scholarly journals Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Huaijun Wang ◽  
Jing Zhao ◽  
Junhuai Li ◽  
Ling Tian ◽  
Pengjia Tu ◽  
...  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Specific Activity ◽  
Recognition Rate ◽  
Human Activity Recognition ◽  
Wearable Sensor ◽  
Identification Rate

Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.

scholarly journals On-Device Deep Learning Inference for Efficient Activity Data Collection

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3434 ◽  
Author(s):  
Nattaya Mairittha ◽  
Tittaya Mairittha ◽  
Sozo Inoue
Keyword(s):  
Deep Learning ◽  
Data Collection ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Classification Model ◽  
Activity Data ◽  
Ground Truth Data ◽  
Recognition Systems

Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. To enable us to perform evaluations, we conduct the experiments with two conditional methods. We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications. By evaluating with the dataset gathered, the results show our proposed method has improvements in both data quality (i.e., the performance of a classification model) and data quantity (i.e., the number of data collected) that reflect our method could improve activity data collection, which can enhance human activity recognition systems. We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish the preliminary dataset collected to the research community for activity recognition.

scholarly journals Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1716 ◽  
Author(s):  
Seungeun Chung ◽  
Jiyoun Lim ◽  
Kyoung Ju Noh ◽  
Gague Kim ◽  
Hyuntae Jeong
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Sensor Fusion ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Sampling Rate ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Activity Data

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities.

scholarly journals Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yu Zhao ◽  
Rennong Yang ◽  
Guillaume Chevalier ◽  
Ximeng Xu ◽  
Zhenxing Zhang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Network Architecture ◽  
Short Term Memory ◽  
Wearable Sensors ◽  
Recognition Rate ◽  
Human Activity Recognition ◽  
Time Direction ◽  
Positive Time ◽  
New Ideas

Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked) dimensions, aiming to enhance the recognition rate. When testing with the Opportunity dataset and the public domain UCI dataset, the accuracy is significantly improved compared with previous results.

IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment

2019 ◽  
Vol 6 (5) ◽  
pp. 8553-8562 ◽  
Author(s):  
Valentina Bianchi ◽  
Marco Bassoli ◽  
Gianfranco Lombardo ◽  
Paolo Fornacciari ◽  
Monica Mordonini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Home Environment ◽  
Smart Home ◽  
Integrated Approach ◽  
Human Activity Recognition ◽  
Wearable Sensor ◽  
Smart Home Environment

scholarly journals Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2141
Author(s):  
Ohoud Nafea ◽  
Wadood Abdul ◽  
Ghulam Muhammad ◽  
Mansour Alsulaiman
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Effective Selection ◽  
Spatio Temporal ◽  
High Level ◽  
Improved Accuracy

Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the development of deep learning, automatic high-level feature extraction has become a possibility and has been used to optimize HAR performance. Furthermore, deep-learning techniques have been applied in various fields for sensor-based HAR. This study introduces a new methodology using convolution neural networks (CNN) with varying kernel dimensions along with bi-directional long short-term memory (BiLSTM) to capture features at various resolutions. The novelty of this research lies in the effective selection of the optimal video representation and in the effective extraction of spatial and temporal features from sensor data using traditional CNN and BiLSTM. Wireless sensor data mining (WISDM) and UCI datasets are used for this proposed methodology in which data are collected through diverse methods, including accelerometers, sensors, and gyroscopes. The results indicate that the proposed scheme is efficient in improving HAR. It was thus found that unlike other available methods, the proposed method improved accuracy, attaining a higher score in the WISDM dataset compared to the UCI dataset (98.53% vs. 97.05%).

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.

Smart Healthcare for ECG Telemonitoring System

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Jwan Najeeb Saeed ◽  
◽  
Siddeeq Y. Ameen ◽  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Feature Learning ◽  
Human Activity Recognition ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
Smart Health

Cardiovascular disorders are one of the major causes of sad death among older and middle-aged people. Over the past two decades, health monitoring services have evolved quickly and had the ability to change the way health care is currently provided. However, the most challenging aspect of the mobile and wearable sensor-based human activity recognition pipeline is the extraction of the related features. Feature extraction decreases both computational complexity and time. Deep learning techniques are used for automatic feature learning in a variety of fields, including health, image classification, and, most recently, for the extraction and classification of complex and straightforward human activity recognition in smart health care. This paper reviews the recent state of the art in electrocardiogram (ECG) smart health monitoring systems based on the Internet of things with the machine and deep learning techniques. Moreover, the paper provided possible research and challenges that can help researchers advance state of art in future work.

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