The Experts below are selected from a list of 4989 Experts worldwide ranked by ideXlab platform
Takeshi Kurata - One of the best experts on this subject based on the ideXlab platform.
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personal positioning based on walking locomotion analysis with self contained sensors and a Wearable Camera
2003Co-Authors: Masakatsu Kourogi, Takeshi KurataAbstract:In this paper, we propose a method of personal positioning for a Wearable augmented reality (AR) system that allows a user to freely move around indoors and outdoors. The user is equipped with self-contained sensors, a Wearable Camera, an inertial head tracker and display. The method is based on sensor fusion of estimates for relative displacement caused by human walking locomotion and estimates for absolute position and orientation within a Kalman filtering framework. The former is based on intensive analysis of human walking behavior using self-contained sensors. The latter is based on image matching of video frames from a Wearable Camera with an image database that was prepared beforehand.
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a method of personal positioning based on sensor data fusion of Wearable Camera and self contained sensors
2003Co-Authors: Masakatsu Kourogi, Takeshi KurataAbstract:In this paper, we propose a method of personal positioning that combines images taken from a Wearable Camera with data from self-contained sensors attached to the user through a Kalman filter as a data integration mechanism. The proposed method estimates the user's position and direction by image registration between the input images from the Camera and a set of images captured at known positions and directions beforehand as a database. It updates the estimation of the user's position and direction with pedestrian dead-reckoning by detecting walking behavior of the user and by estimating the heading direction of the body with the self-contained sensors.
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A Wearable augmented reality system with personal positioning based on walking locomotion analysis
2003Co-Authors: Masakatsu Kourogi, Takeshi KurataAbstract:In this paper, we present a Wearable augmented reality (AR) system with personal positioning based on walking locomotion analysis that allows a user to freely mover around indoors and outdoors. The user is equipped with self-contained sensors, a Wearable Camera, an inertial head tracker and display. The system is based on the sensor fusion of estimates for relative displacement caused by human walking locomotion and estimates for absolute position and orientation within a Kalman filtering framework. The former is based on intensive analysis of human walking behavior using self-contained sensors. The latter is based on image matching of video frames from a Wearable Camera with an image database that was prepared beforehand.
Cathryn Tonne - One of the best experts on this subject based on the ideXlab platform.
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day (when?), location (where?), and individuals’ activities (what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions (n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of...
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.
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integrated assessment of exposure to pm2 5 in south india and its relation with cardiovascular risk design of the chai observational cohort study
2017Co-Authors: Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Cathryn Tonne, Sankar Sambandam, Kalpana BalakrishnanAbstract:While there is convincing evidence that fine particulate matter causes cardiovascular mortality and morbidity, little of the evidence is based on populations outside of high income countries, leaving large uncertainties at high exposures. India is an attractive setting for investigating the cardiovascular risk of particles across a wide concentration range, including concentrations for which there is the largest uncertainty in the exposure-response relationship. CHAI is a European Research Council funded project that investigates the relationship between particulate air pollution from outdoor and household sources with markers of atherosclerosis, an important cardiovascular pathology. The project aims to (1) characterize the exposure of a cohort of adults to particulate air pollution from household and outdoor sources (2) integrate information from GPS, Wearable Cameras, and continuous measurements of personal exposure to particles to understand where and through which activities people are most exposed and (3) quantify the association between particles and markers of atherosclerosis. CHAI has the potential to make important methodological contributions to modeling air pollution exposure integrating outdoor and household sources as well as in the application of Wearable Camera data in environmental exposure assessment.
Maëlle Salmon - One of the best experts on this subject based on the ideXlab platform.
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day (when?), location (where?), and individuals’ activities (what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions (n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of...
