본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지 패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다.

Extreme Learning Machine 기반 퍼지 패턴 분류기 설계

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly’s daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

pdf/human-activity-recognition-using-inertial-physiological-and-4wtwq1twc9.pdf

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems.

In recent years, there is a growing interest in Human Activity Recognition (HAR) systems applied in healthcare. A HAR system is essentially made of a wearable device equipped with a set of sensors (like accelerometers, gyroscopes, magnetometers, heart-rate sensors, etc…) and a classifier able to recognize the activity performed. In this study we focused on the choice of the classifier, since there isn’t a unique and consolidated methodology for HAR. The main aim of the study is to compare the performances of 5 classifiers, based on machine learning. Furthermore, we analyzed advantages and disadvantages of their implementation onto a wearable and realtime HAR system. We acquired magnetic and inertial measurement unit (MIMU) signals from 15 young volunteers. For each subject, we recorded 9 signals from tri-axis accelerometer, gyroscope and magnetometer. All signals were divided in 5s-windows and processed to extract 342 features in time, frequency and time-frequency domains. By means of two feature selection steps (correlation-based and genetic algorithm), we reduced the number of features to 69. These features were used as input for the following 5 classifiers: K-Nearest Neighbor (KNN), Feedforward Neural Network (FNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Tree (DT). Our results showed that all classifiers were able to correctly recognize more than 90% of activities. The best performances were obtained by KNN. Analyzing advantages and disadvantages of each classifier for its implementation by means of a microcontroller the most suitable was DT. In fact, this classifier can be easily implemented, it has low memory and computational requirements, and it allows for a further reduction of the required features.

Human Activity Recognition by Wearable Sensors : Comparison of different classifiers for real-time applications

Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to compare two different feature sets for HAR. Particularly, we compared a set including time, frequency, and time-frequency domain features widely used in literature (FeatSet_A) with a set of time-domain features derived by considering the physical meaning of the acquired signals (FeatSet_B). The comparison of the two sets were based on the performances obtained using four machine learning classifiers. Sixty-one healthy subjects were asked to perform seven different daily activities wearing a MIMU-based device. Each signal was segmented using a 5-s window and for each window, 222 and 221 variables were extracted for the FeatSet_A and FeatSet_B respectively. Each set was reduced using a Genetic Algorithm (GA) simultaneously performing feature selection and classifier optimization. Our results showed that Support Vector Machine achieved the highest performances using both sets (97.1% and 96.7% for FeatSet_A and FeatSet_B respectively). However, FeatSet_B allows to better understand alterations of the biomechanical behavior in more complex situations, such as when applied to pathological subjects.

https://www.mdpi.com/1424-8220/18/12/4189/pdf

Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors.

Aiming at the poor accuracy of single classifier in recognizing daily activities based on single accelerometer, this paper presents a method of daily activity recognition based on ensemble learning and full information matrix based fusion weight. Firstly, features from three attributes are extracted from the acceleration signals respectively. The three kinds of features can well describe the information of the activity, and they are relatively independent, which can reduce the interference caused by information redundancy in the process of fusion. Then three base classifiers of support vector machines are constructed based on three kinds of features respectively. Secondly, Euclidean distance between the test sample and every training sample for each type of feature vector is calculated to find out the k nearest neighbors of the test sample from the training set by the K-nearest neighbour method. The cluster analysis is used to compute the similarity between every neighbor and the test sample. Then, a proper threshold is utilized to remove the invalid neighbor whose similarity is less than the threshold. According to the effective neighbor, the full information matrix is constructed to calculate the accuracy. The weight of every single classifier is set dynamically according to the accuracy. Experiments showed that our proposed method get the best average recognition accuracy of 94.79% among several other weight functions when using majority voting method, besides, the time cost is also appealing.

Adaptive multiple classifiers fusion for inertial sensor based human activity recognition

In order to achieve the goal of controlling the intelligent lower limb prosthesis effectively, it is very crucial to recognize the gait pattern of the lower limb, which usually includes walk, up and down stairs or slopes, etc. This paper proposes a gait recognition method based on coalitional game-based feature selection and extreme learning machine. Firstly, this paper extracts characteristic values of four periods in gait cycle, obtaining 24 features. Secondly, in order to improve the accuracy and reduce the computational complexity, a coalitional game-based feature selection algorithm is used to select the prominent features. Lastly, the extreme learning machine (ELM) is used to recognize the gait pattern, which can have a better result in identifying the five kinds of gait pattern in this experiment, compared with BP neural network. Compared with other feature selection algorithms, including mRMR and Relief-F, the proposed method selects fewer features and provides higher accuracy and has faster recognition speed, which proves the effectiveness and feasibility of the proposed method.

Gait Recognition Via Coalitional Game-based Feature Selection and Extreme Learning Machine

The automatic classification of epileptic electroencephalogram (EEG) is important for the diagnosis and treatment of epilepsy. In this paper, an epileptic EEG classification method based on wavelet multi-scale analysis and particle swarm optimization is proposed. Firstly, the multi-scale is carried out to the original EEG to extract its sub-bands of different frequency. Secondly, the Hurst exponent and the sample entropy are used to extract the EEG signals and its sub-bands. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of the extreme learning machine (ELM), and the obtained eigenvector is put to PSO-ELM to realize the purpose of classification of epileptic EEG. The proposed method in this paper achieved 99.7% classification accuracy for the discrimination between epileptic ictal and interictal EEG, which is superior to those methods in other studies.

A Classification Method for Epileptic Electroencephalogram Based on Wavelet Multi-scale Analysis and Particle Swarm Optimization Algorithm

The intonation recognition of piano scores is an important problem in the field of music information retrieval. Based on the neural network theory, this study constructs a piano playing intonation recognition model and uses the optimized result as the feature of piano music to realize the prediction of the music recognition of the intonation preference. The model combines the behavioral preference relationship between intonation and musical notation to measure the similarity between intonations, which is used to calculate the similarity between intonation preference and music, and solves the quantification problem of intonation recognition. In the simulation process, the pitch preference feature of piano playing is used as the identification basis, and the effectiveness of the algorithm is verified through four sets of experiments. The experimental results show that the average symbol error rate of the improved network model is reduced to 0.3234%, and the model training time is about 33.3% of the traditional convolutional recurrent neural network, which is optimized in terms of recognition accuracy and training time in single-class pitch feature. In the recommended method of multi-category evaluation of pitch features, the recognition accuracy of multi-category pitch features is 42.89%, which effectively improves the musical tone recognition rate.

/pdf/construction-and-application-of-a-piano-playing-pitch-h1a5vez9.pdf

Construction and Application of a Piano Playing Pitch Recognition Model Based on Neural Network

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Papers

Adaptive multiple classifiers fusion for inertial sensor based human activity recognition

Gait Recognition Via Coalitional Game-based Feature Selection and Extreme Learning Machine

A Classification Method for Epileptic Electroencephalogram Based on Wavelet Multi-scale Analysis and Particle Swarm Optimization Algorithm

Construction and Application of a Piano Playing Pitch Recognition Model Based on Neural Network