Rita H. Wouhaybi, M. Yarvis, Philip Muse, Chieh-Yih Wan, Sangita Sharma, Sai Prasad, Lenitra M. Durham, R. Sahni, R. Norton, Merlin Curry, H. Jimison, R. Harper, R. Lowe
Patient care can be intense and stressful, especially in emergency care situations. Emergency care has two parts, field care by a paramedic and in-hospital care. Paramedics often consult with physicians before the patient reaches the hospital. To do this effectively, they must convey the patient's condition rapidly and effectively. Upon hospital arrival they must also transfer as much patient data as possible to ensure continuation of care. In this paper, we present a context-management framework for telemedicine that is designed to capture sensor data for transfer to a remote location. We further describe an application developed on top of the framework for emergency medicine. We examine design considerations for the application based on collaboration with medical personnel. Finally, we present technical results obtained from use of the technology in simulated emergency scenarios.
{"title":"A context-management framework for telemedicine: an emergency medicine case study","authors":"Rita H. Wouhaybi, M. Yarvis, Philip Muse, Chieh-Yih Wan, Sangita Sharma, Sai Prasad, Lenitra M. Durham, R. Sahni, R. Norton, Merlin Curry, H. Jimison, R. Harper, R. Lowe","doi":"10.1145/1921081.1921101","DOIUrl":"https://doi.org/10.1145/1921081.1921101","url":null,"abstract":"Patient care can be intense and stressful, especially in emergency care situations. Emergency care has two parts, field care by a paramedic and in-hospital care. Paramedics often consult with physicians before the patient reaches the hospital. To do this effectively, they must convey the patient's condition rapidly and effectively. Upon hospital arrival they must also transfer as much patient data as possible to ensure continuation of care. In this paper, we present a context-management framework for telemedicine that is designed to capture sensor data for transfer to a remote location. We further describe an application developed on top of the framework for emergency medicine. We examine design considerations for the application based on collaboration with medical personnel. Finally, we present technical results obtained from use of the technology in simulated emergency scenarios.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"38 1","pages":"164-173"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72555337","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}
We have seen the rise of smartphones and highly capable mobile computing platforms over a relatively short period of time. These devices are quickly becoming ubiquitous and already enable access to basic sensors to augment the excellent communication and computing capabilities. However, advanced sensor interaction has been limited. Readily available environment and health sensors cannot effectively leverage the smartphone yet. One such health application that holds much promise is breath analysis through laser absorption spectroscopy (LAS). Breath analysis can help augment existing diagnostic techniques with early detection. Technology advances have led to the development of portable LAS sensors that can monitor the user's health and the environment. Our demonstration will show such a portable laser sensor along with an event-driven data collection architecture to enable future personalized health applications.
{"title":"Breath analysis with laser sensors on an Android platform","authors":"Siddharth Gupta, P. Breen, Duo Wu, A. Sabharwal","doi":"10.1145/1921081.1921111","DOIUrl":"https://doi.org/10.1145/1921081.1921111","url":null,"abstract":"We have seen the rise of smartphones and highly capable mobile computing platforms over a relatively short period of time. These devices are quickly becoming ubiquitous and already enable access to basic sensors to augment the excellent communication and computing capabilities. However, advanced sensor interaction has been limited. Readily available environment and health sensors cannot effectively leverage the smartphone yet. One such health application that holds much promise is breath analysis through laser absorption spectroscopy (LAS). Breath analysis can help augment existing diagnostic techniques with early detection. Technology advances have led to the development of portable LAS sensors that can monitor the user's health and the environment. Our demonstration will show such a portable laser sensor along with an event-driven data collection architecture to enable future personalized health applications.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"110 1","pages":"200-201"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75478099","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}
In this work, we consider low power, wearable pulse oximeter sensors for ambulatory, remote vital signs monitoring applications. It is extremely important for such sensors to maintain clinical accuracy and yet provide power savings to enable non-intrusive, long lasting sensors. Our contributions in this work include sub-Nyquist, random sampling of evanescent red and infra red (IR) photoplethysmograph (PPG) signals in real time under the Compressed Sensing (CS) paradigm. We describe the real time platform and demonstrate that the SpO2 accuracy is not compromised due to aliasing of ambient light artifacts, even when average number of measurements is much below that of Nyquist rate. We briefly discuss the various modules contributing to overall power consumption of a wireless pulse oximeter sensor and show that 10x reductions in LED power and radio power are possible, without sacrificing the SpO2 accuracy.