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Wearable Camera-derived microenvironments in relation to personal exposure to PM2.5
2018Co-Authors: Maëlle Salmon, Santhi Bhogadi, Carles Milà, Srivalli Addanki, Pavitra Madhira, Niharika Muddepaka, Amaravathi Mora, Margaux Sanchez, Sanjay Kinra, V. SreekanthAbstract:Data regarding which microenvironments drive exposure to air pollution in low and middle income countries are scarce. Our objective was to identify sources of time-resolved personal PM2.5 exposure in peri-urban India using Wearable Camera-derived microenvironmental information. We conducted a panel study with up to 6 repeated non-consecutive 24 h measurements on 45 participants (186 participant-days). Camera images were manually annotated to derive visual concepts indicative of microenvironments and activities. Men had slightly higher daily mean PM2.5 exposure (43 μg/m3) compared to women (39 μg/m3). Cameras helped identify that men also had higher exposures when near a biomass cooking unit (mean (sd) μg/m3: 119 (383) for men vs 83 (196) for women) and presence in the kitchen (133 (311) for men vs 48 (94) for women). Visual concepts associated in regression analysis with higher 5-minute PM2.5 for both sexes included: smoking (+93% (95% confidence interval: 63%, 129%) in men, +29% (95% CI: 2%, 63%) in women), biomass cooking unit (+57% (95% CI: 28%, 93%) in men, +69% (95% CI: 48%, 93%) in women), visible flame or smoke (+90% (95% CI: 48%, 144%) in men, +39% (95% CI: 6%, 83%) in women), and presence in the kitchen (+49% (95% CI: 27%, 75%) in men, +14% (95% CI: 7%, 20%) in women). Our results indicate Wearable Cameras can provide objective, high time-resolution microenvironmental data useful for identifying peak exposures and providing insights not evident using standard self-reported time-activity.
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integrated assessment of exposure to pm2 5 in south india and its relation with cardiovascular risk design of the chai observational cohort study
2017Co-Authors: Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Cathryn Tonne, Sankar Sambandam, Kalpana BalakrishnanAbstract:While there is convincing evidence that fine particulate matter causes cardiovascular mortality and morbidity, little of the evidence is based on populations outside of high income countries, leaving large uncertainties at high exposures. India is an attractive setting for investigating the cardiovascular risk of particles across a wide concentration range, including concentrations for which there is the largest uncertainty in the exposure-response relationship. CHAI is a European Research Council funded project that investigates the relationship between particulate air pollution from outdoor and household sources with markers of atherosclerosis, an important cardiovascular pathology. The project aims to (1) characterize the exposure of a cohort of adults to particulate air pollution from household and outdoor sources (2) integrate information from GPS, Wearable Cameras, and continuous measurements of personal exposure to particles to understand where and through which activities people are most exposed and (3) quantify the association between particles and markers of atherosclerosis. CHAI has the potential to make important methodological contributions to modeling air pollution exposure integrating outdoor and household sources as well as in the application of Wearable Camera data in environmental exposure assessment.
Margaux Sanchez - One of the best experts on this subject based on the ideXlab platform.
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day (when?), location (where?), and individuals’ activities (what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions (n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of...
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Wearable Camera-derived microenvironments in relation to personal exposure to PM2.5
2018Co-Authors: Maëlle Salmon, Santhi Bhogadi, Carles Milà, Srivalli Addanki, Pavitra Madhira, Niharika Muddepaka, Amaravathi Mora, Margaux Sanchez, Sanjay Kinra, V. SreekanthAbstract:Data regarding which microenvironments drive exposure to air pollution in low and middle income countries are scarce. Our objective was to identify sources of time-resolved personal PM2.5 exposure in peri-urban India using Wearable Camera-derived microenvironmental information. We conducted a panel study with up to 6 repeated non-consecutive 24 h measurements on 45 participants (186 participant-days). Camera images were manually annotated to derive visual concepts indicative of microenvironments and activities. Men had slightly higher daily mean PM2.5 exposure (43 μg/m3) compared to women (39 μg/m3). Cameras helped identify that men also had higher exposures when near a biomass cooking unit (mean (sd) μg/m3: 119 (383) for men vs 83 (196) for women) and presence in the kitchen (133 (311) for men vs 48 (94) for women). Visual concepts associated in regression analysis with higher 5-minute PM2.5 for both sexes included: smoking (+93% (95% confidence interval: 63%, 129%) in men, +29% (95% CI: 2%, 63%) in women), biomass cooking unit (+57% (95% CI: 28%, 93%) in men, +69% (95% CI: 48%, 93%) in women), visible flame or smoke (+90% (95% CI: 48%, 144%) in men, +39% (95% CI: 6%, 83%) in women), and presence in the kitchen (+49% (95% CI: 27%, 75%) in men, +14% (95% CI: 7%, 20%) in women). Our results indicate Wearable Cameras can provide objective, high time-resolution microenvironmental data useful for identifying peak exposures and providing insights not evident using standard self-reported time-activity.
