Toon De Pessemier, T. V. Renterghem, K. Vanhecke, A. All, Karlo Filipan, Kang Sun, B. D. Coensel, L. Marez, L. Martens, D. Botteldooren, W. Joseph
Sound pollution is an ever growing problem in modern society, and especially in urban environments. In this paper, we investigate if and how artificial sounds can improve the experience of visitors of an urban park with a lot of traffic noise. By using a mobile app, park visitors can control the sound playback by selecting the natural sounds they like, such as birds or a waterfall, and setting the volume. This process of adding artificial sounds to the existing sound environment results in an augmented soundscape. Comparison of the environment with and without this sound playback showed that most visitors experience this as an improvement of the park environment and enjoy controlling the sounds. An experiment with 165 users identified various correlations between the visitors’ subjective evaluations of the sound environment and objective measures of their usage behavior with the app, such as the number of interactions and the spent time.
{"title":"Enhancing the park experience by giving visitors control over the park's soundscape","authors":"Toon De Pessemier, T. V. Renterghem, K. Vanhecke, A. All, Karlo Filipan, Kang Sun, B. D. Coensel, L. Marez, L. Martens, D. Botteldooren, W. Joseph","doi":"10.3233/ais-220621","DOIUrl":"https://doi.org/10.3233/ais-220621","url":null,"abstract":"Sound pollution is an ever growing problem in modern society, and especially in urban environments. In this paper, we investigate if and how artificial sounds can improve the experience of visitors of an urban park with a lot of traffic noise. By using a mobile app, park visitors can control the sound playback by selecting the natural sounds they like, such as birds or a waterfall, and setting the volume. This process of adding artificial sounds to the existing sound environment results in an augmented soundscape. Comparison of the environment with and without this sound playback showed that most visitors experience this as an improvement of the park environment and enjoy controlling the sounds. An experiment with 165 users identified various correlations between the visitors’ subjective evaluations of the sound environment and objective measures of their usage behavior with the app, such as the number of interactions and the spent time.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"2 1","pages":"99-118"},"PeriodicalIF":1.7,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82101101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Adhikary, Samiran Chattopadhyay, B. Ghosh, S. Choudhury, S. Nath, Nilkantha Garain
Wireless Body Area Network (WBAN) is an emerging technology that has the potential to redefine healthcare sector around the world. It can perform proactively by ubiquitously monitoring human health. But its enormous scope is challenged by limited battery power of the sensors, energy and bandwidth. Moreover, the random motion of human beings makes sensor positioning difficult and restricts efficiently routing of critical health parameter values. State of the art protocols do not address the adverse effects of heating of the implanted sensors on human tissues along with energy constraints and interference issues simultaneously. This paper handles all these issues jointly by designing a topology which has an optimized number of relay nodes and then proposes an efficient routing algorithm. Relay nodes are incorporated to frame the backbone of the connected wireless network so that all sensor nodes are coupled with at least one relay node and none of the nodes in the network remain isolated. In the proposed method, the remaining energy of the in-vivo sensors are dissipated intelligently and homogeneously so that network lifetime is enhanced without compromising reliability. Moreover, in our method, multicasting has been used to reduce transmission of unnecessary packets. Our design also leads to minimum hop count from body sensors to the sink node. The effectiveness and feasibility of our proposed approach has been evaluated and analyzed through numerous simulations. The analysis illustrates the efficacy of the proposed solution in terms of delay, network lifetime, energy efficiency, SAR and throughput.
