Development of Occupant Pose Classification Model Using Deep Neural Network for Personalized Thermal Conditioning

Choi, Eun Ji; Yoo, Yeon-Jee; Park, Bo Rang; Choi, Young Jae; Moon, Jin Woo

doi:10.3390/en13010045

Cited by 14 publications

(3 citation statements)

References 8 publications

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“…WOA algorithm is combined with BP neural network, and WOA algorithm is used to adjust the parameters of BP neural network. Then the RSSI value data and corresponding parameters collected at different distances are taken as the input value of BP neural network, and the coordinates of unknown nodes are taken as the output value of BP neural network, so as to establish WOA-BP neural network model and complete the node positioning [21][22][23][24][25][26].…”

Section: Using Woa-bp To Optimize Indoor Environment Attenuation Modelmentioning

confidence: 99%

A Node Localization Algorithm based on WoaBp Optimization

Lang¹,

Sun²,

Jun³

et al. 2021

IJWMT

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With the rapid development of 5G technology, the era of interconnection of all things has arrived. At the same time, a variety of hardware and software are getting more and more location information through sensors, and the accuracy of location information is increasingly important. Because traditional positioning relies on satellite signals, it achieves good results outdoors without obstruction, but indoors, due to the obstruction of various walls, such as Beidou satellite navigation system and U.S. Global Positioning System, it is difficult to meet the accuracy requirements for indoor positioning. Therefore, how to improve the positioning accuracy of indoor nodes has become a research hotspot in the field of wireless sensor. In order to improve the indoor positioning accuracy, this paper combines artificial neural network, intelligent optimization algorithm and node positioning to improve the accuracy of indoor positioning. One of the essences of the neural network is to solve the regression problem. Through the analysis of indoor node positioning, it can be concluded that the accuracy of distance-based positioning method lies in finding the relationship between signal strength and distance value. Therefore, the neural network can be used to regression analysis of signal strength and distance value and generate related models. In order to further improve the accuracy and stability of indoor node positioning, a method combining whale optimization algorithm with neural network is proposed. By using the whale optimization algorithm to find the optimal parameters of the neural network model, the training accuracy and speed of the neural network are improved. Then, using the excellent fitting ability of the neural network, the mapping relationship between RSSI value and distance value of indoor nodes is fitted, and the corresponding regression analysis model is generated, which can minimize the noise problem caused by abnormal signal attenuation and reduce the indoor positioning error. Finally, the data is processed by the neural network to get the parameters needed in the positioning algorithm. The experimental results show that the node positioning model based on the optimized neural network and the single optimization algorithm has significantly improved the positioning accuracy and stability.

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Section: Using Woa-bp To Optimize Indoor Environment Attenuation Modelmentioning

confidence: 99%

A Node Localization Algorithm based on WoaBp Optimization

Lang¹,

Sun²,

Jun³

et al. 2021

IJWMT

View full text Add to dashboard Cite

show abstract

“…These properties have made them very popular today for studying regression and classification problems of different scope [27,28]. They have proven to be effective in many health and monitoring related applications, and using samples of different types as inputs, such as spatial samples taken in an instant of time [29,30], sample characteristics obtained after a frequency analysis [31][32][33], or as a result of applying convolutional operations [34,35].…”

Section: Introductionmentioning

confidence: 99%

IoT Device for Sitting Posture Classification Using Artificial Neural Networks

et al. 2021

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Nowadays, the percentage of time that the population spends sitting has increased substantially due to the use of computers as the main tool for work or leisure and the increase in jobs with a high office workload. As a consequence, it is common to suffer musculoskeletal pain, mainly in the back, which can lead to both temporary and chronic damage. This pain is related to holding a posture during a prolonged period of sitting, usually in front of a computer. This work presents a IoT posture monitoring system while sitting. The system consists of a device equipped with Force Sensitive Resistors (FSR) that, placed on a chair seat, detects the points where the user exerts pressure when sitting. The system is complemented with a Machine Learning model based on Artificial Neural Networks, which was trained to recognize the neutral correct posture as well as the six most frequent postures that involve risk of damage to the locomotor system. In this study, data was collected from 12 participants for each of the seven positions considered, using the developed sensing device. Several neural network models were trained and evaluated in order to improve the classification effectiveness. Hold-Out technique was used to guide the training and evaluation process. The results achieved a mean accuracy of 81% by means of a model consisting of two hidden layers of 128 neurons each. These results demonstrate that is feasible to distinguish different sitting postures using few sensors allocated in the surface of a seat, which implies lower costs and less complexity of the system.

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“…The DNN model was developed to estimate the joint coordinates of the person in the image, and the accuracy of the proposed model was assessed. The developed model is a vital technology for being applied to estimate the actual activity [17,18], and it is believed that the metabolic rate and the PMV of occupants can be calculated based on the estimated indoor activity. In addition, this possibility is expected to enable PMV-based environmental control and enhance indoor thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

et al. 2021

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The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

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Development of Occupant Pose Classification Model Using Deep Neural Network for Personalized Thermal Conditioning

Cited by 14 publications

References 8 publications

A Node Localization Algorithm based on WoaBp Optimization

A Node Localization Algorithm based on WoaBp Optimization

IoT Device for Sitting Posture Classification Using Artificial Neural Networks

Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

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