DOCSLIB.ORG

Feature Engineering for Human Activity Recognition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Feature Engineering for Human Activity Recognition (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 2, 2021 Feature Engineering for Human Activity Recognition Basma A. Atalaa1*, Ibrahim Ziedan2, Ahmed Alenany3, Ahmed Helmi4 Department of Computer and Systems Engineering Faculty of Engineering Zagazig University, Zagazig, 44519 Egypt Abstract—Human activity recognition (HAR) techniques can relatively limited-resources smart devices. Therefore, significantly contribute to the enhancement of health and life numerous studies in literature have been conducted to look for care systems for elderly people. These techniques, which suitable representative features for activities, as well as good generally operate on data collected from wearable sensors or enough recognition models [9]. Moreover, benchmark datasets those embedded in most smart phones, have therefore attracted available in literature are different in type of activities, number increasing interest recently. In this paper, a random forest-based of recorded examples for each activity, experimental settings, classifier for human activity recognition is proposed. The i.e. controlled procedure [18] whether indoor or outdoor classifier is trained using a set of time-domain features extracted environments [19], used sensors and sensor position on from raw sensor data after being segmented into windows of 5 subject body. According to aforementioned factors, there is a seconds duration. A detailed study of model parameter selection significant variance of available HAR systems accuracy in is presented using the statistical t-test. Several simulation experiments are conducted on the WHARF accelerometer conjunction with different datasets [20]. benchmark dataset, to compare the performance of the proposed HAR recognition techniques can be grouped into two main classifier to support vector machines (SVM) and Artificial Neural categories. The first is based on computer vision [21, 22] and Network (ANN). The proposed model shows high recognition the second is based on data collected from one or more rates for different activities in the WHARF dataset compared to sensors. What makes the latter approach appealing is that other classifiers using the same set of features. Furthermore, it sensors are affordable and are usually found in reasonably achieves an overall average precision of 86.1% outperforming priced smartphones. Another advantage is that computational the recognition rate of 79.1% reported in the literature using Convolution Neural Networks (CNN) for the WHARF dataset. and storage requirements for processing sensor data is less From a practical point of view, the proposed model is simple and than those required for image processing techniques. efficient. Therefore, it is expected to be suitable for In this work, the relatively challenging Wearable Human implementation in hand-held devices such as smart phones with Activity Recognition Folder (WHARF) dataset is extensively their limited memory and computational resources. investigated. This dataset is collected using a tri-axial accelerometer placed on the right wrist of subjects; hence it Keywords—Human activity recognition; random forest; feature emulates a smart watch. It is chanllenging because of its small engineering; sensor signal processing sampling rate, 32 Hz, compared to other datasets collected I. INTRODUCTION using e.g. 50 Hz sampling frequency. Real-time considerations for HAR systems require dealing with segments of data points In daily life, a person performs diverse set of activities with window length between 2 seconds and 10 seconds. such as standing up, sitting down, walking, climbing stairs, Therefore, sensors with small sampling rate will deliver fewer etc. Automatic recognition of human activities has interesting data points complicating the task of HAR system. Moreover, applications in healthcare [1], keeping track of elderly people there are 12 different activities in WHARF with few number [2], and home automation [3]. Also, it has many clinical of examples per activity [13]. The proposed approach here applications for stroke patients [4], Parkinson's disease applies data preporcessing in which signals are filtered using a patients[5], heart rate estimation [6] and in a smart health care low-pass filter and then scaled so that all features lie within environment [7]. the same range. In the second step, data is segmented into The last two decades witnessed increasing interest in windows of length 5 seconds with 50% overlapping. In the Human Activity Recognition (HAR) techniques due to the third step, several effective time-domain functions or features availability of low cost sensors specially those built-in sensors are extracted. The proposed classifier employs the Random available in affordable smartphones [8-10]. Commonly used Forest (RF) algorithm which achieves the best precision and sensor types in HAR applications are accelerometers [11-14], also the best training time compared to other classifiers such heart rate belt sensor [15], gyroscope [16, 17], magnetometer as Artificial Neural Networks (ANN) and Support Vector [17], or three-inertial sensor units mounted on chest, right Machine (SVM). The proposed system is expected to be thigh and left ankle [12]. Such inertia devices operate at low efficient and resource-friendly for smart devices. Besides, frequencies and require low sampling rates. There are several sensitivity analysis of proposed system components such as issues which make HAR task challenging such as noisy sensor RF parameters, some important features and preprocessing data, insufficient training examples due to few participating scaling step is conducted. Also, feature importance is subjects, and the need to implement HAR