Azziza Bankole, M. Anderson, Aubrey Knight, Kyunghui Oh, T. Smith-Jackson, M. Hanson, Adam T. Barth, J. Lach
Agitated behavior is one of the most frequent reasons that patients with dementia are placed in long-term care settings. These behaviors are indicators of distress and are associated with increased risk of injury to the patients and their caregivers. This study aims to explore the ability of a custom inertial wireless body sensor network (BSN) to objectively detect and quantify agitation, validating against currently accepted subjective clinical measures -- the Cohen-Mansfield Agitation Inventory (CMAI) and the Aggressive Behavior Scale (ABS) -- within the nursing home setting. The ultimate goal is to enable continuous, real-time monitoring of physical agitation in any location over an extended period. Continuous, longitudinal assessment facilitates timely response to agitation events in order to minimize patient distress and risk for injury, to more appropriately titrate pharmacotherapy, and to enable staff (or caregivers) to successfully intervene. Six patients identified as being at high risk for agitated behaviors were enrolled in this pilot study. Patients underwent a series of the above validated tests of memory and agitation. The BSN nodes were applied at three sites on body for three hours while behaviors were annotated simultaneously. This process was subsequently repeated twice for each enrolled subject. The BSN data was then processed using Teager energy analysis, which an earlier study suggested was a promising method for extracting jerky and repetitive movements from inertial data. Results based on construct validity testing for agitation (CMAI) and aggression (ABS) were promising and suggest that additional study with larger sample sizes is warranted.
{"title":"Continuous, non-invasive assessment of agitation in dementia using inertial body sensors","authors":"Azziza Bankole, M. Anderson, Aubrey Knight, Kyunghui Oh, T. Smith-Jackson, M. Hanson, Adam T. Barth, J. Lach","doi":"10.1145/2077546.2077548","DOIUrl":"https://doi.org/10.1145/2077546.2077548","url":null,"abstract":"Agitated behavior is one of the most frequent reasons that patients with dementia are placed in long-term care settings. These behaviors are indicators of distress and are associated with increased risk of injury to the patients and their caregivers. This study aims to explore the ability of a custom inertial wireless body sensor network (BSN) to objectively detect and quantify agitation, validating against currently accepted subjective clinical measures -- the Cohen-Mansfield Agitation Inventory (CMAI) and the Aggressive Behavior Scale (ABS) -- within the nursing home setting. The ultimate goal is to enable continuous, real-time monitoring of physical agitation in any location over an extended period. Continuous, longitudinal assessment facilitates timely response to agitation events in order to minimize patient distress and risk for injury, to more appropriately titrate pharmacotherapy, and to enable staff (or caregivers) to successfully intervene.\u0000 Six patients identified as being at high risk for agitated behaviors were enrolled in this pilot study. Patients underwent a series of the above validated tests of memory and agitation. The BSN nodes were applied at three sites on body for three hours while behaviors were annotated simultaneously. This process was subsequently repeated twice for each enrolled subject. The BSN data was then processed using Teager energy analysis, which an earlier study suggested was a promising method for extracting jerky and repetitive movements from inertial data. Results based on construct validity testing for agitation (CMAI) and aggression (ABS) were promising and suggest that additional study with larger sample sizes is warranted.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"43 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74465085","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}
Recently, several wireless body sensor-based systems have been proposed for continuous, long-term physiological monitoring. A major challenge in such systems is that a large amount of data is collected, and transmission of this data incurs significant energy consumption at the sensor. In this work, we demonstrate a data reporting method that significantly reduces energy consumption while maintaining a high diagnostic accuracy of the reported physiological signal. This is achieved by using a generative model of the physiological signal of interest at the sensor, and suppressing data transmission when sensed data matches the model. In this demonstration, we implement the proposed technique for electrocardiogram (ECG) signal and illustrate its performance in terms of energy savings and accuracy of reported data.
