The Experts below are selected from a list of 741 Experts worldwide ranked by ideXlab platform
Niels Peek - One of the best experts on this subject based on the ideXlab platform.
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digital biomarkers from Geolocation Data in bipolar disorder and schizophrenia a systematic review
Journal of the American Medical Informatics Association, 2019Co-Authors: Anna L Beukenhorst, Paolo Fraccaro, Niels Peek, Sabine N Van Der Veer, Matthew Sperrin, Simon Harper, Jasper Palmierclaus, Shon LewisAbstract:OBJECTIVE: The study sought to explore to what extent Geolocation Data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based Geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS: We searched MEDLINE, PsycINFO, and Scopus by combining terms related to Geolocation and smartphones with SMI concepts. Study selection and Data extraction were done in duplicate. RESULTS: Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common Geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between Geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer Data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS: The growing body of evidence for the association between SMI concepts and Geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.
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behavioural phenotyping of daily activities relevant to social functioning based on smartphone collected Geolocation Data
Studies in health technology and informatics, 2019Co-Authors: Paolo Fraccaro, Stuart Laveryblackie, Sabine N Van Der Veer, Niels PeekAbstract:: Smartphones offer new opportunities to monitor health-related behaviours in the real world. This allows researchers to go beyond traditional Data collection methods, such as interviews and questionnaires that suffer from recall bias and low spatio-temporal resolution. In this study, we present an experiment that uses advanced analytical methods to identify daily activities relevant to assess social functioning, from Geolocation Data. Twenty-one healthy volunteers used a smartphone to continuously record their GPS location for up to 10 days. Participants also completed a diary to record their daily activities that was used as ground truth. Using clustering algorithms and semantic enrichment methods we were able to predict these activities from the GPS Data with a precision of 0.75 (standard deviation [SD] 0.13) and a recall of 0.60 (SD 0.11). Although performed on a limited sample, our study shows potential for continuous, and passive Geolocation-based monitoring of patient behaviour in mental health.
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MedInfo - Behavioural Phenotyping of Daily Activities Relevant to Social Functioning Based on Smartphone-Collected Geolocation Data.
Studies in health technology and informatics, 2019Co-Authors: Paolo Fraccaro, Sabine N Van Der Veer, Stuart Lavery-blackie, Niels PeekAbstract:Smartphones offer new opportunities to monitor health-related behaviours in the real world. This allows researchers to go beyond traditional Data collection methods, such as interviews and questionnaires that suffer from recall bias and low spatio-temporal resolution. In this study, we present an experiment that uses advanced analytical methods to identify daily activities relevant to assess social functioning, from Geolocation Data. Twenty-one healthy volunteers used a smartphone to continuously record their GPS location for up to 10 days. Participants also completed a diary to record their daily activities that was used as ground truth. Using clustering algorithms and semantic enrichment methods we were able to predict these activities from the GPS Data with a precision of 0.75 (standard deviation [SD] 0.13) and a recall of 0.60 (SD 0.11). Although performed on a limited sample, our study shows potential for continuous, and passive Geolocation-based monitoring of patient behaviour in mental health.
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the minimum sampling rate and sampling duration when applying Geolocation Data technology to human activity monitoring
Artificial Intelligence in Medicine in Europe, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation Data required to accurately recognise out-of-home activities. We collected Geolocation Data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each Data collection day. For finding the minimum sampling rate, we derived Datasets at lower sampling rates by down sampling the original Data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
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AIME - The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring
Artificial Intelligence in Medicine, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation Data required to accurately recognise out-of-home activities. We collected Geolocation Data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each Data collection day. For finding the minimum sampling rate, we derived Datasets at lower sampling rates by down sampling the original Data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
Paolo Fraccaro - One of the best experts on this subject based on the ideXlab platform.
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digital biomarkers from Geolocation Data in bipolar disorder and schizophrenia a systematic review
Journal of the American Medical Informatics Association, 2019Co-Authors: Anna L Beukenhorst, Paolo Fraccaro, Niels Peek, Sabine N Van Der Veer, Matthew Sperrin, Simon Harper, Jasper Palmierclaus, Shon LewisAbstract:OBJECTIVE: The study sought to explore to what extent Geolocation Data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based Geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS: We searched MEDLINE, PsycINFO, and Scopus by combining terms related to Geolocation and smartphones with SMI concepts. Study selection and Data extraction were done in duplicate. RESULTS: Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common Geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between Geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer Data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS: The growing body of evidence for the association between SMI concepts and Geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.