systems on discussed using the statistical t-test. *Corresponding Author 160 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 2, 2021 The contribution of this work can be highlighted as On the other hand, classifiers used in HAR studies can be follows: (1) introducing RF-based effective and efficient HAR classified into supervised or unsupervised. Supervised system with average precision of 86.1% and average accuracy classifiers [20] include multilayer neural networks [17, 18, 30, of 84.8% which improves the state-of-the-art rate of 79.1% for 31, 34], support vector machine (SVM) [11, 12], decision WHARF dataset, (2) testing the proposed system on the trees [30, 31], random forest [12], k-Nearest Neighbors (kNN) challenging WHARF datase which is considered in only few [12, 16] and Bayes classifier [16, 25]. Unsupervised studies in literature [23] and [24], (3) discussing the practical technique, on the other hand, include Gaussian mixture model implementation issues of proposed system which is important (GMM) [13], linear-discriminant analysis [27, 28], minimal in case of further system application on smart devices, and (4) learning machine (MLM) [16], k-means clustering, conducting sensitivity analysis of important system convolutional neural networks (CNN) [35-37] and hidden components to determine the optimal settings for proposed Markov model (HMM) [12]. system. III. TIME-DOMAIN AND STATISTICAL FEATURES The rest of this paper is organized as follows. In Section II, relevant related work in the literature is reviewed. The set of In this section, the set of features extracted from pre- features to be employed and the proposed Random Forest- processed raw acceleration signals is listed. It is assumed that based classifier are presented in Sections III and IV, there is a three-dimensional dataset of size N data points respectively. In Section V, a set of experiments are conducted collected from an accelerometer or a gyroscope, ax(i), ay(i), to evaluate the performance of the proposed model and az(i), i =1, 2, · · · , N, for the x, y, and z dimensions. The data is compare it to other machine learning techniques. Sensitivity first filtered using low pass filter to reduce noise and extract analysis is preformed to optimally select the parameters of the the body acceleration bx(i), by(i), bz(i) and gravity acceleration proposed model in Section VI. Finally, conclusions and gx(i), gy(i), gz(i) components [24]. possible future work are drawn in Section VII. The set of features to be employed in classification are derived from both body and gravity acceleration signals as II. RELATED WORK listed in Table I. The body acceleration signal features include The HAR procedure from preprocessed raw sensory data the mean (M) and standard deviation (STD) of filtered signals, can be divided into two steps: (1) extracting relevant key autoregressive model coefficients, signal magnitude area, tilt features from collected data signals (so-called feature angle, mean, standard deviation, entropy of jerk of signals, engineering), and (2) classifying the observed activity based mean, standard deviation, power and entropy of jerk of roll on the extracted features. The reduction of data dimensionality angle. For gravity acceleration component, the signal power may can also be required using e.g. principle component along each axis and the mean of angle of x-axis component are analysis [25]. Due to the diversity of feature types and the used. classifiers that can be used in these two steps, respectively, the literature of HAR problem is wide and extensive. IV. THE PROPOSED MODEL Sensors such as tri-axial accelerometer and gyroscope The proposed classifier consists of three stages as shown provide time domain acceleration
Load more

Recommended publications
  • Channel Selective Activity Recognition with Wifi: a Deep Learning Approach Exploring Wideband Information
    1 Channel Selective Activity Recognition with WiFi: A Deep Learning Approach Exploring Wideband Information Fangxin Wang, Student Member, IEEE, Wei Gong, Member, IEEE, Jiangchuan Liu, Fellow, IEEE and Kui Wu, Senior Member, IEEE Abstract—WiFi-based human activity recognition explores the correlations between body movement and the reflected WiFi signals to classify different activities. State-of-the-art solutions mostly work on a single WiFi channel and hence are quite sensitive to the quality of a particular channel. Co-channel interference in an indoor environment can seriously undermine the recognition accuracy. In this paper, we for the first time explore wideband WiFi information with advanced deep learning toward more accurate and robust activity recognition. We present a practical Channel Selective Activity Recognition system (CSAR) with Commercial Off-The-Shelf (COTS) WiFi devices. The key innovation is to actively select available WiFi channels with good quality and seamlessly hop among adjacent channels to form an extended channel. The wider bandwidth with more subcarriers offers stable information with a higher resolution for feature extraction. Conventional classification tools, e.g., hidden Markov model and k-nearest neighbors, however, are not only sensitive to feature distortion but also not smart enough to explore the time-scale correlations from the extracted spectrogram. We accordingly explore advanced deep learning tools for this application context. We demonstrate an integration of channel selection and long short term memory network (LSTM), which seamlessly combine the richer time and frequency features for activity recognition. We have implemented a CSAR prototype using Intel 5300 WiFi cards. Our real-world experiments show that CSAR achieves a stable recognition accuracy around 95% even in crowded wireless environments (compared to 80% with state-of-the-art solutions that highly depend on the quality of the working channel).