{"title":"Energy-efficient long term physiological monitoring","authors":"Ayan Banerjee, S. Nabar, S. Gupta, R. Poovendran","doi":"10.1145/2077546.2077566","DOIUrl":"https://doi.org/10.1145/2077546.2077566","url":null,"abstract":"Recently, several wireless body sensor-based systems have been proposed for continuous, long-term physiological monitoring. A major challenge in such systems is that a large amount of data is collected, and transmission of this data incurs significant energy consumption at the sensor. In this work, we demonstrate a data reporting method that significantly reduces energy consumption while maintaining a high diagnostic accuracy of the reported physiological signal. This is achieved by using a generative model of the physiological signal of interest at the sensor, and suppressing data transmission when sensed data matches the model. In this demonstration, we implement the proposed technique for electrocardiogram (ECG) signal and illustrate its performance in terms of energy savings and accuracy of reported data.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"9 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82109210","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}
Smart phone OEM camera technology enables PPG measurement of HR. HR readings acquired by a Motorola Droid#8482; using a self-developed Android HR application were compared to HR readings acquired by an electrocardiograph (ECG) and Nonin 9560BT pulse oximeter during three 5 min. periods (rest, reading aloud, video game). Subjects were 14, 20--58 year olds. Across all conditions, and device pairings correlations (rs≥.99). Bland-Altman plots revealed that 95% of the data points (differences between devices) fell within the limits of agreement when the Droid was compared to ECG. Lack of electrode patches or sensor telemetric straps and general ease of use make it advantageous for use in monitoring adherence to m-Health delivered health promotion and wellness programs (e.g., anxiety reduction, meditation, yoga, tai chi, etc.).
{"title":"Photoplethysmograph (PPG) derived heart rate (HR) acquisition using an Android smart phone","authors":"M. Gregoski, A. Vertegel, F. Treiber","doi":"10.1145/2077546.2077572","DOIUrl":"https://doi.org/10.1145/2077546.2077572","url":null,"abstract":"Smart phone OEM camera technology enables PPG measurement of HR. HR readings acquired by a Motorola Droid#8482; using a self-developed Android HR application were compared to HR readings acquired by an electrocardiograph (ECG) and Nonin 9560BT pulse oximeter during three 5 min. periods (rest, reading aloud, video game). Subjects were 14, 20--58 year olds. Across all conditions, and device pairings correlations (rs≥.99). Bland-Altman plots revealed that 95% of the data points (differences between devices) fell within the limits of agreement when the Droid was compared to ECG. Lack of electrode patches or sensor telemetric straps and general ease of use make it advantageous for use in monitoring adherence to m-Health delivered health promotion and wellness programs (e.g., anxiety reduction, meditation, yoga, tai chi, etc.).","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"4 1","pages":"23"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79517339","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}
Robert F. Dickerson, Eugenia I. Gorlin, J. Stankovic
Depression is a major health issue affecting over 21 million American adults that often goes untreated, and even when undergoing treatment it is hard to monitor the effectiveness of the treatment. To address these issues, we have created a real-time depression monitoring system for the home. This system runs 24/7 and can potentially detect the early signs of a depression episode, as well track progress managing a depressive illness. A cohesive set of integrated wireless sensors, a touch screen station, mobile device, and associated software deliver the above capabilities. The data collected are multi-modal, spanning a number of different behavioral domains including sleep, weight, activities of daily living, and speech prosody. The reports generated by this aggregated data across multiple behavioral domains are aimed to provide caregivers with more accurate and thorough information about the client's current functioning, thus helping in their diagnostic assessment and therapeutic treatment planning as well for patients in the management and tracking of their symptoms. We present data of a case study showing the value of the system, deployed over a period of two weeks in a home during a depressive episode. Larger scale studies are planned for the future.
{"title":"Empath: a continuous remote emotional health monitoring system for depressive illness","authors":"Robert F. Dickerson, Eugenia I. Gorlin, J. Stankovic","doi":"10.1145/2077546.2077552","DOIUrl":"https://doi.org/10.1145/2077546.2077552","url":null,"abstract":"Depression is a major health issue affecting over 21 million American adults that often goes untreated, and even when undergoing treatment it is hard to monitor the effectiveness of the treatment. To address these issues, we have created a real-time depression monitoring system for the home. This system runs 24/7 and can potentially detect the early signs of a depression episode, as well track progress managing a depressive illness. A cohesive set of integrated wireless sensors, a touch screen station, mobile device, and associated software deliver the above capabilities. The data collected are multi-modal, spanning a number of different behavioral domains including sleep, weight, activities of daily living, and speech prosody. The reports generated by this aggregated data across multiple behavioral domains are aimed to provide caregivers with more accurate and thorough information about the client's current functioning, thus helping in their diagnostic assessment and therapeutic treatment planning as well for patients in the management and tracking of their symptoms. We present data of a case study showing the value of the system, deployed over a period of two weeks in a home during a depressive episode. Larger scale studies are planned for the future.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"61 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82047301","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}
Shanshan Chen, Christopher L. Cunningham, B. Bennett, J. Lach
Ankle-foot orthoses (AFOs) are often prescribed to individuals with walking disabilities, including children with cerebral palsy. Despite the widespread use of AFOs, their efficacy is not well evaluated because the quantitative assessment of gait improvement from AFOs is currently limited to short-term, in-clinic observation. To better understand how AFOs perform in aiding individuals with walking disabilities and to further enhance their efficacy, longitudinal, continuous, non-invasive measurement is necessary. Ankle joint angle is a key parameter impacted by the AFO and is central to assessing AFO efficacy. With a wireless inertial body sensor network (BSN) mounted on -- or even embedded in -- the AFOs, the ankle joint angle can be extracted and then used to derive other gait parameters such as the range of ankle angle and the percentage of time in dorsi-/plantar-flexion mode. The methodology of extracting ankle joint angle and related gait parameters for assessing AFO efficacy is detailed in this paper. In order to obtain accurate spatial information, techniques for compensating integration drift, mounting error and multi-plane motion are also presented. The BSN results are validated against the industrial standard optical motion capture system on four children with cerebral palsy. An ankle angle RMSE of 2.41 degrees was achieved, demonstrating the potential of using BSNs for longitudinal assessment of AFO efficacy.
