Proceedings ArticleDOI
Wearable Sensors for Activity Analysis using SMO-based Random Forest over Smart home and Sports Datasets
Sheikh Badar ud din Tahir,Ahmad Jalal,Mouazma Batool +2 more
- pp 1-6
TLDR
Experimental results show that the proposed model can compete with other state-of-the-art methods and can be effectively used to recognize robust human activities in terms of efficiency and accuracy.Abstract:
Human activity recognition using MotionNode sensors is getting prominence effect in our daily life logs. Providing accurate information on human's activities and behaviors is one of the most challenging tasks in ubiquitous computing and human-Computer interaction. In this paper, we proposed an efficient model for having statistical features along SMO-based random forest. Initially, we processed a 1-D Hadamard transform wavelet and 1-D LBP based extraction algorithm to extract valuable features. For activity classification, we used sequential minimal optimization along with Random Forest over two benchmarks USC-HAD dataset and IMSB datasets. Experimental results show that our proposed model can compete with other state-of-the-art methods and can be effectively used to recognize robust human activities in terms of efficiency and accuracy.read more
Citations
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Journal ArticleDOI
A Comprehensive Survey on Deep Neural Networks for Stock Market: The Need, Challenges, and Future Directions
Ankit Thakkar,Kinjal Chaudhari +1 more
TL;DR: This article aims to review the significance and need of DNNs in the field of stock price and trend prediction, and discusses the applicability ofDNN variations to the temporal stock market data, and extends the survey to include hybrid, as well as metaheuristic, approaches withDNNs.
Journal ArticleDOI
Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors
TL;DR: A novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors is proposed, which will be applicable to a wide variety of man–machine interfaces.
Journal ArticleDOI
Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing
TL;DR: A novel HPE and SEC system for which a pseudo-2D stick model is proposed to extract full-body human silhouette features, and features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation.
Journal ArticleDOI
An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia
Pradeep Kumar Das,Sukadev Meher +1 more
TL;DR: An efficient deep CNNs framework is proposed to mitigate this issue and yield more accurate ALL detection, and a novel probability-based weight factor is suggested, which has a significant role in efficiently hybridizing MobilenetV2 and ResNet18 with preserving the benefits of both approaches.
Journal ArticleDOI
Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network
TL;DR: A novel method to classify stochastic remote sensing events and to perform adaptive posture estimation and a unified pseudo-2D stick model are proposed, which are superior compared to existing state-of-the-art methods.
References
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Proceedings ArticleDOI
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Mi Zhang,Alexander A. Sawchuk +1 more
TL;DR: The freely available USC human activity dataset (USC-HAD), consisting of well-defined low-level daily activities intended as a benchmark for algorithm comparison particularly for healthcare scenarios, is described.
Journal ArticleDOI
Robust human activity recognition from depth video using spatiotemporal multi-fused features
TL;DR: The experimental results on three challenging depth video datasets demonstrate that the proposed online HAR method using the proposed multi-fused features outperforms the state-of-the-art HAR methods in terms of recognition accuracy.
Journal ArticleDOI
A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors.
Jian Wu,Lu Sun,Roozbeh Jafari +2 more
TL;DR: A wearable system for recognizing ASL in real time is proposed, fusing information from an inertial sensor and sEMG sensors, and an information gain-based feature selection scheme is used to select the best subset of features from a broad range of well-established features.
Journal ArticleDOI
A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments.
TL;DR: A depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space and achieves satisfactory recognition rates against the conventional approaches.
Journal ArticleDOI
Human Activity Recognition via Recognized Body Parts of Human Depth Silhouettes for Residents Monitoring Services at Smart Home
TL;DR: This work presents a novel HAR methodology utilizing the recognized body parts of human depth silhouettes and Hidden Markov Models (HMMs) to train random forests (RFs) and performs HAR with the trained HMMs for six typical home activities.
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