    [Show full text]
  • Human Activity Analysis and Recognition from Smartphones Using Machine Learning Techniques
    Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques Jakaria Rabbi, Md. Tahmid Hasan Fuad, Md. Abdul Awal Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna-9203, Bangladesh jakaria [email protected], [email protected], [email protected] Abstract—Human Activity Recognition (HAR) is considered a HAR is the problem of classifying day-to-day human ac- valuable research topic in the last few decades. Different types tivity using data collected from smartphone sensors. Data are of machine learning models are used for this purpose, and this continuously generated from the accelerometer and gyroscope, is a part of analyzing human behavior through machines. It is not a trivial task to analyze the data from wearable sensors and these data are instrumental in predicting our activities for complex and high dimensions. Nowadays, researchers mostly such as walking or standing. There are lots of datasets and use smartphones or smart home sensors to capture these data. ongoing research on this topic. In [8], the authors discuss In our paper, we analyze these data using machine learning wearable sensor data and related works of predictions with models to recognize human activities, which are now widely machine learning techniques. Wearable devices can predict an used for many purposes such as physical and mental health monitoring. We apply different machine learning models and extensive range of activities using data from various sensors. compare performances. We use Logistic Regression (LR) as the Deep Learning models are also being used to predict various benchmark model for its simplicity and excellent performance on human activities [9].
    [Show full text]
  • Human Activity Recognition Using Computer Vision Based Approach – Various Techniques
    International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 06 | June 2020 www.irjet.net p-ISSN: 2395-0072 Human Activity Recognition using Computer Vision based Approach – Various Techniques Prarthana T V1, Dr. B G Prasad2 1Assistant Professor, Dept of Computer Science and Engineering, B. N. M Institute of Technology, Bengaluru 2Professor, Dept of Computer Science and Engineering, B. M. S. College of Engineering, Bengaluru ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Computer vision is an important area in the field known as Usual Activities. Any activity which is different of computer science which aids the machines in becoming from the defined set of activities is called as Unusual Activity. smart and intelligent by perceiving and analyzing digital These unusual activities occur because of mental and physical images and videos. A significant application of this is Activity discomfort. Unusual activity or anomaly detection is the recognition – which automatically classifies the actions being process of identifying and detecting the activities which are performed by an agent. Human activity recognition aims to different from actual or well-defined set of activities and apprehend the actions of an individual using a series of attract human attention. observations considering various challenging environmental factors. This work is a study of various techniques involved, Applications of human activity recognition are many. A few challenges identified and applications of this research area. are enumerated and explained below: Key Words: Human Activity Recognition, HAR, Deep Behavioral Bio-metrics - Bio-metrics involves uniquely learning, Computer vision identifying a person through various features related to the person itself. Behavior bio-metrics is based on the 1.