{"title":"Reliable routing in Wireless Body Area Network using optimum number of relay nodes for enhancing network lifetime","authors":"S. Adhikary, Samiran Chattopadhyay, B. Ghosh, S. Choudhury, S. Nath, Nilkantha Garain","doi":"10.3233/ais-210055","DOIUrl":"https://doi.org/10.3233/ais-210055","url":null,"abstract":"Wireless Body Area Network (WBAN) is an emerging technology that has the potential to redefine healthcare sector around the world. It can perform proactively by ubiquitously monitoring human health. But its enormous scope is challenged by limited battery power of the sensors, energy and bandwidth. Moreover, the random motion of human beings makes sensor positioning difficult and restricts efficiently routing of critical health parameter values. State of the art protocols do not address the adverse effects of heating of the implanted sensors on human tissues along with energy constraints and interference issues simultaneously. This paper handles all these issues jointly by designing a topology which has an optimized number of relay nodes and then proposes an efficient routing algorithm. Relay nodes are incorporated to frame the backbone of the connected wireless network so that all sensor nodes are coupled with at least one relay node and none of the nodes in the network remain isolated. In the proposed method, the remaining energy of the in-vivo sensors are dissipated intelligently and homogeneously so that network lifetime is enhanced without compromising reliability. Moreover, in our method, multicasting has been used to reduce transmission of unnecessary packets. Our design also leads to minimum hop count from body sensors to the sink node. The effectiveness and feasibility of our proposed approach has been evaluated and analyzed through numerous simulations. The analysis illustrates the efficacy of the proposed solution in terms of delay, network lifetime, energy efficiency, SAR and throughput.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"126 1","pages":"135-153"},"PeriodicalIF":1.7,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88114767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid growth of smart manufacturing leads to an increase in the power consumption of the equipment used and has the challenges like misuse of equipment, operator safety, and protection of equipment from any electrical disturbances or sudden power surge. This paper aims in creating a smart access control unit with a Dual Access Energy Monitoring (DAEM) system. Here, the equipment is restricted to use by the authorized individual under its working limits. The access control for energy monitoring has been carried out using Radio Frequency Identification (RFID) and IEEE 802.11ac with the design of customized breakout board. The DAEM has been carried out by comparing the electrical characteristics like current, voltage, active power, apparent power, and power factor with their preset warning and trip threshold values. Using DAEM, the electrical circuit overload is prevented ensuring the operator’s safety. Moreover, the timer in the circuit will automatically disconnect the load from the mains after a timeout to prevent unnecessary wastage of energy.
{"title":"Development of dual access energy monitoring for the smart control system","authors":"Shubham Devidas Gujar, S. Sulthana, A. Rajesh","doi":"10.3233/ais-210356","DOIUrl":"https://doi.org/10.3233/ais-210356","url":null,"abstract":"The rapid growth of smart manufacturing leads to an increase in the power consumption of the equipment used and has the challenges like misuse of equipment, operator safety, and protection of equipment from any electrical disturbances or sudden power surge. This paper aims in creating a smart access control unit with a Dual Access Energy Monitoring (DAEM) system. Here, the equipment is restricted to use by the authorized individual under its working limits. The access control for energy monitoring has been carried out using Radio Frequency Identification (RFID) and IEEE 802.11ac with the design of customized breakout board. The DAEM has been carried out by comparing the electrical characteristics like current, voltage, active power, apparent power, and power factor with their preset warning and trip threshold values. Using DAEM, the electrical circuit overload is prevented ensuring the operator’s safety. Moreover, the timer in the circuit will automatically disconnect the load from the mains after a timeout to prevent unnecessary wastage of energy.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"3 1","pages":"119-133"},"PeriodicalIF":1.7,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81035432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cities are in a period of rapid urban development and high-rise buildings are constantly emerging. The characteristics of a fire in a high-rise building are the rapid spread of the fire, the difficulty of fighting the fire, and the difficulty of evacuation. Intelligent fire evacuation requires dynamic planning of paths in fire field, it is necessary to automatically adjust the evacuation route in the building according to the real-time information of the fire. In this paper, an improved bidirectional ant colony algorithm is proposed to optimize fire evacuation routes. In order to improve the global search capability of the algorithm, a bidirectional search strategy with the A* algorithm is designed for the ant colony algorithm, the blindness of the algorithm is reduced in the initial search, the pheromone update strategy is improved, and the convergence speed of the algorithm is increased. The fire scene information is combined with the steering penalty coefficient to improve the algorithm’s evaporation coefficient, heuristic function and transition probability, avoid the risk of falling into the local optimum, improve the search efficiency of the algorithm and the smoothness of the path, and effectively avoid areas affected by the fire. The effectiveness of the algorithm is verified by simulation.
