Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828269
Caitlin G. Knowles, B. Sennik, Beomjun Ju, Marissa Noon, A. Mills, J. Jur
Achieving accurate fit and body contact pressure is one of the key issues in designing e-textiles such as electrocardiogram (ECG) sensing garments, in which ECG signal quality is dependent on the contact pressure of the electrodes on the skin. While this is a known mechanism, few strategies exist for predictive design as fabrics' body contact pressure response with strain is unique to the fabric's composition and structure. In this work, we propose a technique using 3D garment simulation to predict the body contact pressure of an ECG armband with screen printed Ag/AgCl electrodes. The contact pressure response with strain is evaluated for seven different polyester-spandex jersey knit fabrics. The effects of strain on the signal to noise ratio (SNR) and average R-peak height are measured for armbands of two different fabrics and five sizes. This technique shows potential to improve ECG data quality and shorten the prototyping process for compressive e-textile garments.
{"title":"E-Textile Garment Simulation to Improve ECG Data Quality","authors":"Caitlin G. Knowles, B. Sennik, Beomjun Ju, Marissa Noon, A. Mills, J. Jur","doi":"10.1109/ismict56646.2022.9828269","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828269","url":null,"abstract":"Achieving accurate fit and body contact pressure is one of the key issues in designing e-textiles such as electrocardiogram (ECG) sensing garments, in which ECG signal quality is dependent on the contact pressure of the electrodes on the skin. While this is a known mechanism, few strategies exist for predictive design as fabrics' body contact pressure response with strain is unique to the fabric's composition and structure. In this work, we propose a technique using 3D garment simulation to predict the body contact pressure of an ECG armband with screen printed Ag/AgCl electrodes. The contact pressure response with strain is evaluated for seven different polyester-spandex jersey knit fabrics. The effects of strain on the signal to noise ratio (SNR) and average R-peak height are measured for armbands of two different fabrics and five sizes. This technique shows potential to improve ECG data quality and shorten the prototyping process for compressive e-textile garments.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116815147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828307
A. Zienkiewicz, E. Vihriälä, Eveliina Seppälä, H. Ferdinando, T. Myllylä
Wearable health devices for continuous recording of heart rate (HR) and respiration are a common practice in wellness measurement applications. Typically, these can provide information such as health status and fitness level in daily life. At present, techniques that can provide all vital signals simultaneously, including blood pressure (BP), and validated for clinical accuracy, are lacking. One reason for this is that wearable sensors typically measure the signals from peripherals which are highly influenced by peripheral changes in blood circulation but also cause movement disturbances to recorder signals.In this paper, we present a wearable multimodal sensor system that allows continuous measurement of HR, respiration, and BP oscillations. All sensor elements are enclosed in a single, small-sized unit, which is placed on the chest above the sternum, ensuring monitoring of central BP oscillations. The device has been tested with voluntary subjects (adults and children) in various settings: in static positions, during sleep and in different physiological tasks. In addition, accuracy of the continuous BP measurement was tested against golden standard cuff based continuous BP monitor device, Finometer. We observed that the correlation of BP estimation with the reference had a Pearson’s coefficient in the range of 0.67 to 0.82 (mean 0.76). Both HR and breathing patterns were accurately followed as well. We present the technical solutions used in the sensors’ design, with an emphasis on the novel method to quantify BP from chest movements caused by cardiac activity. Furthermore, we discuss the advantages and challenges of the presented solution, and its feasibility for vital signals monitoring in both home and ambulatory environment.
