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 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 Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Mohammad Iqbal ◽  
Chandrawati Putri Wulandari ◽  
Wawan Yunanto ◽  
Ghaluh Indah Permata Sari
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Activity Patterns ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Sequential Patterns ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Human Activity Patterns

Discovering rare human activity patterns—from triggered motion sensors deliver peculiar information to notify people about hazard situations. This study aims to recognize rare human activities using mining non-zero-rare sequential patterns technique. In particular, this study mines the triggered motion sensor sequences to obtain non-zero-rare human activity patterns—the patterns which most occur in the motion sensor sequences and the occurrence numbers are less than the pre-defined occurrence threshold. This study proposes an algorithm to mine non-zero-rare pattern on human activity recognition called Mining Multi-class Non-Zero-Rare Sequential Patterns (MMRSP).  The experimental result showed that non-zero-rare human activity patterns succeed to capture the unusual activity. Furthermore, the MMRSP performed well according to the precision value of rare activities.

scholarly journals Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6316
Author(s):  
Dinis Moreira ◽  
Marília Barandas ◽  
Tiago Rocha ◽  
Pedro Alves ◽  
Ricardo Santos ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Short Term Memory ◽  
Indoor Localization ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Location Based Services ◽  
Sensor Data ◽  
Indoor Environments

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.

Can You See It?

2021 ◽  
Vol 25 (2) ◽  
pp. 38-42
Author(s):  
Hyeokhyen Kwon ◽  
Catherine Tong ◽  
Harish Haresamudram ◽  
Yan Gao ◽  
Gregory D. Abowd ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensors ◽  
Human Subjects ◽  
Wearable Devices ◽  
Human Activity Recognition ◽  
Training Data ◽  
Considerable Time ◽  
Administrative Work ◽  
Everyday Activities

Today's smartphones and wearable devices come equipped with an array of inertial sensors, along with IMU-based Human Activity Recognition models to monitor everyday activities. However, such models rely on large amounts of annotated training data, which require considerable time and effort for collection. One has to recruit human subjects, define clear protocols for the subjects to follow, and manually annotate the collected data, along with the administrative work that goes into organizing such a recording.

Sign in / Sign up

Export Citation Format

Share Document