Human activity recognition by combining external features with accelerometer sensor data using deep learning network model

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.

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Deep Learning Analysis of Location Sensor Data for Human‐Activity Recognition

10.1002/9781119682806.ch6 ◽

2021 ◽

pp. 103-116

Author(s):

Hariprasath Manoharan ◽

Ganesan Sivarajan ◽

Subramanian Srikrishna

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Sensor Data ◽

Learning Analysis ◽

Location Sensor

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Classifying Imbalanced Multi-modal Sensor Data for Human Activity Recognition in a Smart Home using Deep Learning

2020 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn48605.2020.9207697 ◽

2020 ◽

Author(s):

Ali A. Alani ◽

Georgina Cosma ◽

Aboozar Taherkhani

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Smart Home ◽

Human Activity Recognition ◽

Sensor Data

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An end-to-end deep learning model for human activity recognition from highly sparse body sensor data in Internet of Medical Things environment

The Journal of Supercomputing ◽

10.1007/s11227-020-03361-4 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mohammad Mehedi Hassan ◽

Sana Ullah ◽

M. Shamim Hossain ◽

Abdulhameed Alelaiwi

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Learning Model ◽

Human Activity Recognition ◽

Sensor Data ◽

Internet Of Medical Things ◽

End To End ◽

Deep Learning Model

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

Sensors ◽

10.3390/s21062141 ◽

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%).

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

Applied Sciences ◽

10.3390/app10155293 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5293 ◽

Cited By ~ 1

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.

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