{"title":"Wearable sensor system on chest for continuous measurement of blood pressure and other vital signs","authors":"A. Zienkiewicz, E. Vihriälä, Eveliina Seppälä, H. Ferdinando, T. Myllylä","doi":"10.1109/ismict56646.2022.9828307","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828307","url":null,"abstract":"Wearable health devices for continuous recording of heart rate (HR) and respiration are a common practice in wellness measurement applications. Typically, these can provide information such as health status and fitness level in daily life. At present, techniques that can provide all vital signals simultaneously, including blood pressure (BP), and validated for clinical accuracy, are lacking. One reason for this is that wearable sensors typically measure the signals from peripherals which are highly influenced by peripheral changes in blood circulation but also cause movement disturbances to recorder signals.In this paper, we present a wearable multimodal sensor system that allows continuous measurement of HR, respiration, and BP oscillations. All sensor elements are enclosed in a single, small-sized unit, which is placed on the chest above the sternum, ensuring monitoring of central BP oscillations. The device has been tested with voluntary subjects (adults and children) in various settings: in static positions, during sleep and in different physiological tasks. In addition, accuracy of the continuous BP measurement was tested against golden standard cuff based continuous BP monitor device, Finometer. We observed that the correlation of BP estimation with the reference had a Pearson’s coefficient in the range of 0.67 to 0.82 (mean 0.76). Both HR and breathing patterns were accurately followed as well. We present the technical solutions used in the sensors’ design, with an emphasis on the novel method to quantify BP from chest movements caused by cardiac activity. Furthermore, we discuss the advantages and challenges of the presented solution, and its feasibility for vital signals monitoring in both home and ambulatory environment.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124993434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828197
Akira Saito, T. Aoyagi
Developments of WBAN channel models require a lot of experiments and simulations. To reduce them, our research group has been proposing a concept of WBAN channel modeling using human motions as parameters. In this report, a human motion and received signal strength synchronization measurement system is proposed. Human motion data is collected by a motion capture device (MOCAP) and the received signal strength (RSSI) data is collected by a BLE wireless device. To synchronize MOCAP and BLE data, a gesture-based method is proposed and confirmed by experiments. We also verified the operation of the measurement system and the possibility of path-loss or channel modeling based on human motion parameters. By using the measured human motion and RSSI data, RSSIs of future times are predicted by machine learning methods, RNN (Recurrent neural network), and LSTM (Long short-term memory). In conclusion, it was found that the RSSI in the future can be predicted to some extent from the past values of human body movements. This result would suggest the possibility of the modeling of WBAN channel variation with human motion as parameters.
{"title":"Development of a Synchronous Measurement System for WBAN Channel Modeling Considering Human Body Motion","authors":"Akira Saito, T. Aoyagi","doi":"10.1109/ismict56646.2022.9828197","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828197","url":null,"abstract":"Developments of WBAN channel models require a lot of experiments and simulations. To reduce them, our research group has been proposing a concept of WBAN channel modeling using human motions as parameters. In this report, a human motion and received signal strength synchronization measurement system is proposed. Human motion data is collected by a motion capture device (MOCAP) and the received signal strength (RSSI) data is collected by a BLE wireless device. To synchronize MOCAP and BLE data, a gesture-based method is proposed and confirmed by experiments. We also verified the operation of the measurement system and the possibility of path-loss or channel modeling based on human motion parameters. By using the measured human motion and RSSI data, RSSIs of future times are predicted by machine learning methods, RNN (Recurrent neural network), and LSTM (Long short-term memory). In conclusion, it was found that the RSSI in the future can be predicted to some extent from the past values of human body movements. This result would suggest the possibility of the modeling of WBAN channel variation with human motion as parameters.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129193301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828164
Yang An, Sang Hun Han, S. Ling
Motor Imagery (MI) is a critical topic in Brain-Computer Interface (BCI). Due to the low signal-to-noise ratio, it is not easy to accurately classify motor imagery signals, especially for multiple classification tasks. Common Spatial Pattern (CSP) is a spatial transformation method that can effectively extract spatial features of EEG signals. However, the covariance matrix is inaccurate due to the small training data size,. Thus, in this paper, a regularization parameter auto-selection algorithm is proposed to automatically adjust the ratio of the covariance matrix calculated by other subjects’ data based on the mutual information. It can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters.To illustrate the merits of the proposed Auto-selected Filter Bank Regularized Common Spatial Pattern (AFBRCSP), we used the ten folds cross-validation accuracy and Kappa as the evaluation metrics to evaluate two data sets (BCI4-2a and BCI3a data set). Both data set include four mental classes. By using BCI4-2a data set, we found that the mean accuracy of AFBRSP is 77.31% and the Kappa is 0.6975, which is higher than Filter Bank Regularized Common Spatial Pattern (FBRCSP) by 5.67% and 0.0756, respectively. By using BCI3a data set, the proposed AFBRCSP improved the accuracy by 8.34% and the Kappa by 0.1111 compared with FBRCSP where the mean accuracy of AFBRCSP is 80.56%, and the kappa is 0.7407. The overall Kappa obtained by the proposed method is also higher than some state-of-the-art methods, implying that the proposed method is more reliable.
