scholarly journals Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3213 ◽  
Author(s):  
Wesllen Sousa Lima ◽  
Eduardo Souto ◽  
Khalil El-Khatib ◽  
Roozbeh Jalali ◽  
Joao Gama
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Scientific Community ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Current Situation ◽  
Physical Activities ◽  
Historical Events

The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

scholarly journals Study on Temperature (τ) Variation for SimCLR based Activity Recognition

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Inertial Sensors ◽  
Healthcare Setting ◽  
Human Activity Recognition ◽  
Stream Data ◽  
Loss Mechanism ◽  
Overall Performance ◽  
Physiological Sensors

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.

scholarly journals Study on Temperature Variance for SimCLR based Activity Recognition

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Inertial Sensors ◽  
Visual Representations ◽  
Human Activity Recognition ◽  
Stream Data ◽  
Temperature Variance ◽  
Learning Technique ◽  
Physiological Sensors

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. In this paper, we propose a robust SimCLR model for human activity recognition with a temperature variance study. In this work, SimCLR, a contrasting learning technique is optimized via regulating the temperature for visual representations, is incorporated for improving the HAR performance in healthcare.

scholarly journals HHARNet: Taking inspiration from Inception and Dense Networks for Human Activity Recognition using Inertial Sensors

2021 ◽  
Author(s):  
Hamza Ali Imran ◽  
Usama Latif
Keyword(s):  
Computer Vision ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Dense Networks

Human Activity Recognition (HAR) an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human computer interface, child care, education and many more. Use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper we have proposed a 1-dimensional Convolutions Neural Network which is inspired by two state-of-the art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, F1-measure and accuracies are reported.<br>

scholarly journals HHARNet: Taking inspiration from Inception and Dense Networks for Human Activity Recognition using Inertial Sensors

2021 ◽  
Author(s):  
Hamza Ali Imran ◽  
Usama Latif
Keyword(s):  
Computer Vision ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Dense Networks

Human Activity Recognition (HAR) an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human computer interface, child care, education and many more. Use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper we have proposed a 1-dimensional Convolutions Neural Network which is inspired by two state-of-the art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, F1-measure and accuracies are reported.<br>

scholarly journals Rate-Invariant Modeling in Lie Algebra for Activity Recognition

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1888
Author(s):  
Malek Boujebli ◽  
Hassen Drira ◽  
Makram Mestiri ◽  
Imed Riadh Farah
Keyword(s):  
Computer Vision ◽  
Lie Algebra ◽  
Activity Recognition ◽  
Human Activity ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
High Complexity ◽  
Human Motions ◽  
Inter Frame ◽  
Automatic Systems

Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. In this paper, we propose to model the inter-frame rigid evolution of skeleton parts as the trajectory in the Lie group SE(3)×…×SE(3). The motion of the object is similarly modeled as an additional trajectory in the same manifold. The classification is performed based on a rate-invariant comparison of the resulting trajectories mapped to a vector space, the Lie algebra. Experimental results on three action and activity datasets show that the proposed method outperforms various state-of-the-art human activity recognition approaches.

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