{"title":"Bidirectional ACO intelligent fire evacuation route optimization","authors":"Jingfang Wang","doi":"10.3233/ais-220620","DOIUrl":"https://doi.org/10.3233/ais-220620","url":null,"abstract":"Cities are in a period of rapid urban development and high-rise buildings are constantly emerging. The characteristics of a fire in a high-rise building are the rapid spread of the fire, the difficulty of fighting the fire, and the difficulty of evacuation. Intelligent fire evacuation requires dynamic planning of paths in fire field, it is necessary to automatically adjust the evacuation route in the building according to the real-time information of the fire. In this paper, an improved bidirectional ant colony algorithm is proposed to optimize fire evacuation routes. In order to improve the global search capability of the algorithm, a bidirectional search strategy with the A* algorithm is designed for the ant colony algorithm, the blindness of the algorithm is reduced in the initial search, the pheromone update strategy is improved, and the convergence speed of the algorithm is increased. The fire scene information is combined with the steering penalty coefficient to improve the algorithm’s evaporation coefficient, heuristic function and transition probability, avoid the risk of falling into the local optimum, improve the search efficiency of the algorithm and the smoothness of the path, and effectively avoid areas affected by the fire. The effectiveness of the algorithm is verified by simulation.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"31 1","pages":"79-97"},"PeriodicalIF":1.7,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81347982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hamid Aghajan,Juan Carlos Augusto,Andrés Muñoz Ortega
Over the past thirteen years of its life, our journal has been supported by a large number of colleagues who contributed with their time and expertise to assess the quality of the submissions to JAISE and helped decide which papers qualify to be published. These reviewers are an important part of the JAISE community and we would like to explicitly thank all of them for their valuable contributions. The effort of reviewers often remains unnoticed in the community served by a journal, especially in a blind review system. Since eight years ago, we have been acknowledging the participation of our reviewers in the making of JAISE. As a second step towards making our gratitude explicit and highlighting the importance of the contributions made by our reviewers, we have also implemented the practice of selecting two reviewers each year who have consistently provided detailed and quality reviews and inviting them to serve as part of the Editorial Board of JAISE. The list of reviewers in 2021 includes:1 Long Meng, Mario Quinde, Hangyu Zhu, Wael Yafooz, Vincent Tam, Shaoxiong Sun, Ruisheng Su, Gautam Srivastava, Xiaowen Huang, Tahera Hossain, Abba Suganda Girsang, Zhineng Chen, Chao Cai, Zhenxing Zhou, Xu Zhao, Ali Yousefi, Lin Xu, Ying Wang, Wenjin Wang, Lei Wang, Elena Verdú, V. E. Sathishkumar, Akihito Taya, Sunit Sivasankaran, Iñigo Sarria, Wendy Sanchez, Oscar San Juan, Xingqun Qi, François Portet, Parvaneh Parvin, Theodor Panagiotakopoulos, P. Shakeel Mohamed, Kizito Nkurikiyeyezu, Wenjuan Lu, Ilde Lorato, Zhenglong Li, Rosen Ivanov, Hans Guesgen, Pushpa Gothwal, Mohammad Reza Ebrahimi Dishabi, Xiaorong Ding, Haikang Diao, Samik Datta, Andreea Danielescu, Stephen Czarnuch, Peirui Bai, Abdulsattar Abdullah Hamad, Santhosh Kumar B, Giridhar Reddy Bojja, C. B. Sivaparthipan, Gabriele Civitarese, Muhammed Faheem, Thippa Reddy Gadekallu, Lalit Garg, Judith Good, Abdul Rehman Javed, Mahdi Jemmali, Awais Khan Jumani, Charalampos Karagiannidis, Rashi Kohli, Zhenglong Lin, Qingguo Li, Andrew Lui, Praveen Kumar Reddy Maddikunta, Devi Mani, Luis Sánchez Fernández, Thomas van Rompay, Frank Wallhoff, Weizheng Wang, Zhenxing Zhou.