    [Show full text]
  • Vision-Based Human Tracking and Activity Recognition Robert Bodor Bennett Jackson Nikolaos Papanikolopoulos AIRVL, Dept
    Vision-Based Human Tracking and Activity Recognition Robert Bodor Bennett Jackson Nikolaos Papanikolopoulos AIRVL, Dept. of Computer Science and Engineering, University of Minnesota. to humans, as it requires careful concentration over long Abstract-- The protection of critical transportation assets periods of time. Therefore, there is clear motivation to and infrastructure is an important topic these days. develop automated intelligent vision-based monitoring Transportation assets such as bridges, overpasses, dams systems that can aid a human user in the process of risk and tunnels are vulnerable to attacks. In addition, facilities detection and analysis. such as chemical storage, office complexes and laboratories can become targets. Many of these facilities A great deal of work has been done in this area. Solutions exist in areas of high pedestrian traffic, making them have been attempted using a wide variety of methods (e.g., accessible to attack, while making the monitoring of the optical flow, Kalman filtering, hidden Markov models, etc.) facilities difficult. In this research, we developed and modalities (e.g., single camera, stereo, infra-red, etc.). components of an automated, “smart video” system to track In addition, there has been work in multiple aspects of the pedestrians and detect situations where people may be in issue, including single pedestrian tracking, group tracking, peril, as well as suspicious motion or activities at or near and detecting dropped objects. critical transportation assets. The software tracks individual pedestrians as they pass through the field of For surveillance applications, tracking is the fundamental vision of the camera, and uses vision algorithms to classify component. The pedestrian must first be tracked before the motion and activities of each pedestrian.
    [Show full text]
  • Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
    Fine-Grained Activity Recognition by Aggregating Abstract Object Usage Donald J. Patterson, Dieter Fox, Henry Kautz Matthai Philipose University of Washington Intel Research Seattle Department of Computer Science and Engineering Seattle, Washington, USA Seattle, Washington, USA [email protected] {djp3,fox,kautz}@cs.washington.edu Abstract in directly sensed experience. More recent work on plan- based behavior recognition [4] uses more robust probabilis- In this paper we present results related to achieving fine- tic inference algorithms, but still does not directly connect grained activity recognition for context-aware computing to sensor data. applications. We examine the advantages and challenges In the computer vision community there is much work of reasoning with globally unique object instances detected on behavior recognition using probabilistic models, but it by an RFID glove. We present a sequence of increasingly usually focuses on recognizing simple low-level behaviors powerful probabilistic graphical models for activity recog- in controlled environments [9]. Recognizing complex, high- nition. We show the advantages of adding additional com- level activities using machine vision has only been achieved plexity and conclude with a model that can reason tractably by carefully engineering domain-specific solutions, such as about aggregated object instances and gracefully general- for hand-washing [13] or operating a home medical appli- izes from object instances to their classes by using abstrac- ance [17]. tion smoothing. We apply these models to data collected There has been much recent work in the wearable com- from a morning household routine. puting community, which, like this paper, quantifies the value of various sensing modalities and inference tech- niques for interpreting context.
    [Show full text]
  • Joint Recognition of Multiple Concurrent Activities Using Factorial Conditional Random Fields
    Joint Recognition of Multiple Concurrent Activities using Factorial Conditional Random Fields Tsu-yu Wu and Chia-chun Lian and Jane Yung-jen Hsu Department of Computer Science and Information Engineering National Taiwan University [email protected] Abstract the home for varying lengths of time. Sensor data are col- lected as the occupants interact with digital information in Recognizing patterns of human activities is an important this naturalistic living environment. As a result, the House n enabling technology for building intelligent home environ- ments. Existing approaches to activity recognition often fo- data set (Intille et al. 2006a) is potentially a good benchmark cus on mutually exclusive activities only. In reality, peo- for research on activity recognition. In this paper, we use the ple routinely carry out multiple concurrent activities. It is data set as the material to design our experiment. therefore necessary to model the co-temporal relationships Our analysis of the House n data set reveals a significant among activities. In this paper, we propose using Factorial observation. That is, in a real-home environment, people of- Conditional Random Fields (FCRFs) for recognition of mul- ten do multiple activities concurrently. For example, the par- tiple concurrent activities. We designed experiments to com- ticipant would throw the clothes into the washing machine pare our FCRFs model with Linear Chain Condition Random and then went to the kitchen doing some meal preparation. Fields (LCRFs) in learning and performing inference with the As another example, the participant had the habit of using MIT House n data set, which contains annotated data col- lected from multiple sensors in a real living environment.
