Pub Date : 2023-06-08DOI: 10.1109/iwasi58316.2023.10164385
{"title":"Session 4: Sensors for healthcare applications I","authors":"","doi":"10.1109/iwasi58316.2023.10164385","DOIUrl":"https://doi.org/10.1109/iwasi58316.2023.10164385","url":null,"abstract":"","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127440142","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 : 2023-06-08DOI: 10.1109/IWASI58316.2023.10164372
Marcello Zanghieri, S. Benatti, L. Benini, Elisa Donati
Human-Machine Interfaces (HMIs) are a rapidly progressing field, and gesture recognition is a promising method in industrial, consumer, and health use cases. Surface electromyography (sEMG) is a State-of-the-Art (SoA) pathway for human-to-machine communication. Currently, the research goal is a more intuitive and fluid control, moving from signal classification of discrete positions to continuous control based on regression. The sEMG-based regression is still scarcely explored in research since most approaches have addressed classification. In this work, we propose the first event-based EMG encoding applied to the regression of hand kinematics suitable for working in streaming on a low-power microcontroller (STM32 F401, mounting ARM Cortex-M4). The motivation for event-based encoding is to exploit upcoming neuromorphic hardware to benefit from reduced latency and power consumption. We achieve a Mean Absolute Error of $8.8pm 2.3$ degrees on 5 degrees of actuation on the public dataset NinaPro DB8, comparable with the SoA Deep Neural Network (DNN). We use $9times$ less memory and $13times$ less energy per inference, with $10times$ shorter latency per inference compared to the SoA deep net, proving suitable for resource-constrained embedded platforms.
{"title":"Event-based Low-Power and Low-Latency Regression Method for Hand Kinematics from Surface EMG","authors":"Marcello Zanghieri, S. Benatti, L. Benini, Elisa Donati","doi":"10.1109/IWASI58316.2023.10164372","DOIUrl":"https://doi.org/10.1109/IWASI58316.2023.10164372","url":null,"abstract":"Human-Machine Interfaces (HMIs) are a rapidly progressing field, and gesture recognition is a promising method in industrial, consumer, and health use cases. Surface electromyography (sEMG) is a State-of-the-Art (SoA) pathway for human-to-machine communication. Currently, the research goal is a more intuitive and fluid control, moving from signal classification of discrete positions to continuous control based on regression. The sEMG-based regression is still scarcely explored in research since most approaches have addressed classification. In this work, we propose the first event-based EMG encoding applied to the regression of hand kinematics suitable for working in streaming on a low-power microcontroller (STM32 F401, mounting ARM Cortex-M4). The motivation for event-based encoding is to exploit upcoming neuromorphic hardware to benefit from reduced latency and power consumption. We achieve a Mean Absolute Error of $8.8pm 2.3$ degrees on 5 degrees of actuation on the public dataset NinaPro DB8, comparable with the SoA Deep Neural Network (DNN). We use $9times$ less memory and $13times$ less energy per inference, with $10times$ shorter latency per inference compared to the SoA deep net, proving suitable for resource-constrained embedded platforms.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123474210","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 : 2023-06-08DOI: 10.1109/IWASI58316.2023.10164498
A. Vladimirescu
Advances in semiconductor and superconductor technology have sparked a new round of research in quantum computing in recent years. Quantum computers hold the promise to efficiently solve problems that are intractable by today’s electronic computers. In a quantum computer, standard logic bits ‘0’ and ‘1’ are replaced by quantum states |0⟩ and |1⟩ referred to as quantum bits (qubits). The challenge facing researchers is controlling and detecting these quantum states, which are preserved long enough only at deep sub-Kelvin temperatures.