运动意象(MI)是脑机接口(BCI)研究的一个重要课题。由于运动图像信号的信噪比较低，很难对运动图像信号进行准确的分类，特别是对于多个分类任务。共同空间模式(CSP)是一种能够有效提取脑电信号空间特征的空间变换方法。但由于训练数据量小，协方差矩阵不准确。因此，本文提出了一种正则化参数自动选择算法，根据互信息自动调整其他受试者数据计算的协方差矩阵的比例。它可以用来解决固定正则化参数导致的混合协方差矩阵不准确的问题。为了说明所提出的自动选择滤波器组正则化公共空间模式(AFBRCSP)的优点，我们使用十倍交叉验证精度和Kappa作为评估指标来评估两个数据集(BCI4-2a和BCI3a数据集)。这两个数据集都包括四个心理类别。利用BCI4-2a数据集，我们发现AFBRSP的平均准确率为77.31%，Kappa为0.6975，分别比滤波组正则化公共空间模式(Filter Bank regularization Common Spatial Pattern, FBRCSP)高5.67%和0.0756。使用BCI3a数据集，与FBRCSP相比，AFBRCSP的准确率提高了8.34%，Kappa提高了0.1111，其中AFBRCSP的平均准确率为80.56%，Kappa为0.7407。所提方法得到的总体Kappa值也高于一些现有方法，表明所提方法的可靠性更高。
{"title":"Multi-classification for EEG Motor Imagery Signals using Auto-selected Filter Bank Regularized Common Spatial Pattern","authors":"Yang An, Sang Hun Han, S. Ling","doi":"10.1109/ismict56646.2022.9828164","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828164","url":null,"abstract":"Motor Imagery (MI) is a critical topic in Brain-Computer Interface (BCI). Due to the low signal-to-noise ratio, it is not easy to accurately classify motor imagery signals, especially for multiple classification tasks. Common Spatial Pattern (CSP) is a spatial transformation method that can effectively extract spatial features of EEG signals. However, the covariance matrix is inaccurate due to the small training data size,. Thus, in this paper, a regularization parameter auto-selection algorithm is proposed to automatically adjust the ratio of the covariance matrix calculated by other subjects’ data based on the mutual information. It can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters.To illustrate the merits of the proposed Auto-selected Filter Bank Regularized Common Spatial Pattern (AFBRCSP), we used the ten folds cross-validation accuracy and Kappa as the evaluation metrics to evaluate two data sets (BCI4-2a and BCI3a data set). Both data set include four mental classes. By using BCI4-2a data set, we found that the mean accuracy of AFBRSP is 77.31% and the Kappa is 0.6975, which is higher than Filter Bank Regularized Common Spatial Pattern (FBRCSP) by 5.67% and 0.0756, respectively. By using BCI3a data set, the proposed AFBRCSP improved the accuracy by 8.34% and the Kappa by 0.1111 compared with FBRCSP where the mean accuracy of AFBRCSP is 80.56%, and the kappa is 0.7407. The overall Kappa obtained by the proposed method is also higher than some state-of-the-art methods, implying that the proposed method is more reliable.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131881497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828343
Han Sun, Yu Chen
With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user’s ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user’s privacy. To achieve a high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system preforms action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.
{"title":"Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition","authors":"Han Sun, Yu Chen","doi":"10.1109/ismict56646.2022.9828343","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828343","url":null,"abstract":"With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user’s ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user’s privacy. To achieve a high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system preforms action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124559225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828125
Avraam El Hamidieh, Nikolaos Dietis, A. Samoylenko, I. Meiser, Niovi Nicolaou, Eslam Abdelrady, A. Zhyvolozhnyi, S. Vainio, A. Odysseos
The evolution of Glioblastoma Multiforme (GBM) is defined by the dynamics of growing bionanomachine networks in an interplay between "senders" or "transceivers" and "receivers". Central to this process are the inter-communications between sub-cellular bionanomachines secreted by GBM cells in the form of exosomes. Herein we present a dynamic cell-based therapeutic nanonetwork of genetically engineered optically active bionanomachines. The communication paradigm is defined by the interaction between neural stem cell-derived exosomes expressing Enhanced Green Fluorescent Protein and GBM cells expressing Tandem Dimer Tomato protein (tdT), based on the dynamic transfer of energies of excited state bionanomachines in resonance. With EGFP serving as energy donor and tdT as energy acceptor we provide multilevel evidence validating a Förster Resonance Energy Transfer - mediated interaction between GBM cells and exosomes, therefore documenting their sustainable and close proximity. Such an approach has the potential to enable wireless communication between optically active bionanomachines via quantifiable interfaces within channel networks, further enabling mechanistic and therapeutic models.