{"title":"Blood oxygen estimation from compressively sensed photoplethysmograph","authors":"P. Baheti, H. Garudadri, Somdeb Majumdar","doi":"10.1145/1921081.1921084","DOIUrl":"https://doi.org/10.1145/1921081.1921084","url":null,"abstract":"In this work, we consider low power, wearable pulse oximeter sensors for ambulatory, remote vital signs monitoring applications. It is extremely important for such sensors to maintain clinical accuracy and yet provide power savings to enable non-intrusive, long lasting sensors. Our contributions in this work include sub-Nyquist, random sampling of evanescent red and infra red (IR) photoplethysmograph (PPG) signals in real time under the Compressed Sensing (CS) paradigm. We describe the real time platform and demonstrate that the SpO2 accuracy is not compromised due to aliasing of ambient light artifacts, even when average number of measurements is much below that of Nyquist rate. We briefly discuss the various modules contributing to overall power consumption of a wireless pulse oximeter sensor and show that 10x reductions in LED power and radio power are possible, without sacrificing the SpO2 accuracy.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"42 1","pages":"10-14"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74180883","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}
The advancement of precision micropower amplifiers, microcontrollers, and MEMs devices have allowed for a paradigm shift from traditionally large and costly health monitoring equipment only found in hospitals or care centers to smaller, wireless, low powered portable devices that can provide continuous monitoring for a number of applications. Along these lines, we have developed a small wireless health monitoring device, named The Berkeley Tricorder, capable of monitoring a wide range of health-related signals, and have vetted it in a number of human trials. We will present a number of different real-time visualization tools that have been developed, and discuss some relevant applications for the Tricorder as a platform. Real time wireless telemetry from the device will be demonstrated.
{"title":"The Berkeley Tricorder: wireless health monitoring","authors":"Reza Naima, J. Canny","doi":"10.1145/1921081.1921117","DOIUrl":"https://doi.org/10.1145/1921081.1921117","url":null,"abstract":"The advancement of precision micropower amplifiers, microcontrollers, and MEMs devices have allowed for a paradigm shift from traditionally large and costly health monitoring equipment only found in hospitals or care centers to smaller, wireless, low powered portable devices that can provide continuous monitoring for a number of applications. Along these lines, we have developed a small wireless health monitoring device, named The Berkeley Tricorder, capable of monitoring a wide range of health-related signals, and have vetted it in a number of human trials. We will present a number of different real-time visualization tools that have been developed, and discuss some relevant applications for the Tricorder as a platform. Real time wireless telemetry from the device will be demonstrated.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"28 1","pages":"212-213"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90935065","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}
Zhihao Jiang, M. Pajic, Allison T. Connolly, S. Dixit, R. Mangharam
We present the design of an integrated modeling platform to investigate efficient methodologies for certifying medical device software. The outcome of this research has the potential to expedite medical device software certification for safer operation. Our specific focus in this study is on our ongoing research in artificial pacemaker software. Designing bug-free medical device software is difficult, especially in complex implantable devices that may be used in unanticipated contexts. In the 20-year period from 1985 to 2005, the US Food and Drug Administration's (FDA) Maude database records almost 30,000 deaths and almost 600,000 injuries from device failures [1]. There is currently no formal methodology or open experimental platform to validate and verify the correct operation of medical device software. To this effect, a real-time Virtual Heart Model (VHM) has been developed to model the electrophysiological operation of the functioning (i.e. during normal sinus rhythm) and malfunctioning (i.e. during arrhythmia) heart. We present a methodology to extract timing properties of the heart to construct a timed-automata model. The platform exposes functional and formal interfaces for validation and verification of implantable cardiac devices. We demonstrate the VHM is capable of generating clinically-relevant response to intrinsic (i.e. premature stimuli) and external (i.e. artificial pacemaker) signals for a variety of common arrhythmias. By connecting the VHM with a pacemaker model, we are able to pace and synchronize the heart during the onset of irregular heart rhythms. The VHM has been implemented on a hardware platform for closed-loop experimentation with existing and virtual medical devices.
