Yu-Liang Hsu

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.

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.

Gait analysis for patients with Alzheimer'S disease using a triaxial accelerometer

This paper presents an inertial-sensor-based wearable device and its associated stride detection algorithm to analyze gait information for patients with Alzheimers disease (AD). The wearable gait analysis device is composed of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. To validate the effectiveness of the proposed device and algorithm, nine AD patients and three healthy controls were recruited to participate a gait analysis experiment. They were asked to mount the device on their foot and walk along a straight line of 40 meters at normal speed. The stride detection algorithm, consisting of procedures of data collection, signal preprocessing, and stride detection, has been developed for acquiring gait feature information from acceleration signals. The advantages of this wearable gait analysis device include the following: 1) It can be used anywhere without any external device, and 2) the stride detection algorithm can acquire gait feature information from acceleration signals automatically and effectively. Experimental results show that the AD patients exhibited a significantly shorter mean stride length and slower mean gait speed than those of the healthy controls. No significant differences in mean stride frequency and mean cadence were observed in the two groups. The variability in the percentage of the stance phase of the AD patients was slightly greater than that of the healthy controls. Based on the above results and discussions with physicians, we conclude that the proposed wearable gait analysis device is a promising tool for automatically analyzing gait information which can serve as indicators for early diagnosis of AD.

A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease

This paper presents a wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm to obtain quantitative measurements and explore clinical indicators from the spatiotemporal gait patterns for patients with stroke or Parkinson’s disease. The wearable system is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and an RF wireless transmission module. The spatiotemporal gait analysis algorithm, consisting of procedures of inertial signal acquisition, signal preprocessing, gait phase detection, and ankle range of motion estimation, has been developed for extracting gait features from accelerations and angular velocities. In order to estimate accurate ankle range of motion, we have integrated accelerations and angular velocities into a complementary filter for reducing the accumulation of integration error of inertial signals. All 24 participants mounted the system on their foot to walk along a straight line of 10 m at normal speed and their walking recordings were collected to validate the effectiveness of the proposed system and algorithm. Experimental results show that the proposed inertial measurement system with the designed spatiotemporal gait analysis algorithm is a promising tool for automatically analyzing spatiotemporal gait information, serving as clinical indicators for monitoring therapeutic efficacy for diagnosis of stroke or Parkinson’s disease.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

A Music Emotion Recognition Algorithm with Hierarchical SVM Based Classifiers

This paper proposes a music emotion recognition algorithm consisting of a kernel-based class separability (KBCS) feature selection method, a nonparametric weighted feature extraction (NWFE) feature extraction method, and a hierarchical support vector machines (SVMs) classifier to recognize four types of music emotion. For each music sample, a total of 35 features from dynamic, rhythm, pitch, and timbre of music were generated from music audio recordings. With the extracted features via feature selection and extraction methods, hierarchical SVM-based classifiers are then utilized to recognize four types of music emotion including happy, tensional, sad and peaceful. The performance of the proposed algorithm was evaluated by two datasets with a total of 219 classical music samples. In the first dataset, music emotion of each sample was annotated by recruited subjects, while the second dataset was labelled by music therapists. The two datasets were used to verify the perceived emotions from normal audience and music expert, respectively. The average accuracy of the proposed algorithm achieved at 86.94% and 92.33% for these two music datasets, respectively. The experimental results have successfully validated the effectiveness of the proposed music emotion recognition algorithm with hierarchical SVM-based classifiers.

An MDL-based Hammerstein recurrent neural network for control applications

This paper presents an efficient control scheme using a Hammerstein recurrent neural network (HRNN) based on the minimum description length (MDL) principle for controlling nonlinear dynamic systems. In the proposed control approach, an unknown system is first identified by the MDL-based HRNN, which consists of a static nonlinear model cascaded by a dynamic linear model and can be expressed in a state-space representation. For high-accuracy system modeling, we have developed a self-construction algorithm that integrates the MDL principle and recursive recurrent learning algorithm for constructing a parsimonious HRNN in an efficient manner. To ease the control of the system, we have established a nonlinearity eliminator that functions as the inverse of the static nonlinear model to remove the global nonlinear behavior of the unknown system. If the system modeling and the inverse of the nonlinear model are accurate, the compound model, the unknown system cascaded with the nonlinearity eliminator, will behave like the linear dynamic model. This assumption turns the task of complex nonlinear control problems into a simple feedback linear controller design. Hence, well-developed linear controller design theories can be applied directly to achieve satisfactory control performance. Computer simulations on unknown nonlinear system control problems have successfully validated the effectiveness of the proposed MDL-based HRNN and its control scheme as well as its superiority in control performance.

A Wearable Inertial Pedestrian Navigation System With Quaternion-Based Extended Kalman Filter for Pedestrian Localization

This paper presents a wearable inertial pedestrian navigation system and its associated pedestrian trajectory reconstruction algorithm for reconstructing pedestrian walking trajectories in indoor and outdoor environments. The proposed wearable inertial pedestrian navigation system is constructed by integrating a triaxial accelerometer, a triaxial gyroscope, a triaxial magnetometer, a microcontroller, and a Bluetooth wireless transmission module. Users wear the system on foot while walking in indoor and outdoor environments at normal speed without any external positioning techniques. During walking movement, the measured inertial signals generated from walking movements are transmitted to a computer via the wireless module. Based on the foot-mounted inertial pedestrian navigation system, a pedestrian trajectory reconstruction algorithm composed of the procedures of inertial signal acquisition, signal preprocessing, trajectory reconstruction, and trajectory height estimation has been developed to reconstruct floor walking and stair climbing trajectories. In order to minimize the cumulative error of the inertial signals, we have utilized a sensor fusion technique based on a double-stage quaternion-based extended Kalman filter to fuse acceleration, angular velocity, and magnetic signals. Experimental results have successfully validated the effectiveness of the proposed wearable inertial pedestrian navigation system and its associated pedestrian trajectory reconstruction algorithm.

Finding similar users in social networks by using the depth-k skyline query

Search algorithms designed to seek out similar users in social networking sites are a significant function of recommendation systems. Conventionally, such sub-algorithms consider all the dimensions of user data as a whole. However, as the information in various dimensions is generally independent, the conventional approaches may not be the best way to find similar users. This paper solves this problem by proposing an approach based on depth-k skyline queries that searches for similar users with multiple conditions. This paper also presented an algorithm to accelerate this process, the effectiveness of which was demonstrated in a simulation.