{"title":"Optically Active Bionanomachine Interfaces Build Therapeutic Nanonetworks for Glioblastoma Multiforme","authors":"Avraam El Hamidieh, Nikolaos Dietis, A. Samoylenko, I. Meiser, Niovi Nicolaou, Eslam Abdelrady, A. Zhyvolozhnyi, S. Vainio, A. Odysseos","doi":"10.1109/ismict56646.2022.9828125","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828125","url":null,"abstract":"The evolution of Glioblastoma Multiforme (GBM) is defined by the dynamics of growing bionanomachine networks in an interplay between \"senders\" or \"transceivers\" and \"receivers\". Central to this process are the inter-communications between sub-cellular bionanomachines secreted by GBM cells in the form of exosomes. Herein we present a dynamic cell-based therapeutic nanonetwork of genetically engineered optically active bionanomachines. The communication paradigm is defined by the interaction between neural stem cell-derived exosomes expressing Enhanced Green Fluorescent Protein and GBM cells expressing Tandem Dimer Tomato protein (tdT), based on the dynamic transfer of energies of excited state bionanomachines in resonance. With EGFP serving as energy donor and tdT as energy acceptor we provide multilevel evidence validating a Förster Resonance Energy Transfer - mediated interaction between GBM cells and exosomes, therefore documenting their sustainable and close proximity. Such an approach has the potential to enable wireless communication between optically active bionanomachines via quantifiable interfaces within channel networks, further enabling mechanistic and therapeutic models.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"40 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114035046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828264
M. Hämäläinen, L. Mucchi, Tuomas Paso
Utilisation of Internet of Things (IoT) is spreading fast in modern home and industrial automation, but IoT solutions can also be widely utilised in healthcare domain. Monitoring of a person's vital signs and the connectivity between different medical devices are moving towards wireless implementation. Wearables and other health and wellbeing related personal gadgets carried by humans, or even implanted inside a human, are seeing to be more popular in healthcare applications. To respond to these challenges, new technologies in different domains are continuously developed. This paper is discussing the possibilities to effectively and reliably deliver eHealth services outside hospitals or other care units. In addition, we will introduce how ETSI SmartBAN can be utilised in various eHealth applications as well as how the security aspects should be taken into account when dealing with personal data.
{"title":"Remote Secure eHealth Provision: ETSI SmartBAN as an Enabler","authors":"M. Hämäläinen, L. Mucchi, Tuomas Paso","doi":"10.1109/ismict56646.2022.9828264","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828264","url":null,"abstract":"Utilisation of Internet of Things (IoT) is spreading fast in modern home and industrial automation, but IoT solutions can also be widely utilised in healthcare domain. Monitoring of a person's vital signs and the connectivity between different medical devices are moving towards wireless implementation. Wearables and other health and wellbeing related personal gadgets carried by humans, or even implanted inside a human, are seeing to be more popular in healthcare applications. To respond to these challenges, new technologies in different domains are continuously developed. This paper is discussing the possibilities to effectively and reliably deliver eHealth services outside hospitals or other care units. In addition, we will introduce how ETSI SmartBAN can be utilised in various eHealth applications as well as how the security aspects should be taken into account when dealing with personal data.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-02DOI: 10.1109/ismict56646.2022.9828045
L. Mucchi, S. Jayousi, P. Cappanera, Filippo Visintin, P. Zoppi, E. Paoletti, Simona Geli, Pietro Dionisio, E. Tamburini, C. Paggetti
An efficient management of chronic patients is a key element in the current and future Healthcare context.This paper aims at providing an overview of the PROASSIST 4.0 solution for an healthcare assistance territorial model 4.0. The proposed service relies on the integration between the organizational assets and advanced ICT technologies: a dynamic and adaptive system able to respond to the needs of citizens / patients and to support staff healthcare in the management of an ever-increasing number of patients is proposed. This is achieved by optimizing the scheduling of the territorial assistance based on multiple occurrences and actual patients’ health status.
{"title":"PROASSIST4.0: An Optimized Tele-Assistance System for the Future Healthcare","authors":"L. Mucchi, S. Jayousi, P. Cappanera, Filippo Visintin, P. Zoppi, E. Paoletti, Simona Geli, Pietro Dionisio, E. Tamburini, C. Paggetti","doi":"10.1109/ismict56646.2022.9828045","DOIUrl":"https://doi.org/10.1109/ismict56646.2022.9828045","url":null,"abstract":"An efficient management of chronic patients is a key element in the current and future Healthcare context.This paper aims at providing an overview of the PROASSIST 4.0 solution for an healthcare assistance territorial model 4.0. The proposed service relies on the integration between the organizational assets and advanced ICT technologies: a dynamic and adaptive system able to respond to the needs of citizens / patients and to support staff healthcare in the management of an ever-increasing number of patients is proposed. This is achieved by optimizing the scheduling of the territorial assistance based on multiple occurrences and actual patients’ health status.","PeriodicalId":436823,"journal":{"name":"2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117119012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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