{"title":"A platform for implantable medical device validation: demo abstract","authors":"Zhihao Jiang, M. Pajic, Allison T. Connolly, S. Dixit, R. Mangharam","doi":"10.1145/1921081.1921115","DOIUrl":"https://doi.org/10.1145/1921081.1921115","url":null,"abstract":"We present the design of an integrated modeling platform to investigate efficient methodologies for certifying medical device software. The outcome of this research has the potential to expedite medical device software certification for safer operation. Our specific focus in this study is on our ongoing research in artificial pacemaker software. Designing bug-free medical device software is difficult, especially in complex implantable devices that may be used in unanticipated contexts. In the 20-year period from 1985 to 2005, the US Food and Drug Administration's (FDA) Maude database records almost 30,000 deaths and almost 600,000 injuries from device failures [1]. There is currently no formal methodology or open experimental platform to validate and verify the correct operation of medical device software. To this effect, a real-time Virtual Heart Model (VHM) has been developed to model the electrophysiological operation of the functioning (i.e. during normal sinus rhythm) and malfunctioning (i.e. during arrhythmia) heart. We present a methodology to extract timing properties of the heart to construct a timed-automata model. The platform exposes functional and formal interfaces for validation and verification of implantable cardiac devices. We demonstrate the VHM is capable of generating clinically-relevant response to intrinsic (i.e. premature stimuli) and external (i.e. artificial pacemaker) signals for a variety of common arrhythmias. By connecting the VHM with a pacemaker model, we are able to pace and synchronize the heart during the onset of irregular heart rhythms. The VHM has been implemented on a hardware platform for closed-loop experimentation with existing and virtual medical devices.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"3 1","pages":"208-209"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87445694","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}
Two approaches are presented and compared for segmenting motion data from on-body Orient wireless motion capture system for mobile gait analysis. The first is a basic, model-based algorithm which operates directly on the joint angles computed by the Orient sensor devices. The second is a model-free, Latent Space algorithm, which first aggregates all the sensor data, and then embeds them in a low-dimensional manifold to perform segmentation. The two approaches are compared for segmenting four different styles of walking, and then applied in a hospital-based clinical study for analysing the motion of elderly patients recovering from a fall.
{"title":"Comparative study of segmentation of periodic motion data for mobile gait analysis","authors":"Aris Valtazanos, D. Arvind, S. Ramamoorthy","doi":"10.1145/1921081.1921099","DOIUrl":"https://doi.org/10.1145/1921081.1921099","url":null,"abstract":"Two approaches are presented and compared for segmenting motion data from on-body Orient wireless motion capture system for mobile gait analysis. The first is a basic, model-based algorithm which operates directly on the joint angles computed by the Orient sensor devices. The second is a model-free, Latent Space algorithm, which first aggregates all the sensor data, and then embeds them in a low-dimensional manifold to perform segmentation. The two approaches are compared for segmenting four different styles of walking, and then applied in a hospital-based clinical study for analysing the motion of elderly patients recovering from a fall.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"54 1","pages":"145-154"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83243118","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}
P. Ganapathy, J. Yadegar, Niranajan Kamath, Shantanu H. Joshi, C. Caluser
We propose to develop a portable, handheld, noninvasive solution for accurate screening and real-time monitoring of traumatic brain injury (TBI) in ambulatory/emergency response scenarios. A layered sensing concept that unifies modalities such as a) ultrasound (US) (B-mode, Doppler flow), b) tonometry and c) pulse oximeter to predict TBI, its severity and mode of recommendations for emergency medical service (EMS) personnel is currently investigated. Specifically, we aim to determine novel 3D morphometric parameters of optic nerve sheath (ONS) that can predict elevated intracranial pressure (EICP) from US data. These parameters when combined with intraocular pressure (IOP), blood oxygen saturation (SaO2) and Doppler flow readings of the carotid artery can improve the overall classification accuracy. In addition, we have also developed a preliminary decision-support system (DSS) to provide an automated analysis of subject's brain health status and thereby, recommend further screening, etc. In the demo, we would show the chain of processing starting from capture of our desired signals from a volunteer, pre-processing (reformatting, de-noising) of US data, post-processing of features extracted from the 3D US model and finally, the classification output of the DSS.
