scholarly journals ezTrack—A Step‐by‐Step Guide to Behavior Tracking

2021 ◽  
Vol 1 (10) ◽  
Author(s):  
Zachary T. Pennington ◽  
Keziah S. Diego ◽  
Taylor R. Francisco ◽  
Alexa R. LaBanca ◽  
Sophia I. Lamsifer ◽  
...  
Keyword(s):  
Behavior Tracking

Weight and Physical Activity: a 12-month follow-up of a digital diabetes prevention program (VP Transform for Prediabetes) in adults with prediabetes (Preprint)

2020 ◽  
Author(s):  
Ryan Batten ◽  
Meshari F Alwashmi ◽  
Gerald Mugford ◽  
Misa Muccio ◽  
Angele Besner ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Secondary Analysis ◽  
Diabetes Prevention Program ◽  
Global Scale ◽  
Lifestyle Modifications ◽  
Behavior Tracking

BACKGROUND The prevalence of diabetes increasingly rapidly. Previous research has demonstrated the efficacy of a diabetes prevention program (DPP) in lifestyle modifications which can prevent or delay the onset of type 2 diabetes among individuals at-risk. Digital DPPs have the potential to utilize technology, in conjunction with behavior change science, to prevent prediabetes on a national and global scale OBJECTIVE The aim of this study was to investigate the effects of a digital DPP (VP Transform for Prediabetes) on weight loss and physical activity among participants who had completed twelve months of the program. METHODS This study was a secondary analysis of retrospective data of adults with prediabetes who were enrolled in VP Transform for Prediabetes for 12 months of the program. The program incorporates interactive mobile computing, remote monitoring, an evidence-based curriculum, behavior tracking tools, health coaching and online peer support to prevent or delay the onset of type 2 diabetes. Analysis included data that were collected at baseline and after 12 months of the VP Transform for Prediabetes DPP. RESULTS The sample (N=1,095) comprised people with prediabetes who completed 12 months of the VP Transform for Prediabetes program. Participants included 67.7% female, with a mean age of 53.6 (SD 9.75). On average, participants decreased their weight by 10.9 pounds (5.5%) and increased their physical activity by 91.2 minutes per week. CONCLUSIONS These results suggest that VP Transform for Prediabetes is effective at preventing type 2 diabetes through significant reduction in body weight and increase of physical activity. Furthermore, these results suggest that the DPP remains effective 12 months after beginning the program. A prospective, controlled clinical study is warranted to validate these findings.

scholarly journals Emotion Classification Using a Tensorflow Generative Adversarial Network Implementation

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 414 ◽  
Author(s):  
Traian Caramihale ◽  
Dan Popescu ◽  
Loretta Ichim
Keyword(s):  
Assisted Living ◽  
Generative Adversarial Networks ◽  
Generation Process ◽  
Emotion Classification ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Testing Dataset ◽  
Behavior Tracking ◽  
Domestic Appliance ◽  
Facial Images

The detection of human emotions has applicability in various domains such as assisted living, health monitoring, domestic appliance control, crowd behavior tracking real time, and emotional security. The paper proposes a new system for emotion classification based on a generative adversarial network (GAN) classifier. The generative adversarial networks have been widely used for generating realistic images, but the classification capabilities have been vaguely exploited. One of the main advantages is that by using the generator, we can extend our testing dataset and add more variety to each of the seven emotion classes we try to identify. Thus, the novelty of our study consists in increasing the number of classes from N to 2N (in the learning phase) by considering real and fake emotions. Facial key points are obtained from real and generated facial images, and vectors connecting them with the facial center of gravity are used by the discriminator to classify the image as one of the 14 classes of interest (real and fake for seven emotions). As another contribution, real images from different emotional classes are used in the generation process unlike the classical GAN approach which generates images from simple noise arrays. By using the proposed method, our system can classify emotions in facial images regardless of gender, race, ethnicity, age and face rotation. An accuracy of 75.2% was obtained on 7000 real images (14,000, also considering the generated images) from multiple combined facial datasets.

ARM-based Behavior Tracking and Identification System for Grouphoused Pigs

2019 ◽  
Vol 12 (6) ◽  
pp. 554-565
Author(s):  
Xingqiao Liu ◽  
Jun Xuan ◽  
Fida Hussain ◽  
Chen Chong ◽  
Pengyu Li
Keyword(s):  
Monitoring System ◽  
Complete Information ◽  
Recognition Algorithm ◽  
Detection Methods ◽  
Identification System ◽  
Monitoring Method ◽  
Embedded Platform ◽  
Food And Water Intake ◽  
Behavior Tracking

Background: A smart monitoring system is essential to improve the quality of pig farming. A real-time monitoring system provides growth, health and food information of pigs while the manual monitoring method is inefficient and produces stress on pigs, and the direct contact between human and pig body increases diseases. Methods: In this paper, an ARM-based embedded platform and image recognition algorithms are proposed to monitor the abnormality of pigs. The proposed approach provides complete information on in-house pigs throughout the day such as eating, drinking, and excretion behaviors. The system records in detail each pig's time to eat and drink, and the amount of food and water intake. Results: The experimental results show that the accuracy of the proposed method is about 85%, and the effect of the technique has a significant advantage over traditional behavior detection methods. Conclusion: Therefore, the ARM-based behavior recognition algorithm has certain reference significance for the fine group aquaculture industry. The proposed approach can be used for a central monitoring system.

Driving Behavior Tracking and Recognition Based on Multisensors Data Fusion

2020 ◽  
Vol 20 (18) ◽  
pp. 10811-10823 ◽  
Author(s):  
Long Liu ◽  
Zhelong Wang ◽  
Sen Qiu
Keyword(s):  
Data Fusion ◽  
Driving Behavior ◽  
Behavior Tracking ◽  
Tracking And Recognition

scholarly journals Validation of an integrated pedal desk and electronic behavior tracking platform

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
John M. Schuna ◽  
Catrine Tudor-Locke ◽  
Mahara Proença ◽  
Tiago V. Barreira ◽  
Daniel S. Hsia ◽  
...  
Keyword(s):  
Behavior Tracking ◽  
Electronic Behavior

User behavior tracking for Educational assist by using RGB-D sensor

2019 ◽  
Vol 2019 (0) ◽  
pp. 2P2-D08
Author(s):  
Haibin Xia ◽  
Bin Zhang ◽  
Hunok Lim ◽  
Tomoaki Nakamura ◽  
Takayuki Nagai ◽  
...  
Keyword(s):  
User Behavior ◽  
Behavior Tracking
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