Performance Analysis of Deep Learning based Human Activity Recognition Methods (original) (raw)
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International Journal of Machine Learning and Computing
Human activity recognition (HAR) has been a popular fields of research in recent times. Many approaches have been implemented in literature with the aim of recognizing and analyzing human activity. Classical machine learning approaches use hand-crafted feature extraction and are based on classification technique, however of late, deep learning approaches have shown greater success in recognition accuracy with increased performance. With the current, wide popularity of mobile phones and various sensors such as accelerometers, gyroscopes, and cameras that are already installed on mobile phones, the activity recognition using the accumulating data from mobile phones has been a significant area of research in HAR. In this paper, we investigate the HAR based on the data collected through the accelerometer sensor of mobile devices. We employ different machine learning (ML) classifiers, algorithms, and deep learning (DL) models across different benchmark datasets. The experimental results from this study provide a comparative performance analysis based on accuracy, performance, and the costs of different ML algorithms and DL algorithms, based on recurrent neural network (RNN) and convolutional neural network (CNN) models for activity recognition.
A Close Look into Human Activity Recognition Models using Deep Learning
2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)
Human activity recognition using deep learning techniques has become increasing popular because of its high effectivity with recognizing complex tasks, as well as being relatively low in costs compared to more traditional machine learning techniques. This paper surveys some state-of-the-art human activity recognition models that are based on deep learning architecture and has layers containing Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), or a mix of more than one type for a hybrid system. The analysis outlines how the models are implemented to maximize its effectivity and some of the potential limitations it faces.
International Journal of Computing and Digital Systems, 2022
Human Activity Recognition (HAR) is a vital area of Computer Vision. HAR focuses on various activities carried out by humans. Information relative to the human activities is collected by smart sensors and wearable devices. HAR is classified into two categories, e.g. (a) Vision-based, i.e. human activities are captured in form of image and video and (b) Sensor-based, i.e. human activity input can be taken from wearable devices and object tagging techniques. Human activity recognition is an extensive thrust area for Content-based video analysis, Human-machine interaction, animation, healthcare fields. The paper presents a comprehensive analysis of various deep learning-based approaches adopted to implement human activity recognition based on accuracy. It is observed that for the vision-based category the performance of the Depth Camera-based Recurrent Neural Network model is 99.55% accuracy with 12 activities for MSRC-12 datasets and for the sensor-based category, the performance of HAR by Wearable sensors using Deep Neural Network model is 99.93% accuracy with 03 activities for SHO datasets. It is also observed that for Opportunity dataset, InnoHAR: A DNN for complex HAR model gives good performance with 94.6% accuracy along with 18 activities, for PAMAP2 dataset, Multi-input CNN-GRU model gives good performance with 95.27% accuracy along with 12 activities, for WISDM dataset, ConvAE-LSTM model gives good performance with 98.67% accuracy along with 6 activities, and for UCI-HAR dataset, ConvAE-LSTM model gives good performance with 98.14% accuracy along with 6 activities.
Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks
Human physical activity recognition based on wearable sensors has applications relevant to our daily life such as healthcare. How to achieve high recognition accuracy with low computational cost is an important issue in the ubiquitous computing. Rather than exploring handcrafted features from time-series sensor signals, we assemble signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to automatically learn the optimal features from the activity image for the activity recognition task. Our proposed approach is evaluated on three public datasets and it outperforms state-of-the-arts in terms of recognition accuracy and computational cost.
Deep Architectures for Human Activity Recognition using Sensors
3C Tecnología_Glosas de innovación aplicadas a la pyme, 2019
Human activity recognition (HAR) is a renowned research field in recent years due to its applications such as physical fitness monitoring, assisted living, elderly-care, biometric authentication and many more. The ubiquitous nature of sensors makes them a good choice to use for activity recognition. The latest smart gadgets are equipped with most of the wearable sensors i.e. accelerometer, gyroscope, GPS, compass, camera, microphone etc. These sensors measure various aspects of an object, and are easy to use with less cost. The use of sensors in the field of HAR opens new avenues for machine learning (ML) researchers to accurately recognize human activities. Deep learning (DL) is becoming popular among HAR researchers due to its outstanding performance over conventional ML techniques. In this paper, we have reviewed recent research studies on deep models for sensor-based human activity recognition. The aim of this article is to identify recent trends and challenges in HAR.
Deep learning approaches for human activity recognition using wearable technology
Medicinski podmladak
The need for long-term monitoring of individuals in their natural environment has initiated the development of a various number of wearable healthcare sensors for a wide range of applications: medical monitoring in clinical or home environments, physical activity assessment of athletes and recreators, baby monitoring in maternity hospitals and homes etc. Neural networks (NN) are data-driven type of modelling. Neural networks learn from experience, without knowledge about the model of phenomenon, but knowing the desired "output" data for the training "input" data. The most promising concept of machine learning that involves NN is the deep learning (DL) approach. The focus of this review is on approaches of DL for physiological activity recognition or human movement analysis purposes, using wearable technologies. This review shows that deep learning techniques are useful tools for health condition prediction or overall monitoring of data, streamed by wearable systems. Despite the considerable progress and wide field of applications, there are still some limitations and room for improvement of DL approaches for wearable healthcare systems, which may lead to more robust and reliable technology for personalized healthcare.
2022
Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with t...
An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones
Sensors
Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architectu...
Tutorial on Deep Learning for Human Activity Recognition
ArXiv, 2021
Activity recognition systems that are capable of estimating human activities from wearable inertial sensors have come a long way in the past decades. Not only have state-of-the-art methods moved away from feature engineering and have fully adopted end-to-end deep learning approaches, best practices for setting up experiments, preparing datasets, and validating activity recognition approaches have similarly evolved. This tutorial was first held at the 2021 ACM International Symposium on Wearable Computers (ISWC’21) and International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’21). The tutorial, after a short introduction in the research field of activity recognition, provides a hands-on and interactive walk-through of the most important steps in the data pipeline for the deep learning of human activities. All presentation slides shown during the tutorial, which also contain links to all code exercises, as well as the link of the GitHub page of the tutorial can be ...