{"title":"Acknowledgment of JAISE reviewers in 2021","authors":"Hamid Aghajan,Juan Carlos Augusto,Andrés Muñoz Ortega","doi":"10.3233/ais-210619","DOIUrl":"https://doi.org/10.3233/ais-210619","url":null,"abstract":"Over the past thirteen years of its life, our journal has been supported by a large number of colleagues who contributed with their time and expertise to assess the quality of the submissions to JAISE and helped decide which papers qualify to be published. These reviewers are an important part of the JAISE community and we would like to explicitly thank all of them for their valuable contributions. The effort of reviewers often remains unnoticed in the community served by a journal, especially in a blind review system. Since eight years ago, we have been acknowledging the participation of our reviewers in the making of JAISE. As a second step towards making our gratitude explicit and highlighting the importance of the contributions made by our reviewers, we have also implemented the practice of selecting two reviewers each year who have consistently provided detailed and quality reviews and inviting them to serve as part of the Editorial Board of JAISE. The list of reviewers in 2021 includes:1 Long Meng, Mario Quinde, Hangyu Zhu, Wael Yafooz, Vincent Tam, Shaoxiong Sun, Ruisheng Su, Gautam Srivastava, Xiaowen Huang, Tahera Hossain, Abba Suganda Girsang, Zhineng Chen, Chao Cai, Zhenxing Zhou, Xu Zhao, Ali Yousefi, Lin Xu, Ying Wang, Wenjin Wang, Lei Wang, Elena Verdú, V. E. Sathishkumar, Akihito Taya, Sunit Sivasankaran, Iñigo Sarria, Wendy Sanchez, Oscar San Juan, Xingqun Qi, François Portet, Parvaneh Parvin, Theodor Panagiotakopoulos, P. Shakeel Mohamed, Kizito Nkurikiyeyezu, Wenjuan Lu, Ilde Lorato, Zhenglong Li, Rosen Ivanov, Hans Guesgen, Pushpa Gothwal, Mohammad Reza Ebrahimi Dishabi, Xiaorong Ding, Haikang Diao, Samik Datta, Andreea Danielescu, Stephen Czarnuch, Peirui Bai, Abdulsattar Abdullah Hamad, Santhosh Kumar B, Giridhar Reddy Bojja, C. B. Sivaparthipan, Gabriele Civitarese, Muhammed Faheem, Thippa Reddy Gadekallu, Lalit Garg, Judith Good, Abdul Rehman Javed, Mahdi Jemmali, Awais Khan Jumani, Charalampos Karagiannidis, Rashi Kohli, Zhenglong Lin, Qingguo Li, Andrew Lui, Praveen Kumar Reddy Maddikunta, Devi Mani, Luis Sánchez Fernández, Thomas van Rompay, Frank Wallhoff, Weizheng Wang, Zhenxing Zhou.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"8 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrés Muñoz, J. Augusto, V. W. L. Tam, H. Aghajan
The paper entitled “ Ultra-wideband data as input of a combined EfficientNet and LSTM architecture for human activity recognition ” by Alexandre Beaulieu et al. addresses the important confluence of activity recognition for ambient assisted living. The technical core contribution of this article includes a system based on a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. The system was validated on a real smart environment and showed improvements to previous similar approaches. The paper entitled “ Fuzzy multi-agent assistance system for elderly care based on user engagement ” by Al-fonso Rojas-Domínguez et al. also considers an ambient assisted living system however more focused on software and algorithmic approach and based on a multi-agent system. The focus of the system is in providing core compo-nents of the multi-agent system strong interaction and engagement capabilities with the main intended beneficiaries of the ambient assisted living system. The system is demonstrated with scenarios focused on providing security, comfort and health-related services. User engagement levels are estimated through a fuzzy inference system. The system was tested using two different datasets of real interactions between users and devices in their home environments which demonstrates how the system improves performance and alignment of the system behaviour with user satisfaction. The paper entitled “ Refillable PUF authentication