{"title":"A multimodal sensing system for detection of traumatic brain injury","authors":"P. Ganapathy, J. Yadegar, Niranajan Kamath, Shantanu H. Joshi, C. Caluser","doi":"10.1145/1921081.1921110","DOIUrl":"https://doi.org/10.1145/1921081.1921110","url":null,"abstract":"We propose to develop a portable, handheld, noninvasive solution for accurate screening and real-time monitoring of traumatic brain injury (TBI) in ambulatory/emergency response scenarios. A layered sensing concept that unifies modalities such as a) ultrasound (US) (B-mode, Doppler flow), b) tonometry and c) pulse oximeter to predict TBI, its severity and mode of recommendations for emergency medical service (EMS) personnel is currently investigated. Specifically, we aim to determine novel 3D morphometric parameters of optic nerve sheath (ONS) that can predict elevated intracranial pressure (EICP) from US data. These parameters when combined with intraocular pressure (IOP), blood oxygen saturation (SaO2) and Doppler flow readings of the carotid artery can improve the overall classification accuracy. In addition, we have also developed a preliminary decision-support system (DSS) to provide an automated analysis of subject's brain health status and thereby, recommend further screening, etc. In the demo, we would show the chain of processing starting from capture of our desired signals from a volunteer, pre-processing (reformatting, de-noising) of US data, post-processing of features extracted from the 3D US model and finally, the classification output of the DSS.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"20 1","pages":"198-199"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86063731","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}
John Hicks, N. Ramanathan, Donnie H. Kim, Mohamad Monibi, J. Selsky, Mark H. Hansen, D. Estrin
Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.
{"title":"AndWellness: an open mobile system for activity and experience sampling","authors":"John Hicks, N. Ramanathan, Donnie H. Kim, Mohamad Monibi, J. Selsky, Mark H. Hansen, D. Estrin","doi":"10.1145/1921081.1921087","DOIUrl":"https://doi.org/10.1145/1921081.1921087","url":null,"abstract":"Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"19 1","pages":"34-43"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83517997","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}
Many innovative mobile health applications can be enabled by augmenting wireless body sensors to mobile phones, e.g. monitoring personal fitness with on-body accelerometer and EKG sensors. However, it is difficult for the majority of smartphone developers to program wireless body sensors directly; current sensor nodes require developers to master node-level programming, implement the communication between the smartphone and sensors, and even learn new languages. The large gap between existing programming styles for smartphones and sensors prevents body sensors from being widely adopted by smartphone applications, despite the burgeoning Apple App Store and Android Market. To bridge this programming gap, we present Dandelion1, a novel framework for developing wireless body sensor applications on smartphones. Dandelion provides three major benefits: 1) platform-agnostic programming abstraction for in-sensor data processing, called senselet, 2) transparent integration of senselets and the smartphone code, and 3) platform-independent development and distribution of senselets. We provide an implementation of Dandelion on the Maemo Linux smartphone platform and the Rice Orbit body sensor platform. We evaluate Dandelion by implementing real-world applications, and show that Dandelion effectively eliminates the programming gap and significantly reduces the development efforts. We further show that Dandelion incurs a very small overhead; in total less than 5% of the memory capacity and less than 3% of the processor time of a typical ultra low power sensor.
