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Human Activities

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Deep Learning-Based Subject Independent Human Activity Recognition using Smart Lacelock Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Human Activity Recognition (HAR) field is rapidly growing and the classification of human activities based on sensor data is crucial for applications in healthcare, rehabilitation and numerous other sectors. In this paper we use a novel device and at...

Overcoming Data Scarcity in Human Activity Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Wearable sensors have become increasingly popular in recent years, with technological advances leading to cheaper, more widely available, and smaller devices. As a result, there has been a growing interest in applying machine learning techniques for ...

HAKE: A Knowledge Engine Foundation for Human Activity Understanding.

IEEE transactions on pattern analysis and machine intelligence
Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis. Although there have been advances with deep learning, it remains challenging. The object recognit...

Single Accelerometer to Recognize Human Activities Using Neural Networks.

Journal of biomechanical engineering
Exoskeletons have decreased physical effort and increased comfort in activities of daily living (ADL) such as walking, squatting, and running. However, this assistance is often activity specific and does not accommodate a wide variety of different ac...

A multimodal fusion enabled ensemble approach for human activity recognition in smart homes.

Health informatics journal
How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful ...

Human activity recognition for analyzing stress behavior based on Bi-LSTM.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Stress is one of the critical health factors that could be detected by Human Activity Recognition (HAR) which consists of physical and mental health. HAR can raise awareness of self-care and prevent critical situations. Recently, HAR used...

Exploring human activity recognition using feature level fusion of inertial and electromyography data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Wearables are objective tools for human activity recognition (HAR). Advances in wearables enable synchronized multi-sensing within a single device. This has resulted in studies investigating the use of single or multiple wearable sensor modalities fo...

A comparative study on recognizing human activities by applying diverse Machine Learning approaches.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper deals with the problem of identifying and recognizing everyday human activities. The main goal is to compare a variety of implemented classification models founded on diverse machine learning approaches; one that utilizes features extracte...

RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition.

Mathematical biosciences and engineering : MBE
Currently, identification of complex human activities is experiencing exponential growth through the use of deep learning algorithms. Conventional strategies for recognizing human activity generally rely on handcrafted characteristics from heuristic ...

Design and optimization of a TensorFlow Lite deep learning neural network for human activity recognition on a smartphone.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Human Activity Recognition (HAR), using machine learning to identify times spent (for example) walking, sitting, and standing, is widely used in health and wellness wearable devices, in ambient assistant living devices, and in rehabilitation. In this...