{"title":"Enabling longitudinal assessment of ankle-foot orthosis efficacy for children with cerebral palsy","authors":"Shanshan Chen, Christopher L. Cunningham, B. Bennett, J. Lach","doi":"10.1145/2077546.2077551","DOIUrl":"https://doi.org/10.1145/2077546.2077551","url":null,"abstract":"Ankle-foot orthoses (AFOs) are often prescribed to individuals with walking disabilities, including children with cerebral palsy. Despite the widespread use of AFOs, their efficacy is not well evaluated because the quantitative assessment of gait improvement from AFOs is currently limited to short-term, in-clinic observation. To better understand how AFOs perform in aiding individuals with walking disabilities and to further enhance their efficacy, longitudinal, continuous, non-invasive measurement is necessary.\u0000 Ankle joint angle is a key parameter impacted by the AFO and is central to assessing AFO efficacy. With a wireless inertial body sensor network (BSN) mounted on -- or even embedded in -- the AFOs, the ankle joint angle can be extracted and then used to derive other gait parameters such as the range of ankle angle and the percentage of time in dorsi-/plantar-flexion mode. The methodology of extracting ankle joint angle and related gait parameters for assessing AFO efficacy is detailed in this paper. In order to obtain accurate spatial information, techniques for compensating integration drift, mounting error and multi-plane motion are also presented. The BSN results are validated against the industrial standard optical motion capture system on four children with cerebral palsy. An ankle angle RMSE of 2.41 degrees was achieved, demonstrating the potential of using BSNs for longitudinal assessment of AFO efficacy.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"39 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87155663","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}
Wearable photoplethysmogram (PPG) sensors are extensively used for remote monitoring of blood oxygen level and flow rate in numerous pervasive healthcare applications with diverse quality of service requirements. These sensors operate under severe resource constraints and communicate over an adverse wireless channel with human body-induced path loss and mobility-caused fading. In this paper, we take a generative model-based data collection approach towards achieving energy-efficient and reliable PPG monitoring. We develop two models that can generate synthetic PPG signals given a set of input parameters. These generative models are then used to design and implement a resource-efficient, reliable data reporting method for wireless PPG sensors. We investigate the performance of our method under realistic wireless channel error models and provide methods to improve accuracy at a marginal energy cost. We implement the proposed technique using existing sensor platforms and evaluate its performance on two datasets: the MIMIC database and data collected using commercial wearable sensors. Results for wearable sensor-based data show bandwidth and communication energy savings of 300:1, while maintaining a diagnostic accuracy above 94%.