{"title":"Dandelion: a framework for transparently programming phone-centered wireless body sensor applications for health","authors":"F. Lin, Ahmad Rahmati, Lin Zhong","doi":"10.1145/1921081.1921091","DOIUrl":"https://doi.org/10.1145/1921081.1921091","url":null,"abstract":"Many innovative mobile health applications can be enabled by augmenting wireless body sensors to mobile phones, e.g. monitoring personal fitness with on-body accelerometer and EKG sensors. However, it is difficult for the majority of smartphone developers to program wireless body sensors directly; current sensor nodes require developers to master node-level programming, implement the communication between the smartphone and sensors, and even learn new languages. The large gap between existing programming styles for smartphones and sensors prevents body sensors from being widely adopted by smartphone applications, despite the burgeoning Apple App Store and Android Market.\u0000 To bridge this programming gap, we present Dandelion1, a novel framework for developing wireless body sensor applications on smartphones. Dandelion provides three major benefits: 1) platform-agnostic programming abstraction for in-sensor data processing, called senselet, 2) transparent integration of senselets and the smartphone code, and 3) platform-independent development and distribution of senselets.\u0000 We provide an implementation of Dandelion on the Maemo Linux smartphone platform and the Rice Orbit body sensor platform. We evaluate Dandelion by implementing real-world applications, and show that Dandelion effectively eliminates the programming gap and significantly reduces the development efforts. We further show that Dandelion incurs a very small overhead; in total less than 5% of the memory capacity and less than 3% of the processor time of a typical ultra low power sensor.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"59 1","pages":"74-83"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88513644","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}
Jingyuan Li, Tejaswi Tamminedi, Guy Yosiphon, A. Ganguli, Lei Zhang, J. Stankovic, J. Yadegar
First responders have been observed to be at increased risk of cardio-vascular diseases compared to the general population. A high percentage of cardiac events have been found to occur during missions. Continuous physiological monitoring during missions can be effective in reducing the number of fatalities. Real-time physiological data such as ECG can be collected using body-worn sensors. This sensor data can be processed on the body itself or can be communicated over an ad hoc wireless network to the incident command center located nearby. First responder missions often take place inside building structures where network connectivity is intermittent. Intermittent connectivity can lead to loss of critical physiological data or delay in that information reaching the base station. Hence, some amount of local processing is needed in order to limit the amount of data that is communicated. In this paper, we introduce a novel Hidden Markov Model based classifier for myocardial infarction detection. The classifier fidelity can be adapted based on the processing power available. We present a peer-to-peer networking protocol for communication over disrupted networks. A low fidelity classifier is used to perform local processing and assign priorities to the data based on its criticality. It is complemented by a disruption-aware epidemic forwarding protocol for transferring first responder's physiological data to the base station. We show that with prioritized epidemic forwarding and buffer eviction policy, packet delivery ratio for abnormal data increases and the latency associated with abnormal packets reaching the destination decreases.
{"title":"Remote physiological monitoring of first responders with intermittent network connectivity","authors":"Jingyuan Li, Tejaswi Tamminedi, Guy Yosiphon, A. Ganguli, Lei Zhang, J. Stankovic, J. Yadegar","doi":"10.1145/1921081.1921090","DOIUrl":"https://doi.org/10.1145/1921081.1921090","url":null,"abstract":"First responders have been observed to be at increased risk of cardio-vascular diseases compared to the general population. A high percentage of cardiac events have been found to occur during missions. Continuous physiological monitoring during missions can be effective in reducing the number of fatalities. Real-time physiological data such as ECG can be collected using body-worn sensors. This sensor data can be processed on the body itself or can be communicated over an ad hoc wireless network to the incident command center located nearby. First responder missions often take place inside building structures where network connectivity is intermittent. Intermittent connectivity can lead to loss of critical physiological data or delay in that information reaching the base station. Hence, some amount of local processing is needed in order to limit the amount of data that is communicated. In this paper, we introduce a novel Hidden Markov Model based classifier for myocardial infarction detection. The classifier fidelity can be adapted based on the processing power available. We present a peer-to-peer networking protocol for communication over disrupted networks. A low fidelity classifier is used to perform local processing and assign priorities to the data based on its criticality. It is complemented by a disruption-aware epidemic forwarding protocol for transferring first responder's physiological data to the base station. We show that with prioritized epidemic forwarding and buffer eviction policy, packet delivery ratio for abnormal data increases and the latency associated with abnormal packets reaching the destination decreases.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"9 1","pages":"64-73"},"PeriodicalIF":0.0,"publicationDate":"2010-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86968767","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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