction has been developed for reconstructing the trajectories of movements. In order to minimize the cumulative errors caused by the intrinsic noise/drift of sensors, we have developed an orientation error compensation method and a multiaxis dynamic switch. The advantages of the IMUPEN include the following: 1) It is portable and can be used anywhere without any external reference device or writing ambit limitations, and 2) its trajectory reconstruction algorithm can reduce orientation and integral errors effectively and thus can reconstruct the trajectories of movements accurately. Our experimental results on motion trajectory reconstruction and handwritten digit recognition have successfully validated the effectiveness of the IMUPEN and its trajectory reconstruction algorithm.

Development of a portable activity detector for daily activity recognition

This paper presents a portable device for real-time daily activity identification. The proposed portable device is realized by an embedded system that integrates a triaxial accelerometer, a microprocessor, and a wireless transceiver module. An online activity recognition algorithm has been developed for human daily activity recognition based on the acceleration signal collected from the triaxial accelerometer. The proposed algorithm is composed of data collection, data preprocessing, feature extraction, feature reduction, and classifier construction. Our experimental results have successfully validated the effectiveness of the portable activity detector in terms of high accuracy.

Walking Pattern Classification and Walking Distance Estimation Algorithms Using Gait Phase Information

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87%, 95.45%, and 95.00% for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42% over a walking distance.

A Wearable Sensor Module With a Neural-Network-Based Activity Classification Algorithm for Daily Energy Expenditure Estimation

This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN.

An Inertial Pen With Dynamic Time Warping Recognizer for Handwriting and Gesture Recognition

This paper presents an inertial-sensor-based digital pen (inertial pen) and its associated dynamic time warping (DTW)-based recognition algorithm for handwriting and gesture recognition. Users hold the inertial pen to write numerals or English lowercase letters and make hand gestures with their preferred handheld style and speed. The inertial signals generated by hand motions are wirelessly transmitted to a computer for online recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition, signal preprocessing, motion detection, template selection, and recognition. We integrate signals collected from an accelerometer, a gyroscope, and a magnetometer into a quaternion-based complementary filter for reducing the integral errors caused by the signal drift or intrinsic noise of the gyroscope, which might reduce the accuracy of the orientation estimation. Furthermore, we have developed a minimal intra-class to maximal inter-class based template selection method (min-max template selection method) for a DTW recognizer to obtain a superior class separation for improved recognition. Experimental results have successfully validated the effectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen.

Gait and balance analysis for patients with Alzheimer's disease using an inertial-sensor-based wearable instrument

Despite patients with Alzheimers disease (AD) were reported of revealing gait disorders and balance problems, there is still lack of objective quantitative measurement of gait patterns and balance capability of AD patients. Based on an inertial-sensor-based wearable device, this paper develops gait and balance analyzing algorithms to obtain quantitative measurements and explores the essential indicators from the measurements for AD diagnosis. The gait analyzing algorithm is composed of stride detection followed by gait cycle decomposition so that gait parameters are developed from the decomposed gait details. On the other hand, the balance is measured by the sway speed in anterior-posterior (AP) and medial-lateral (ML) directions of the projection path of bodys center of mass (COM). These devised gait and balance parameters were explored on twenty-one AD patients and fifty healthy controls (HCs). Special evaluation procedure including single-task and dual-task walking experiments for observing the cognitive function and attention is also devised for the comparison of AD and HC groups. Experimental results show that the wearable instrument with the designed gait and balance analyzing system is a promising tool for automatically analyzing gait information and balance ability, serving as assistant indicators for early diagnosis of AD.

A k-nearest-neighbor classifier with heart rate variability feature-based transformation algorithm for driving stress recognition

Abstract This paper presents a k-nearest-neighbor classifier with HRV feature-based transformation algorithm for driving stress recognition. The proposed feature-based transformation algorithm consists of feature generation, feature selection, and feature dimension reduction. In order to generate significant features from ECG signals, two feature generation approaches: trend-based and parameter-based methods are proposed in this study. The trend-based method computes statistical features from long-term HRV variations, while the parameter-based method calculates features from five-minute HRV analysis. The kernel-based class separability (KBCS) is employed as the selection criterion for feature selection. To reduce computational load of the algorithm, principal component analysis (PCA) and linear discriminant analysis (LDA) are adopted for feature dimension reduction. Our experimental results show that the combination of KBCS, LDA, and PCA can achieve satisfactory recognition rates for the features generated by both trend-based and parameter-based methods. The main contribution of this study is that our proposed approach can use only ECG signals to effectively recognize driving stress conditions with very good recognition performance.