{"title":"Resource-efficient and reliable long term wireless monitoring of the photoplethysmographic signal","authors":"S. Nabar, Ayan Banerjee, S. Gupta, R. Poovendran","doi":"10.1145/2077546.2077556","DOIUrl":"https://doi.org/10.1145/2077546.2077556","url":null,"abstract":"Wearable photoplethysmogram (PPG) sensors are extensively used for remote monitoring of blood oxygen level and flow rate in numerous pervasive healthcare applications with diverse quality of service requirements. These sensors operate under severe resource constraints and communicate over an adverse wireless channel with human body-induced path loss and mobility-caused fading. In this paper, we take a generative model-based data collection approach towards achieving energy-efficient and reliable PPG monitoring. We develop two models that can generate synthetic PPG signals given a set of input parameters. These generative models are then used to design and implement a resource-efficient, reliable data reporting method for wireless PPG sensors. We investigate the performance of our method under realistic wireless channel error models and provide methods to improve accuracy at a marginal energy cost. We implement the proposed technique using existing sensor platforms and evaluate its performance on two datasets: the MIMIC database and data collected using commercial wearable sensors. Results for wearable sensor-based data show bandwidth and communication energy savings of 300:1, while maintaining a diagnostic accuracy above 94%.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"s3-46 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90838207","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}
Respiratory complications may occur in post-operative patients but are often difficult to detect due to the lack of a well-tolerated and reliable monitoring method. We have developed a wireless accelerometer-based device that measures chest wall rotations due to breathing and has been shown to provide an accurate measure of respiratory rate in a clinical setting, when validated against rates obtained from nasal pressure. This paper describes a method for detecting instances of respiratory problems due to physical airway obstruction and central nervous system (CNS) respiratory depression using a combination of nasal pressure and chest wall rotation signals in gynaecological patients during the first night after surgery. Waxing and waning of breath amplitude and irregularity of breath length are found to be useful indicators. A clinical study involving 19 post-operative patients shows that the proposed method is able to detect and distinguish between both forms of respiratory complications.
{"title":"Wireless monitoring of post-operative respiratory complications","authors":"A. Bates, D. Arvind, J. Mann","doi":"10.1145/2077546.2077549","DOIUrl":"https://doi.org/10.1145/2077546.2077549","url":null,"abstract":"Respiratory complications may occur in post-operative patients but are often difficult to detect due to the lack of a well-tolerated and reliable monitoring method. We have developed a wireless accelerometer-based device that measures chest wall rotations due to breathing and has been shown to provide an accurate measure of respiratory rate in a clinical setting, when validated against rates obtained from nasal pressure. This paper describes a method for detecting instances of respiratory problems due to physical airway obstruction and central nervous system (CNS) respiratory depression using a combination of nasal pressure and chest wall rotation signals in gynaecological patients during the first night after surgery. Waxing and waning of breath amplitude and irregularity of breath length are found to be useful indicators. A clinical study involving 19 post-operative patients shows that the proposed method is able to detect and distinguish between both forms of respiratory complications.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"30 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83601833","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}
James Y. Xu, Yuwen Sun, Zhao Wang, W. Kaiser, G. Pottie
Continued rapid progress in the development of embedded motion sensing enables wearable devices that provide fundamental advances in the capability to monitor and classify human motion, detect movement disorders, and estimate energy expenditure. With this progress, it is becoming possible to provide, for the first time, evaluation of outcomes of rehabilitation interventions and direct guidance for advancement of subject health, wellness, and safety. The progress in motion classification relies on both the performance of new sensor fusion methods that provide inference, and the energy efficiency of energy-constrained monitoring sensors. As will be described here, both of these objectives require advances in the capability of detecting and classifying the location and environmental context. Context directly enables both enhanced motion classification accuracy and speed through reduction in search space, and reduced energy demand through context-aware optimization of sensor sampling and operation schedules. There have been attempts to introduce context awareness into activity monitoring with limited success, due to the ambiguity in the definition of context, and the lack of a system architecture that enables the adaptation of signal processing and sensor fusion algorithms specific to the task of personalized activity monitoring. In this paper we present a novel end-to-end system that provides context guided personalized activity classification. With a refined concept of context, the system introduces interface models that feature a context classification committee, the concept of context specific activity classification, the ability to manage sensors given context, and the ability to operate in real time through web services. We also present an implementation that demonstrates accurate context classification, accurate activity classification using context specific models with improved accuracy and speed, and extended operating life through sensor energy management.