    [Show full text]
  • Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
    applied sciences Article Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks Konstantinos Peppas * , Apostolos C. Tsolakis , Stelios Krinidis and Dimitrios Tzovaras Centre for Research and Technology—Hellas, Information Technologies Institute, 6th km Charilaou-Thermi Rd, 57001 Thessaloniki, Greece; [email protected] (A.C.T.); [email protected] (S.K.); [email protected] (D.T.) * Correspondence: [email protected] Received: 20 October 2020; Accepted: 24 November 2020; Published: 27 November 2020 Abstract: Given the ubiquity of mobile devices, understanding the context of human activity with non-intrusive solutions is of great value. A novel deep neural network model is proposed, which combines feature extraction and convolutional layers, able to recognize human physical activity in real-time from tri-axial accelerometer data when run on a mobile device. It uses a two-layer convolutional neural network to extract local features, which are combined with 40 statistical features and are fed to a fully-connected layer. It improves the classification performance, while it takes up 5–8 times less storage space and outputs more than double the throughput of the current state-of-the-art user-independent implementation on the Wireless Sensor Data Mining (WISDM) dataset. It achieves 94.18% classification accuracy on a 10-fold user-independent cross-validation of the WISDM dataset. The model is further tested on the Actitracker dataset, achieving 79.12% accuracy, while the size and throughput of the model are evaluated on a mobile device. Keywords: activity recognition; convolutional neural networks; deep learning; human activity recognition; mobile inference; time series classification; feature extraction; accelerometer data 1.
    [Show full text]
  • LSTM Networks for Mobile Human Activity Recognition
    International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2016) LSTM Networks for Mobile Human Activity Recognition Yuwen Chen*, Kunhua Zhong, Ju Zhang, Qilong Sun and Xueliang Zhao Chongqing institute of green and intelligent technology Chinese Academy of Sciences *Corresponding author Abstract—A lot of real-life mobile sensing applications are features, while discrete cosine transform (DCT) have also been becoming available. These applications use mobile sensors applied with promising results [12], as well as auto-regressive embedded in smart phones to recognize human activities in order model coefficients [13]. Recently, time-delay embedding [14] to get a better understanding of human behavior. In this paper, have been applied for activity recognition. It adopts nonlinear we propose a LSTM-based feature extraction approach to time series analysis to extract features from time series and recognize human activities using tri-axial accelerometers data. shows a significant improvement on periodic activities The experimental results on the (WISDM) Lab public datasets recognition .However, the features from time-delay indicate that our LSTM-based approach is practical and achieves embedding are less suitable for non-periodic activities. The 92.1% accuracy. authors in [15] firstly introduce feature learning methods to the Keywords-Activity recognition, Deep learning, Long short area of activity recognition, they used Deep Belief Networks memory network (DBN) and PCA to learn features for activity recognition in ubiquitous computing. The authors in [16] following the work of [15] applied shift-invariant sparse coding technique. The I. INTRODUCTION authors in [17] also used sparse coding to learn features. Although human activity recognition (HAR) has been The features used in most of researches on HAR are studied extensively in the past decade, HAR on smartphones is selected by hand.
    [Show full text]
  • Convolutional Neural Networks for Human Activity Recognition Using Mobile Sensors
    WK,QWHUQDWLRQDO&RQIHUHQFHRQ0RELOH&RPSXWLQJ$SSOLFDWLRQVDQG6HUYLFHV 0REL&$6( Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, Joy Zhang Department of Electrical and Computer Engineering Carnegie Mellon University Moffett Field, CA, USA Email: {zeng, le.nguyen, bo.yu, ole.mengshoel, jiang.zhu, pang.wu, joy.zhang}@sv.cmu.edu Abstract—A variety of real-life mobile sensing applications are requires domain knowledge [23]. This problem is not unique to becoming available, especially in the life-logging, fitness tracking activity recognition. It has been well-studied in other research and health monitoring domains. These applications use mobile areas such as image recognition [22], where different types sensors embedded in smart phones to recognize human activities of features need to be extracted when trying to recognize a in order to get a better understanding of human behavior. While handwriting as opposed to recognizing faces. In recent years, progress has been made, human activity recognition remains due to advances of the processing capabilities, a large amount a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given of Deep Learning (DL) techniques have been developed and activity can be performed. Using features that clearly separate successful applied in recognition tasks [2], [28]. These tech- between activities is crucial. In this paper, we propose an niques allow an automatic extraction of features without any approach to automatically extract discriminative features for domain knowledge. activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture In this work, we propose an approach based on Convolu- local dependency and scale invariance of a signal as it has been tional Neural Networks (CNN) [2] to recognize activities in shown in speech recognition and image recognition domains.