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integrated assessment of exposure to pm2 5 in south india and its relation with cardiovascular risk design of the chai observational cohort study
2017Co-Authors: Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Cathryn Tonne, Sankar Sambandam, Kalpana BalakrishnanAbstract:While there is convincing evidence that fine particulate matter causes cardiovascular mortality and morbidity, little of the evidence is based on populations outside of high income countries, leaving large uncertainties at high exposures. India is an attractive setting for investigating the cardiovascular risk of particles across a wide concentration range, including concentrations for which there is the largest uncertainty in the exposure-response relationship. CHAI is a European Research Council funded project that investigates the relationship between particulate air pollution from outdoor and household sources with markers of atherosclerosis, an important cardiovascular pathology. The project aims to (1) characterize the exposure of a cohort of adults to particulate air pollution from household and outdoor sources (2) integrate information from GPS, Wearable Cameras, and continuous measurements of personal exposure to particles to understand where and through which activities people are most exposed and (3) quantify the association between particles and markers of atherosclerosis. CHAI has the potential to make important methodological contributions to modeling air pollution exposure integrating outdoor and household sources as well as in the application of Wearable Camera data in environmental exposure assessment.
V. Sreekanth - One of the best experts on this subject based on the ideXlab platform.
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.
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when where and what characterizing personal pm2 5 exposure in periurban india by integrating gps Wearable Camera and ambient and personal monitoring data
2018Co-Authors: Carles Milà, Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Sanjay Kinra, Albert Ambros, Mark J Nieuwenhuijsen, Julian D Marshall, Cathryn TonneAbstract:Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day (when?), location (where?), and individuals’ activities (what?) to PM2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions (n = 227). We integrated data from multiple sources: continuous personal and ambient PM2.5 concentrations; questionnaire, GPS, and Wearable Camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m3 (SD 24.6) for men and 39.7 μg/m3 (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM2.5 was an important predictor of...
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Wearable Camera-derived microenvironments in relation to personal exposure to PM2.5
2018Co-Authors: Maëlle Salmon, Santhi Bhogadi, Carles Milà, Srivalli Addanki, Pavitra Madhira, Niharika Muddepaka, Amaravathi Mora, Margaux Sanchez, Sanjay Kinra, V. SreekanthAbstract:Data regarding which microenvironments drive exposure to air pollution in low and middle income countries are scarce. Our objective was to identify sources of time-resolved personal PM2.5 exposure in peri-urban India using Wearable Camera-derived microenvironmental information. We conducted a panel study with up to 6 repeated non-consecutive 24 h measurements on 45 participants (186 participant-days). Camera images were manually annotated to derive visual concepts indicative of microenvironments and activities. Men had slightly higher daily mean PM2.5 exposure (43 μg/m3) compared to women (39 μg/m3). Cameras helped identify that men also had higher exposures when near a biomass cooking unit (mean (sd) μg/m3: 119 (383) for men vs 83 (196) for women) and presence in the kitchen (133 (311) for men vs 48 (94) for women). Visual concepts associated in regression analysis with higher 5-minute PM2.5 for both sexes included: smoking (+93% (95% confidence interval: 63%, 129%) in men, +29% (95% CI: 2%, 63%) in women), biomass cooking unit (+57% (95% CI: 28%, 93%) in men, +69% (95% CI: 48%, 93%) in women), visible flame or smoke (+90% (95% CI: 48%, 144%) in men, +39% (95% CI: 6%, 83%) in women), and presence in the kitchen (+49% (95% CI: 27%, 75%) in men, +14% (95% CI: 7%, 20%) in women). Our results indicate Wearable Cameras can provide objective, high time-resolution microenvironmental data useful for identifying peak exposures and providing insights not evident using standard self-reported time-activity.
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integrated assessment of exposure to pm2 5 in south india and its relation with cardiovascular risk design of the chai observational cohort study
2017Co-Authors: Maëlle Salmon, V. Sreekanth, Santhi Bhogadi, Margaux Sanchez, Cathryn Tonne, Sankar Sambandam, Kalpana BalakrishnanAbstract:While there is convincing evidence that fine particulate matter causes cardiovascular mortality and morbidity, little of the evidence is based on populations outside of high income countries, leaving large uncertainties at high exposures. India is an attractive setting for investigating the cardiovascular risk of particles across a wide concentration range, including concentrations for which there is the largest uncertainty in the exposure-response relationship. CHAI is a European Research Council funded project that investigates the relationship between particulate air pollution from outdoor and household sources with markers of atherosclerosis, an important cardiovascular pathology. The project aims to (1) characterize the exposure of a cohort of adults to particulate air pollution from household and outdoor sources (2) integrate information from GPS, Wearable Cameras, and continuous measurements of personal exposure to particles to understand where and through which activities people are most exposed and (3) quantify the association between particles and markers of atherosclerosis. CHAI has the potential to make important methodological contributions to modeling air pollution exposure integrating outdoor and household sources as well as in the application of Wearable Camera data in environmental exposure assessment.
