Data Mining Approach to HAR (original) (raw)
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Human Activity Recognition System
IRJET, 2022
Human activity recognition is a capability to interpret natural body gesture or movement via detectors and arbitrate natural exercise or action. Utmost of the human day-today chores can be streamlined or automated if they can be recognized via HAR system. Generally, HAR system can be additionally supervised or unsupervised. A supervised HAR system needed some previous training with constant datasets while unsupervised HAR network is being configured ground rules during development. HAR is accounted as an important element in varied scientific exploration surrounds i.e., supervision, healthcare and human computer interaction (HCI). This paper will present you an elaborate depiction and comparison of the styles, namely CNN and RNN, that can be applied to develop a Human Activity Recognition System.
Sensor-Based Human Activity Recognition: Challenges Ahead
2020
Human Activity Recognition (HAR) has explored a lot recently in the academia and industries for numerous applications. There are lots of progress in the domain of vision-based action or activity recognition due to the advent of deep learning models and due to the availability of very large datasets in the last several years. However, there are still a number of genuine challenges in vision-based HAR. On the other hand, sensor-based HAR has more constraints to decipher, and is still far from maturity due to various challenges. These core challenging issues are addressed in this chapter. The challenges regarding data collection issues have been discussed in detail. Prospective research works and challenges in the field of sensor-based activity recognition have been discussed in terms of new researchers perspective, possible developments in industries for experts, and smart IoT solutions in the medical sector.
Applications and Challenges of Human Activity Recognition Using Sensors In A Smart Environment
We are currently using smart phone sensors to detect physical activities. The sensors which are currently being used are accelerometer, gyroscope, barometer, etc. Recently, smart phones, equipped with a rich set of sensors, are explored as alternative platforms for human activity recognition. Automatic recognition of physical activities – commonly referred to as human activity recognition (HAR) – has emerged as a key research area in human-computer interaction (HCI) and mobile and ubiquitous computing. One goal of activity recognition is to provide information on a user’s behavior that allows computing systems to proactively assist users with their tasks. Human activity recognition requires running classification algorithms, originating from statistical machine learning techniques. Mostly, supervised or semi-supervised learning techniques are utilized and such techniques rely on labeled data, i.e., associated with a specific class or activity. In most of the cases, the user is required to label the activities and this, in turn, increases the burden on the user. Hence, user- independent training and activity recognition are required to foster the use of human activity recognition systems where the system can use the training data from other users in classifying the activities of a new subject.
The dataset is taken from UCI Repository named as Heterogeneity Activity Recognition DataSet. The data is characterized as Multivariate and is used for an associated task like Clustering and classification. The dataset contains the readings of two motion sensors commonly found in smart-phones. Reading were recorded while users executed activities scripted in no specific order carrying smartwatches and smartphones. We need to recognize the human activities of the different user by using Classification and clustering techniques.
A Novel Approach for Machine Learning-Based Identification of Human Activities
IRJET, 2023
Human activity recognition (HAR) is a rapidly growing field of research that uses machine learning to automatically identify and classify human activities from sensor data. This data can be collected from a variety of sources, such as wearable sensors, smartphones, and video cameras. HAR has a wide range of potential applications, including healthcare, sports, and security. In this paper, we present a comprehensive overview of the state-of-the-art in HAR using machine learning based on datasets. We discuss the various feature extraction techniques that can be applied, and the different machine learning algorithms that can be used for model training. We also present a survey of the recent literature on HAR using machine learning, and we discuss the challenges and opportunities that lie ahead in this field. Our findings suggest that HAR using machine learning based on datasets is a promising approach for a variety of applications. However, there are still a number of challenges that need to be addressed in order to improve the accuracy and robustness of HAR systems. These challenges include the need for more accurate and efficient feature extraction techniques, the development of more powerful machine learning algorithms, and the creation of larger and more diverse datasets. We believe that this paper provides a valuable contribution to the field of HAR using machine learning. It provides a comprehensive overview of the stateof-the-art, and it identifies the challenges and opportunities that lie ahead. We hope that this paper will help to accelerate the development of more accurate and reliable HAR systems that can be used to improve the lives of people in a variety of ways.
Human Activity Recognition: Challenges and Process Stages
International Journal of Innovative Research in Computer and Communication Engineering, 2016
With the wide range of applications in vision based intelligent systems, the attention of researchers in the computer vision field have attracted by image and video analysis technologies. Despite the diversity of computer vision researches, few literature reviews have been proposed to monitor people and recognize their activities. This paper focus in the literature reviews on the generic process stages of Human Activity Recognition (HAR) which include: data acquisition from the sensor, Preprocessing, Segmentation, Feature extraction and, training and classification. The challenges corresponding with activity recognition also will be listed.