{"title":"Preface to JAISE 14(1)","authors":"Andrés Muñoz, J. Augusto, V. W. L. Tam, H. Aghajan","doi":"10.3233/ais-210618","DOIUrl":"https://doi.org/10.3233/ais-210618","url":null,"abstract":"The paper entitled “ Ultra-wideband data as input of a combined EfficientNet and LSTM architecture for human activity recognition ” by Alexandre Beaulieu et al. addresses the important confluence of activity recognition for ambient assisted living. The technical core contribution of this article includes a system based on a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. The system was validated on a real smart environment and showed improvements to previous similar approaches. The paper entitled “ Fuzzy multi-agent assistance system for elderly care based on user engagement ” by Al-fonso Rojas-Domínguez et al. also considers an ambient assisted living system however more focused on software and algorithmic approach and based on a multi-agent system. The focus of the system is in providing core compo-nents of the multi-agent system strong interaction and engagement capabilities with the main intended beneficiaries of the ambient assisted living system. The system is demonstrated with scenarios focused on providing security, comfort and health-related services. User engagement levels are estimated through a fuzzy inference system. The system was tested using two different datasets of real interactions between users and devices in their home environments which demonstrates how the system improves performance and alignment of the system behaviour with user satisfaction. The paper entitled “ Refillable PUF authentication","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"28 1","pages":"423-424"},"PeriodicalIF":1.7,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87658177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design of Internet of Things enabled personalized healthcare device for vital signs monitoring","authors":"A. Renold, K. Kumar","doi":"10.3233/AIS-220098","DOIUrl":"https://doi.org/10.3233/AIS-220098","url":null,"abstract":"","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"14 1","pages":"375-384"},"PeriodicalIF":1.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69735476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The emerging technology and innovation on sensing technology, data computing, and artificial intelligence (AI) has resulted in an accelerated development of smart healthcare. This thematic issue on Sensing and Computing for Smart Healthcare aims to highlight the diverse advances and the latest developments and emergent technologies in healthcare applications concerning remote human health monitoring, physiological sensing and imaging, wear-able biosensors, intelligent computing and AI. The thematic issue attracted a good number of submissions from researchers in these domains. After critical peer-review and selection, four manuscripts were accepted for publica-tion in this thematic issue, covering the topics of image analysis and AI, physiological signal processing and disease detection, and ambient assisted living. The paper “ Ambient assisted living framework for elderly care using internet of medical things, smart sensors, and GRU deep learning techniques ” by Syed et al. proposes an Ambient Assisted Living (AAL) system with Internet of Medical Things (IoMT) that leverages deep learning techniques to monitor and evaluate the elderly’s activities and vital signs for clinical decision support. By combining smart sensors (including accelerome-ters, gyroscopes, and magnetometers), IoMT infrastructure, and AI algorithms, elderly activities can be recognized and their heart rate variability over time can be monitored. The proposed AAL system is expected to be beneficial during crucial situations such as the pandemics to remotely monitor elderly patients and their health-related status or risks. The paper “ Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning ” by Nian et al. proposes a spatial geometric-encoding
{"title":"Sensing and computing for smart healthcare","authors":"Chen Chen, Caifeng Shan, Ronald M. Aarts, X. Long","doi":"10.3233/ais-210617","DOIUrl":"https://doi.org/10.3233/ais-210617","url":null,"abstract":"The emerging technology and innovation on sensing technology, data computing, and artificial intelligence (AI) has resulted in an accelerated development of smart healthcare. This thematic issue on Sensing and Computing for Smart Healthcare aims to highlight the diverse advances and the latest developments and emergent technologies in healthcare applications concerning remote human health monitoring, physiological sensing and imaging, wear-able biosensors, intelligent computing and AI. The thematic issue attracted a good number of submissions from researchers in these domains. After critical peer-review and selection, four manuscripts were accepted for publica-tion in this thematic issue, covering the topics of image analysis and AI, physiological signal processing and disease detection, and ambient assisted living. The paper “ Ambient assisted living framework for elderly care using internet of medical things, smart sensors, and GRU deep learning techniques ” by Syed et al. proposes an Ambient Assisted Living (AAL) system with Internet