    [Show full text]
  • Activity Recognition Using Cell Phone Accelerometers
    Activity Recognition using Cell Phone Accelerometers Jennifer R. Kwapisz, Gary M. Weiss, Samuel A. Moore Department of Computer and Information Science Fordham University 441 East Fordham Road Bronx, NY 10458 {kwapisz, gweiss, asammoore}@cis.fordham.edu ABSTRACT Mobile devices are becoming increasingly sophisticated and the 1. INTRODUCTION Mobile devices, such as cellular phones and music players, have latest generation of smart cell phones now incorporates many recently begun to incorporate diverse and powerful sensors. These diverse and powerful sensors. These sensors include GPS sensors, sensors include GPS sensors, audio sensors (i.e., microphones), vision sensors (i.e., cameras), audio sensors (i.e., microphones), image sensors (i.e., cameras), light sensors, temperature sensors, light sensors, temperature sensors, direction sensors (i.e., mag- direction sensors (i.e., compasses) and acceleration sensors (i.e., netic compasses), and acceleration sensors (i.e., accelerometers). accelerometers). Because of the small size of these “smart” mo- The availability of these sensors in mass-marketed communica- bile devices, their substantial computing power, their ability to tion devices creates exciting new opportunities for data mining send and receive data, and their nearly ubiquitous use in our soci- and data mining applications. In this paper we describe and evalu- ety, these devices open up exciting new areas for data mining ate a system that uses phone-based accelerometers to perform research and data mining applications. The goal of our WISDM activity recognition, a task which involves identifying the physi- (Wireless Sensor Data Mining) project [19] is to explore the re- cal activity a user is performing. To implement our system we search issues related to mining sensor data from these powerful collected labeled accelerometer data from twenty-nine users as mobile devices and to build useful applications.
    [Show full text]
  • Deep Learning Based Human Activity Recognition Using Spatio-Temporal Image Formation of Skeleton Joints
    applied sciences Article Deep Learning Based Human Activity Recognition Using Spatio-Temporal Image Formation of Skeleton Joints Nusrat Tasnim 1, Mohammad Khairul Islam 2 and Joong-Hwan Baek 1,* 1 School of Electronics and Information Engineering, Korea Aerospace University, Goyang 10540, Korea; [email protected] 2 Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh; [email protected] * Correspondence: [email protected]; Tel.: +82-2-300-0125 Abstract: Human activity recognition has become a significant research trend in the fields of computer vision, image processing, and human–machine or human–object interaction due to cost-effectiveness, time management, rehabilitation, and the pandemic of diseases. Over the past years, several methods published for human action recognition using RGB (red, green, and blue), depth, and skeleton datasets. Most of the methods introduced for action classification using skeleton datasets are con- strained in some perspectives including features representation, complexity, and performance. How- ever, there is still a challenging problem of providing an effective and efficient method for human action discrimination using a 3D skeleton dataset. There is a lot of room to map the 3D skeleton joint coordinates into spatio-temporal formats to reduce the complexity of the system, to provide a more accurate system to recognize human behaviors, and to improve the overall performance. In this paper, we suggest a spatio-temporal image formation (STIF) technique of 3D skeleton joints by capturing spatial information and temporal changes for action discrimination. We conduct transfer learning (pretrained models- MobileNetV2, DenseNet121, and ResNet18 trained with ImageNet Citation: Tasnim, N.; Islam, M.K.; dataset) to extract discriminative features and evaluate the proposed method with several fusion Baek, J.-H.
    [Show full text]
  • Human Activity Recognition Using Deep Learning Models on Smartphones and Smartwatches Sensor Data
    Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data Bolu Oluwalade1, Sunil Neela1, Judy Wawira2, Tobiloba Adejumo3 and Saptarshi Purkayastha1 1Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, U.S.A. 2Department of Radiology, Imaging Sciences, Emory University, U.S.A. 3Federal University of Agriculture, Abeokuta, Nigeria Keywords: Human Activities Recognition (HAR), WISDM Dataset, Convolutional LSTM (ConvLSTM). Abstract: In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p < 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities.
    [Show full text]