Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 409
Author(s):  
Giovanni Acampora ◽  
Gianluca Minopoli ◽  
Francesco Musella ◽  
Mariacarla Staffa
Keyword(s):  
Activity Recognition ◽  
Human Activities ◽  
Short Term Memory ◽  
Recognition Task ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Surveillance Systems ◽  
Research Activities ◽  
Iot Devices

Human activity recognition is a crucial task in several modern applications based on the Internet of Things (IoT) paradigm, from the design of intelligent video surveillance systems to the development of elderly robot assistants. Recently, machine learning algorithms have been strongly investigated to improve the recognition task of human activities. Though, in spite of these research activities, there are not so many studies focusing on the efficient recognition of complex human activities, namely transitional activities, and there is no research aimed at evaluating the effects of noise in data used to train algorithms. In this paper, we bridge this gap by introducing an innovative activity recognition system based on a neural classifier endowed with memory, able to optimize the performance of the classification of both transitional and non-transitional human activities. The system recognizes human activities from unobtrusive IoT devices (such as the accelerometer and gyroscope) integrated in commonly used smartphones. The main peculiarity provided by the proposed system is related to the exploitation of a neural network extended with short-term memory information about the previous activities’ features. The experimental study proves the reliability of the proposed system in terms of accuracy with respect to state-of-the-art classifiers and the robustness of the proposed framework with respect to noise in data.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6316
Author(s):  
Dinis Moreira ◽  
Marília Barandas ◽  
Tiago Rocha ◽  
Pedro Alves ◽  
Ricardo Santos ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Short Term Memory ◽  
Indoor Localization ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Location Based Services ◽  
Sensor Data ◽  
Indoor Environments

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.

scholarly journals An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1635
Author(s):  
Leyuan Liu ◽  
Jian He ◽  
Keyan Ren ◽  
Jonathan Lungu ◽  
Yibin Hou ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Information Gain ◽  
Recognition Task ◽  
Human Activity Recognition ◽  
Short Term ◽  
Activity Model ◽  
Long Short Term Memory ◽  
Selection Of

Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the same, which affects the promotion and application. In this paper, an information gain-based human activity model is established, and an attention-based recurrent neural network (namely Attention-RNN) for human activity recognition is designed. Besides, the attention-RNN, which combines bidirectional long short-term memory (BiLSTM) with attention mechanism, was tested on the UCI opportunity challenge dataset. Experiments prove that the proposed human activity model provides guidance for the deployment location of sensors and provides a basis for the selection of the number of sensors, which can reduce the number of sensors used to achieve the same classification effect. In addition, experiments show that the proposed Attention-RNN achieves F1 scores of 0.898 and 0.911 in the ML (Modes of Locomotion) task and GR (Gesture Recognition) task, respectively.

scholarly journals Deep-BEJT: A New Human Activity Recognition System based on Beta Elliptical Joint Trajectory (BEJT) and Long Short Term Memory (LSTM)

2021 ◽  
Author(s):  
Ghabri Sawsen ◽  
Wael Ouarda ◽  
Houcine Boubaker ◽  
Mohamed Moncef Ben Khelifa ◽  
Adel Alimi
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Journal Paper ◽  
Long Short Term Memory

Deep-BEJT: A New Human Activity Recognition System basedon Beta Elliptical Joint Trajectory (BEJT) and Long Short TermMemory (LSTM)<div>New journal paper</div>

scholarly journals Human Activity Recognition using Active Learning Methodology

2019 ◽  
Vol 8 (12S) ◽  
pp. 404-406
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Dictionary Learning ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Human Activity Recognition ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Surveillance Systems ◽  
Health Care Monitoring

In current technology, presenting detailed and exact information about one’s daily activities is the major task in artificial intelligence. This paper represents the multiple classification techniques used to monitor the behaviours of aging people. It can also play an important role in health care monitoring system and surveillance systems. Human Activity Recognition (HAR) dataset is used for evaluating and comparing the prediction accuracy of the dictionary learning algorithm, Naive Bayes and J48 algorithms. Based on the classification, J48 algorithm is superior compared to other classifier algorithms. J48 and Naïve Bayes machine learning algorithms are evaluated on WEKA tool and their efficiency is compared with Dictionary learning algorithm for achieving better results on the given dataset.

Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

2020 ◽  
Vol 12 (1) ◽  
pp. 36-48
Author(s):  
Vijayaprabakaran K. ◽  
Sathiyamurthy K. ◽  
Ponniamma M.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Elderly People ◽  
Activity Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Vision

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.

scholarly journals Fast Object Detection Using Dimensional Based Features for Public Street Environments

Smart Cities ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 93-111 ◽  
Author(s):  
Ivan Matveev ◽  
Kirill Karpov ◽  
Ingo Chmielewski ◽  
Eduard Siemens ◽  
Aleksey Yurchenko
Keyword(s):  
Low Power ◽  
Near Infrared ◽  
Moving Objects ◽  
Infrared Camera ◽  
Machine Learning Algorithms ◽  
Frame Rate ◽  
Raspberry Pi ◽  
Real World Data ◽  
Iot Devices

Modern object recognition algorithms have very high precision. At the same time, they require high computational power. Thus, widely used low-power IoT devices, which gather a substantial amount of data, cannot directly apply the corresponding machine learning algorithms to process it due to the lack of local computational resources. A method for fast detection and classification of moving objects for low-power single-board computers is shown in this paper. The developed algorithm uses geometric parameters of an object as well as scene-related parameters as features for classification. The extraction and classification of these features is a relatively simple process which can be executed by low-power IoT devices. The algorithm aims to recognize the most common objects in the street environment, e.g., pedestrians, cyclists, and cars. The algorithm can be applied in the dark environment by processing images from a near-infrared camera. The method has been tested on both synthetic virtual scenes and real-world data. The research showed that a low-performance computing system, such as a Raspberry Pi 3, is able to classify objects with acceptable frame rate and accuracy.

scholarly journals Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition

2020 ◽  
Vol 10 (15) ◽  
pp. 5293 ◽  
Author(s):  
Rebeen Ali Hamad ◽  
Longzhi Yang ◽  
Wai Lok Woo ◽  
Bo Wei
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Short Term Memory ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Temporal Models ◽  
Wide Range

Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities.

scholarly journals Deep-BEJT: A New Human Activity Recognition System based on Beta Elliptical Joint Trajectory (BEJT) and Long Short Term Memory (LSTM)

2021 ◽  
Author(s):  
Ghabri Sawsen ◽  
Wael Ouarda ◽  
Houcine Boubaker ◽  
Mohamed Moncef Ben Khelifa ◽  
Adel Alimi
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Journal Paper ◽  
Long Short Term Memory

Deep-BEJT: A New Human Activity Recognition System basedon Beta Elliptical Joint Trajectory (BEJT) and Long Short TermMemory (LSTM)<div>New journal paper</div>

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