{"title":"The Next Dawn for CMOS: Cryogenic ICs for Quantum Computing","authors":"A. Vladimirescu","doi":"10.1109/IWASI58316.2023.10164498","DOIUrl":"https://doi.org/10.1109/IWASI58316.2023.10164498","url":null,"abstract":"Advances in semiconductor and superconductor technology have sparked a new round of research in quantum computing in recent years. Quantum computers hold the promise to efficiently solve problems that are intractable by today’s electronic computers. In a quantum computer, standard logic bits ‘0’ and ‘1’ are replaced by quantum states |0⟩ and |1⟩ referred to as quantum bits (qubits). The challenge facing researchers is controlling and detecting these quantum states, which are preserved long enough only at deep sub-Kelvin temperatures.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122395071","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 : 2023-06-08DOI: 10.1109/iwasi58316.2023.10164454
{"title":"Session 3: Biological sensors and applications","authors":"","doi":"10.1109/iwasi58316.2023.10164454","DOIUrl":"https://doi.org/10.1109/iwasi58316.2023.10164454","url":null,"abstract":"","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114196812","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 : 2023-06-08DOI: 10.1109/IWASI58316.2023.10164336
D. Venuto, G. Mezzina, Angelo Tricase, P. Bollella, Grazia Mascellaro, L. Torsi
In this paper, we present a pioneer design of a wearable and flexible potentiometric chloride activity sensing platform. This platform is intended to provide real-time support for the diagnosis of Cystic Fibrosis by gathering and correlating historical clinical data of patients under control. To ensure wearable and comfortable functionality, a flexible support has employed for both the multi-working electrochemical electrodes and the smart electronics. The proposed electronic system embeds a microcontroller, enabling potential reasoning on the acquired data and patient history, while a microchip antenna ensures the wireless transmission of measurements and diagnosis to a remote healthcare center. The characterization of the realized electrodes and the electronic readout are here shown: the results are compliant with the requirements of the standard medical equipment.
{"title":"Wearable and Flexible Fibrosis Cystic Tag with Potentiometric Chloride Activity Sensing","authors":"D. Venuto, G. Mezzina, Angelo Tricase, P. Bollella, Grazia Mascellaro, L. Torsi","doi":"10.1109/IWASI58316.2023.10164336","DOIUrl":"https://doi.org/10.1109/IWASI58316.2023.10164336","url":null,"abstract":"In this paper, we present a pioneer design of a wearable and flexible potentiometric chloride activity sensing platform. This platform is intended to provide real-time support for the diagnosis of Cystic Fibrosis by gathering and correlating historical clinical data of patients under control. To ensure wearable and comfortable functionality, a flexible support has employed for both the multi-working electrochemical electrodes and the smart electronics. The proposed electronic system embeds a microcontroller, enabling potential reasoning on the acquired data and patient history, while a microchip antenna ensures the wireless transmission of measurements and diagnosis to a remote healthcare center. The characterization of the realized electrodes and the electronic readout are here shown: the results are compliant with the requirements of the standard medical equipment.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114712621","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 : 2023-06-08DOI: 10.1109/IWASI58316.2023.10164475
Cristian Turetta, Florenc Demrozi, Sofia Franceschi, Davide Zamboni, G. Pravadelli
The design of non-invasive systems for monitoring people’s activities is becoming of central interest in recent years. Such systems are essential for those affected by diseases that modify their cognitive status and are not collaborative in using wearable or interactive systems (e.g., mobile apps to communicate). This is particularly true regarding neurodegenerative diseases that involve memory loss, cognitive decline, communication difficulties, behavioral changes, loss of independence, and physical complications. In response to the need of healthcare structures and caregivers to monitor this category of people during their in-home daily life, this paper proposes a nonintrusive system capable of detecting whether or not a person is in his/her room and if he/she is lying on the bed. Checking these conditions is of utmost importance, in particular, during the night to support the monitoring activity of caregivers and social-health operators taking care of people with Dementia and Alzheimer’s disease. The proposed system exploits WiFi’s Channel State Information (CSI) gathered by common access points installed in the room. CSI data are then used to train a Convolutional Neural Network (CNN) and a fine-tuning technique is applied to increase the generalization capabilities of the CNN model on new environments. In our experimental analysis, we trained the CNN model by collecting CSI data in four different rooms, from two subjects performing three distinct activities. Promising results have been achieved (accuracy >97.5%) in recognizing the target activities.