of Medical Things (IoMT) that leverages deep learning techniques to monitor and evaluate the elderly’s activities and vital signs for clinical decision support. By combining smart sensors (including accelerome-ters, gyroscopes, and magnetometers), IoMT infrastructure, and AI algorithms, elderly activities can be recognized and their heart rate variability over time can be monitored. The proposed AAL system is expected to be beneficial during crucial situations such as the pandemics to remotely monitor elderly patients and their health-related status or risks. The paper “ Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning ” by Nian et al. proposes a spatial geometric-encoding","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"104 1","pages":"3-4"},"PeriodicalIF":1.7,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87622977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fudong Nian, Jie Sun, Dashan Jiang, Jingjing Zhang, Teng Li, W. Lu
Dose-volume histogram (DVH) is an important tool to evaluate the radiation treatment plan quality, which could be predicted based on the distance-volume spatial relationship between planning target volumes (PTV) and organs-at-risks (OARs). However, the prediction accuracy is still limited due to the complicated calculation process and the omission of detailed spatial geometric features. In this paper, we propose a spatial geometric-encoding network (SGEN) to incorporate 3D spatial information with an efficient 2D convolutional neural networks (CNN) for accurate prediction of DVH for esophageal radiation treatments. 3D computed tomography (CT) scans, 3D PTV scans and 3D distance images are used as the multi-view input of the proposed model. The dilation convolution based Multi-scale concurrent Spatial and Channel Squeeze & Excitation (msc-SE) structure in the proposed model not only can maintain comprehensive spatial information with less computation cost, but also can extract the features of organs at different scales effectively. Five-fold cross-validation on 200 intensity-modulated radiation therapy (IMRT) esophageal radiation treatment plans were used in this paper. The mean absolute error (MAE) of DVH focusing on the left lung can achieve 2.73 ± 2.36, while the MAE was 7.73 ± 3.81 using traditional machine learning prediction model. In addition, extensive ablation studies have been conducted and the quantitative results demonstrate the effectiveness of different components in the proposed method.
{"title":"Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning","authors":"Fudong Nian, Jie Sun, Dashan Jiang, Jingjing Zhang, Teng Li, W. Lu","doi":"10.3233/ais-210084","DOIUrl":"https://doi.org/10.3233/ais-210084","url":null,"abstract":"Dose-volume histogram (DVH) is an important tool to evaluate the radiation treatment plan quality, which could be predicted based on the distance-volume spatial relationship between planning target volumes (PTV) and organs-at-risks (OARs). However, the prediction accuracy is still limited due to the complicated calculation process and the omission of detailed spatial geometric features. In this paper, we propose a spatial geometric-encoding network (SGEN) to incorporate 3D spatial information with an efficient 2D convolutional neural networks (CNN) for accurate prediction of DVH for esophageal radiation treatments. 3D computed tomography (CT) scans, 3D PTV scans and 3D distance images are used as the multi-view input of the proposed model. The dilation convolution based Multi-scale concurrent Spatial and Channel Squeeze & Excitation (msc-SE) structure in the proposed model not only can maintain comprehensive spatial information with less computation cost, but also can extract the features of organs at different scales effectively. Five-fold cross-validation on 200 intensity-modulated radiation therapy (IMRT) esophageal radiation treatment plans were used in this paper. The mean absolute error (MAE) of DVH focusing on the left lung can achieve 2.73 ± 2.36, while the MAE was 7.73 ± 3.81 using traditional machine learning prediction model. In addition, extensive ablation studies have been conducted and the quantitative results demonstrate the effectiveness of different components in the proposed method.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"14 1","pages":"25-37"},"PeriodicalIF":1.7,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77087793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liyakathunisa, A. Alsaeedi, S. Jabeen, H. Kolivand
Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.
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