Computational method for monitoring pauses exercises in office workers through a vision model

Herrera, Fabián A. Aldana Herrera Fabián A. Aldana; Niño, Rodrigo; Herrera‐Viedma, Enrique; Gaona-García, Paulo Alonso; Mendívil, Iñigo Sarría Martínez de; Crespo, Rubén González

doi:10.1007/s12652-020-02391-3

Cited by 8 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DNN (Ding and Fan, 2015), BLSTM, CNN and CNN-LSTM (Li, 2018; Kästner, 2013) are used as learning algorithms for the classification models. Verma (2020) has presented two-stage HAR using 2D-ConvNet, and various other researchers have presented the computational model for HAR (Siirtola and Röning, 2012; Herrera, 2020). The DNN takes an input of 80 × 3 Numpy array, and output is simply a one-hot vector containing seven binary values according to the activities classified.…”

Section: Proposed Methodology and Trajectories Generationmentioning

confidence: 99%

Wearable sensor-based pattern mining for human activity recognition: deep learning approach

Bijalwan¹,

Semwal

Gupta

2021

View full text Add to dashboard Cite

Purpose This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups. Design/methodology/approach In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%. Findings The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg. Practical implications This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities. Originality/value The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.

show abstract

Section: Proposed Methodology and Trajectories Generationmentioning

confidence: 99%

Wearable sensor-based pattern mining for human activity recognition: deep learning approach

Bijalwan¹,

Semwal

Gupta

2021

View full text Add to dashboard Cite

show abstract

“…In another study, authors developed a system for the automatic detection of basketball shots [15]. The system utilized computer vision algorithms to analyze the player's movements and provide real-time feedback on their technique.…”

Section: Related Workmentioning

confidence: 99%

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Doskarayev¹,

Omarov²,

Omarov³

et al. 2023

IJACSA

View full text Add to dashboard Cite

This research paper presents the development of computer vision-enabled augmented reality games based on action detection to increase motivation for sports. With the increasing popularity of digital games, physical activity and sports participation have been declining, especially among the younger generation. To address this issue, we developed a series of augmented reality games that require players to perform physical actions to progress and succeed in the game. These games were developed using computer vision technology to detect the players' movements and provide real-time feedback, enhancing the gaming experience and promoting physical activity. The results of our user study showed that participants who played the augmented reality games reported higher levels of motivation to engage in physical activity and sports. The findings suggest that computer vision-enabled augmented reality games can be an effective tool to promote physical activity and sports participation, especially among younger generations.

show abstract

“…In connection with the restricted daily regime of work and rest, a need arose to pay more attention to daily breaks of physical activity and training in the conditions of a hybrid workplace. A meticulously organised schedule of work, rest, and physical activity has a significant effect on relieving fatigue and increasing the body's resistance to various diseases (Herrera et al, 2021). According to the WHO and the International Labour Organisation, overtime work leads to serious outcomes of cardiovascular attacks.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is only physical activity at the workplace that facilitates both restoring and maintaining working capacity throughout the working hours. Using basic means of workout in industry, it is possible to combine care for health, maintenance of physical fitness, and high work capacity (Herrera, 2021). It is necessary to distinguish between the types of physical activity and prevention of the hypodynamic lifestyle.…”

Section: Introductionmentioning

confidence: 99%

“…Sedentary life and work can cause various common occupational diseases affecting muscular, skeletal, cardio-vascular, or sensory systems, but they can be prevented by taking active breaks. Software can help reduce the risk of occupational diseases caused by sedentary work in the office; for this purpose, web and mobile applications have been developed for active control of the work break, which is a proven practice in reducing morbidity (Herrera et al, 2021;. The role that technology can play in increasing the existing professional knowledge of workers is remarkable.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring of training activity as a determinant of people’s health in the COVID-19 pandemic crisis

2023

View full text Add to dashboard Cite

Reliable determination of metabolic and physical activity pa- rameters and influences of blood pressure indicators is relevant in the development of rehabilitation training programmes for people of all ages, especially in the context of the COVID-19 pan- demic and the aging of the working population. The purpose of the study was to determine reliable parameters that were shaping general health during the COVID-19 pandemic crisis in the age- related aspect. This study used the results of comprehensive health monitoring data of 603 adults aged 20–89 from the Human Health Passport online tool (Ukraine) for the period of 2020–2022. On- line health monitoring showed the consequences of mild forms of COVID-19 disease. The main indicators informing about this effect were the laboratory-method indicator for confirming a viral disease and the number of cases of mild forms of the dis- ease. The indirect influence of this factor on various indicators of health, physical activity, metabolism, indicators of blood pressure compensation should also be considered. As for the age aspect, the state of health had a higher influence on the indicators of body mass index (BMI), blood pressure and its correction with drugs, the daily walking distance and exercises performed, and a danger- ous loss of appetite. A noticeable drop in body weight and loss of appetite are reliable factors in the incidence of falls and decline in health. The leading factors shaping the health of the active popu- lation in mild forms of COVID-19 are physical activity (7.77%), metabolism (5.91%), and the blood pressure level (5.21%).

show abstract

Computational method for monitoring pauses exercises in office workers through a vision model

Cited by 8 publications

References 15 publications

Wearable sensor-based pattern mining for human activity recognition: deep learning approach

Wearable sensor-based pattern mining for human activity recognition: deep learning approach

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Monitoring of training activity as a determinant of people’s health in the COVID-19 pandemic crisis

Contact Info

Product

Resources

About