{"title":"Non-Invasive Monitoring of Alzheimer’s patients through WiFi Channel State Information","authors":"Cristian Turetta, Florenc Demrozi, Sofia Franceschi, Davide Zamboni, G. Pravadelli","doi":"10.1109/IWASI58316.2023.10164475","DOIUrl":"https://doi.org/10.1109/IWASI58316.2023.10164475","url":null,"abstract":"The design of non-invasive systems for monitoring people’s activities is becoming of central interest in recent years. Such systems are essential for those affected by diseases that modify their cognitive status and are not collaborative in using wearable or interactive systems (e.g., mobile apps to communicate). This is particularly true regarding neurodegenerative diseases that involve memory loss, cognitive decline, communication difficulties, behavioral changes, loss of independence, and physical complications. In response to the need of healthcare structures and caregivers to monitor this category of people during their in-home daily life, this paper proposes a nonintrusive system capable of detecting whether or not a person is in his/her room and if he/she is lying on the bed. Checking these conditions is of utmost importance, in particular, during the night to support the monitoring activity of caregivers and social-health operators taking care of people with Dementia and Alzheimer’s disease. The proposed system exploits WiFi’s Channel State Information (CSI) gathered by common access points installed in the room. CSI data are then used to train a Convolutional Neural Network (CNN) and a fine-tuning technique is applied to increase the generalization capabilities of the CNN model on new environments. In our experimental analysis, we trained the CNN model by collecting CSI data in four different rooms, from two subjects performing three distinct activities. Promising results have been achieved (accuracy >97.5%) in recognizing the target activities.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128176436","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 : 2023-06-08DOI: 10.1016/B978-0-444-82471-4.50009-4
A. Al-Otaibi
{"title":"Opening Speech","authors":"A. Al-Otaibi","doi":"10.1016/B978-0-444-82471-4.50009-4","DOIUrl":"https://doi.org/10.1016/B978-0-444-82471-4.50009-4","url":null,"abstract":"","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132489313","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 : 2023-06-08DOI: 10.1109/IWASI58316.2023.10164436
Giorgia Subbicini, L. Lavagno, M. Lazarescu
Indoor human monitoring can enable or enhance a wide range of applications, from medical to security and home or building automation. For effective ubiquitous deployment, the monitoring system should be easy to install and unobtrusive, reliable, low cost, tagless, and privacy-aware. Long-range capacitive sensors are good candidates, but they can be susceptible to environmental electromagnetic noise and require special signal processing. Neural networks (NNs), especially 1D convolutional neural networks (1D-CNNs), excel at extracting information and rejecting noise, but they lose important relationships in max/average pooling operations. We investigate the performance of NN architectures for time series analysis without this shortcoming, the capsule networks that use dynamic routing, and the temporal convolutional networks (TCNs) that use dilated convolutions to preserve input resolution across layers and extend their receptive field with fewer layers. The networks are optimized for both inference accuracy and resource consumption using two independent state-of-the-art methods, neural architecture search and knowledge distillation. Experimental results show that the TCN architecture performs the best, achieving 12.7% lower inference loss with 73.3% less resource consumption than the best 1D-CNN when processing noisy capacitive sensor data for indoor human localization and tracking.
{"title":"Enhanced Exploration of Neural Network Models for Indoor Human Monitoring","authors":"Giorgia Subbicini, L. Lavagno, M. Lazarescu","doi":"10.1109/IWASI58316.2023.10164436","DOIUrl":"https://doi.org/10.1109/IWASI58316.2023.10164436","url":null,"abstract":"Indoor human monitoring can enable or enhance a wide range of applications, from medical to security and home or building automation. For effective ubiquitous deployment, the monitoring system should be easy to install and unobtrusive, reliable, low cost, tagless, and privacy-aware. Long-range capacitive sensors are good candidates, but they can be susceptible to environmental electromagnetic noise and require special signal processing. Neural networks (NNs), especially 1D convolutional neural networks (1D-CNNs), excel at extracting information and rejecting noise, but they lose important relationships in max/average pooling operations. We investigate the performance of NN architectures for time series analysis without this shortcoming, the capsule networks that use dynamic routing, and the temporal convolutional networks (TCNs) that use dilated convolutions to preserve input resolution across layers and extend their receptive field with fewer layers. The networks are optimized for both inference accuracy and resource consumption using two independent state-of-the-art methods, neural architecture search and knowledge distillation. Experimental results show that the TCN architecture performs the best, achieving 12.7% lower inference loss with 73.3% less resource consumption than the best 1D-CNN when processing noisy capacitive sensor data for indoor human localization and tracking.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128947856","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 : 2023-06-08DOI: 10.1109/IWASI58316.2023.10164492
Annabella la Grasta, M. D. Carlo, A. Nisio, Francesco Dell’Olio, V. Passaro
The ion-sensitive field-effect transistor (ISFET) is a well-established electronic device mainly used for pH sensing. However, its potential to detect other biomarkers in easily accessible biologic fluids with high accuracy and dynamic range is still an area of active research. In this study, we present the modeling of an ISFET that can detect the presence of chloride ions in sweat with a limit-of-detection of 0.004 mol/m3. The device is specifically designed to aid the diagnosis of cystic fibrosis, considering the interplay between the semiconductor and the electrolyte containing the ions of interest, using the finite element method. Our findings indicate that chloride ions directly interact with the hydroxyl surface groups of the gate oxide and replace protons previously adsorbed on the surface. The results suggest that this device could replace traditional sweat testing in the diagnosis and management of cystic fibrosis, as it is easy-to-use, cost-effective, and non-invasive, leading to earlier and more accurate diagnoses.