{"title":"Context guided and personalized activity classification system","authors":"James Y. Xu, Yuwen Sun, Zhao Wang, W. Kaiser, G. Pottie","doi":"10.1145/2077546.2077559","DOIUrl":"https://doi.org/10.1145/2077546.2077559","url":null,"abstract":"Continued rapid progress in the development of embedded motion sensing enables wearable devices that provide fundamental advances in the capability to monitor and classify human motion, detect movement disorders, and estimate energy expenditure. With this progress, it is becoming possible to provide, for the first time, evaluation of outcomes of rehabilitation interventions and direct guidance for advancement of subject health, wellness, and safety. The progress in motion classification relies on both the performance of new sensor fusion methods that provide inference, and the energy efficiency of energy-constrained monitoring sensors. As will be described here, both of these objectives require advances in the capability of detecting and classifying the location and environmental context. Context directly enables both enhanced motion classification accuracy and speed through reduction in search space, and reduced energy demand through context-aware optimization of sensor sampling and operation schedules. There have been attempts to introduce context awareness into activity monitoring with limited success, due to the ambiguity in the definition of context, and the lack of a system architecture that enables the adaptation of signal processing and sensor fusion algorithms specific to the task of personalized activity monitoring. In this paper we present a novel end-to-end system that provides context guided personalized activity classification. With a refined concept of context, the system introduces interface models that feature a context classification committee, the concept of context specific activity classification, the ability to manage sensors given context, and the ability to operate in real time through web services. We also present an implementation that demonstrates accurate context classification, accurate activity classification using context specific models with improved accuracy and speed, and extended operating life through sensor energy management.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"1 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89424689","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}
Measurement of gait parameters typically takes place in dedicated laboratory environments, which require space commitment, and expensive equipments such as multi-camera motion capture systems and force plates. Despite the high cost, a laboratory-bound approach limits the number of strides each person can take and the length of time the measurements last. To enable detailed measurement of gait in unconstrained environments, we are developing LEGSys#8482; (Locomotion Evaluation and Gait System), a portable gait evaluation system based on wearable motion sensors. LEGSys#8482; supports up to five small 9-degree-of-freedom (9DOF) inertial sensors to capture details of leg movements. The key advantages of this system include the capability of being used outside of gait laboratory, over ample walking distance and long duration of time, with different footwear conditions, and on different walking surfaces.
{"title":"LEGSys: wireless gait evaluation system using wearable sensors","authors":"Bor-rong Chen","doi":"10.1145/2077546.2077569","DOIUrl":"https://doi.org/10.1145/2077546.2077569","url":null,"abstract":"Measurement of gait parameters typically takes place in dedicated laboratory environments, which require space commitment, and expensive equipments such as multi-camera motion capture systems and force plates. Despite the high cost, a laboratory-bound approach limits the number of strides each person can take and the length of time the measurements last. To enable detailed measurement of gait in unconstrained environments, we are developing LEGSys#8482; (Locomotion Evaluation and Gait System), a portable gait evaluation system based on wearable motion sensors. LEGSys#8482; supports up to five small 9-degree-of-freedom (9DOF) inertial sensors to capture details of leg movements. The key advantages of this system include the capability of being used outside of gait laboratory, over ample walking distance and long duration of time, with different footwear conditions, and on different walking surfaces.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"158 1","pages":"20"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75057705","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}
Siddharth Gupta, Peter Chang, Nonso Anyigbo, A. Sabharwal
Respiratory diseases such as asthma are becoming more prevalent among both children and adults. Patients increasingly feel the need to monitor themselves and aid their diagnosis with more frequent measurements without the need to visit a clinic. Spirometry and its accurate interpretation are necessary in addressing this issue, and smartphones and other mobile platforms can economically increase access to these valuable measurements. We will demonstrate mobile-Spiro, a multi-configuration Android-based portable spirometer which allows for self-patient monitoring of respiratory conditions. In particular, mobileSpiro takes real-time measurements of lung capacity and assists in monitoring for potential disorders. Additionally, the results of each spirometric maneuver are transmitted to a remote server. As an open-source, open-interface spirometer, the system encourages innovation at a vastly decreased cost of deployment.
{"title":"mobileSpiro: portable open-interface spirometry for Android","authors":"Siddharth Gupta, Peter Chang, Nonso Anyigbo, A. Sabharwal","doi":"10.1145/2077546.2077573","DOIUrl":"https://doi.org/10.1145/2077546.2077573","url":null,"abstract":"Respiratory diseases such as asthma are becoming more prevalent among both children and adults. Patients increasingly feel the need to monitor themselves and aid their diagnosis with more frequent measurements without the need to visit a clinic. Spirometry and its accurate interpretation are necessary in addressing this issue, and smartphones and other mobile platforms can economically increase access to these valuable measurements. We will demonstrate mobile-Spiro, a multi-configuration Android-based portable spirometer which allows for self-patient monitoring of respiratory conditions. In particular, mobileSpiro takes real-time measurements of lung capacity and assists in monitoring for potential disorders. Additionally, the results of each spirometric maneuver are transmitted to a remote server. As an open-source, open-interface spirometer, the system encourages innovation at a vastly decreased cost of deployment.","PeriodicalId":91386,"journal":{"name":"Proceedings Wireless Health ... [electronic resource]. Wireless Health (Conference)","volume":"354 1","pages":"24"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76482228","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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