{"title":"Modeling and Design of a Ion-Sensitive Field-Effect Transistor for Chloride Ion Sensing","authors":"Annabella la Grasta, M. D. Carlo, A. Nisio, Francesco Dell’Olio, V. Passaro","doi":"10.1109/IWASI58316.2023.10164492","DOIUrl":"https://doi.org/10.1109/IWASI58316.2023.10164492","url":null,"abstract":"The ion-sensitive field-effect transistor (ISFET) is a well-established electronic device mainly used for pH sensing. However, its potential to detect other biomarkers in easily accessible biologic fluids with high accuracy and dynamic range is still an area of active research. In this study, we present the modeling of an ISFET that can detect the presence of chloride ions in sweat with a limit-of-detection of 0.004 mol/m3. The device is specifically designed to aid the diagnosis of cystic fibrosis, considering the interplay between the semiconductor and the electrolyte containing the ions of interest, using the finite element method. Our findings indicate that chloride ions directly interact with the hydroxyl surface groups of the gate oxide and replace protons previously adsorbed on the surface. The results suggest that this device could replace traditional sweat testing in the diagnosis and management of cystic fibrosis, as it is easy-to-use, cost-effective, and non-invasive, leading to earlier and more accurate diagnoses.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124559192","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 : 2023-06-08DOI: 10.1109/IWASI58316.2023.10164335
G. Loseto, F. Scioscia, M. Ruta, F. Gramegna, S. Ieva, Corrado Fasciano, Ivano Bilenchi, Davide Loconte, E. Sciascio
Internet of Things devices allow building increasingly large-scale sensor networks for gathering heterogeneous high-volume data streams. Artificial Intelligence (AI) applications typically collect them into centralized cloud infrastructures to run computationally intensive Machine Learning (ML) tasks. According to the emerging edge computing paradigm, instead, data preprocessing, model training and inference can be distributed among devices at the border of the local network, exploiting data locality to improve response latency, bandwidth usage and privacy, at the cost of suboptimal model accuracy due to smaller training sets. The paper proposes a cloud-edge framework for sensor-based AI applications, enabling a dynamic trade-off between edge and cloud layers by means of: (i) a novel containerized microservice architecture, allowing the execution of both model training and prediction either on edge or on cloud nodes; (ii) flexible automatic migration of tasks between the edge and the cloud, based on opportunistic management of resources and workloads. In order to facilitate implementations, a scouting of compatible device platforms for field sensing and edge computing nodes has been carried out, as well as a selection of suitable open-source off-the-shelf software tools. Early experiments validate the feasibility and core benefits of the proposal.
{"title":"A Cloud-Edge Artificial Intelligence Framework for Sensor Networks","authors":"G. Loseto, F. Scioscia, M. Ruta, F. Gramegna, S. Ieva, Corrado Fasciano, Ivano Bilenchi, Davide Loconte, E. Sciascio","doi":"10.1109/IWASI58316.2023.10164335","DOIUrl":"https://doi.org/10.1109/IWASI58316.2023.10164335","url":null,"abstract":"Internet of Things devices allow building increasingly large-scale sensor networks for gathering heterogeneous high-volume data streams. Artificial Intelligence (AI) applications typically collect them into centralized cloud infrastructures to run computationally intensive Machine Learning (ML) tasks. According to the emerging edge computing paradigm, instead, data preprocessing, model training and inference can be distributed among devices at the border of the local network, exploiting data locality to improve response latency, bandwidth usage and privacy, at the cost of suboptimal model accuracy due to smaller training sets. The paper proposes a cloud-edge framework for sensor-based AI applications, enabling a dynamic trade-off between edge and cloud layers by means of: (i) a novel containerized microservice architecture, allowing the execution of both model training and prediction either on edge or on cloud nodes; (ii) flexible automatic migration of tasks between the edge and the cloud, based on opportunistic management of resources and workloads. In order to facilitate implementations, a scouting of compatible device platforms for field sensing and edge computing nodes has been carried out, as well as a selection of suitable open-source off-the-shelf software tools. Early experiments validate the feasibility and core benefits of the proposal.","PeriodicalId":261827,"journal":{"name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125932375","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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