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behavioural phenotyping of daily activities relevant to social functioning based on smartphone collected Geolocation Data
Studies in health technology and informatics, 2019Co-Authors: Paolo Fraccaro, Stuart Laveryblackie, Sabine N Van Der Veer, Niels PeekAbstract:: Smartphones offer new opportunities to monitor health-related behaviours in the real world. This allows researchers to go beyond traditional Data collection methods, such as interviews and questionnaires that suffer from recall bias and low spatio-temporal resolution. In this study, we present an experiment that uses advanced analytical methods to identify daily activities relevant to assess social functioning, from Geolocation Data. Twenty-one healthy volunteers used a smartphone to continuously record their GPS location for up to 10 days. Participants also completed a diary to record their daily activities that was used as ground truth. Using clustering algorithms and semantic enrichment methods we were able to predict these activities from the GPS Data with a precision of 0.75 (standard deviation [SD] 0.13) and a recall of 0.60 (SD 0.11). Although performed on a limited sample, our study shows potential for continuous, and passive Geolocation-based monitoring of patient behaviour in mental health.
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MedInfo - Behavioural Phenotyping of Daily Activities Relevant to Social Functioning Based on Smartphone-Collected Geolocation Data.
Studies in health technology and informatics, 2019Co-Authors: Paolo Fraccaro, Sabine N Van Der Veer, Stuart Lavery-blackie, Niels PeekAbstract:Smartphones offer new opportunities to monitor health-related behaviours in the real world. This allows researchers to go beyond traditional Data collection methods, such as interviews and questionnaires that suffer from recall bias and low spatio-temporal resolution. In this study, we present an experiment that uses advanced analytical methods to identify daily activities relevant to assess social functioning, from Geolocation Data. Twenty-one healthy volunteers used a smartphone to continuously record their GPS location for up to 10 days. Participants also completed a diary to record their daily activities that was used as ground truth. Using clustering algorithms and semantic enrichment methods we were able to predict these activities from the GPS Data with a precision of 0.75 (standard deviation [SD] 0.13) and a recall of 0.60 (SD 0.11). Although performed on a limited sample, our study shows potential for continuous, and passive Geolocation-based monitoring of patient behaviour in mental health.
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the minimum sampling rate and sampling duration when applying Geolocation Data technology to human activity monitoring
Artificial Intelligence in Medicine in Europe, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation Data required to accurately recognise out-of-home activities. We collected Geolocation Data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each Data collection day. For finding the minimum sampling rate, we derived Datasets at lower sampling rates by down sampling the original Data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
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AIME - The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring
Artificial Intelligence in Medicine, 2019Co-Authors: Yan Zheng, Paolo Fraccaro, Niels PeekAbstract:The availability of Geolocation sensors embedded in smartphones introduces opportunities to monitor behaviours of individuals. However, sensing Geolocation at high sampling rates can affect the battery life of smartphones. In this study, we sought to explore the minimum sampling rate of Geolocation Data required to accurately recognise out-of-home activities. We collected Geolocation Data from 19 volunteers sampled every 10 s for 8 non-consecutive days on average. These volunteers were also instructed to complete a paper-based activity diary to record all activities during each Data collection day. For finding the minimum sampling rate, we derived Datasets at lower sampling rates by down sampling the original Data. A semantic analysis was applied using a previously published activity recognition algorithm. The impact of the sampling rates on accuracy of the algorithm was measured through the F1 score. The best F1 score was found at sampling intervals of 2 min and it did not drop substantially until the sampling intervals increased to 10 min. Our study proves the feasibility of monitoring activities at low sampling rates using smartphone-based Geolocation sensing.
Hu Chao - One of the best experts on this subject based on the ideXlab platform.
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ICCCS (2) - A Delay Step Based Geolocation Data Verification Method.
Cloud Computing and Security, 2018Co-Authors: Zhang Xiaoming, Wang Zhanfeng, Wei Xianglin, Zhuo Zihan, Hu ChaoAbstract:IP Geolocation technology is widely used in network security, especially in attack tracing, cyberspace security situation analysis and display, which has attracked attentions both in academic research and commercial applications. At present, there are many commercial or open source IP Geolocation Databases on the Internet. These Databases have different Geolocation accuracy, can not provide consistent positioning results, and seriously affect the user’s confidence in these Databases. To create an accurate Geolocation Database, a delay step based Geolocation Data verification method was proposed to find suspicious Data in the Geolocation Database, and then an adaptive localization algorithm is introduced to verify the questionable Data and improve the accuracy of Geolocation Database. Experiments show that by this method nearly half of the Geolocation Data can be verified, and results on the test Data show the proposed method can efficiently improve the Geolocation Database.
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a delay step based Geolocation Data verification method
International Conference on Cloud Computing, 2018Co-Authors: Zhang Xiaoming, Wang Zhanfeng, Wei Xianglin, Zhuo Zihan, Hu ChaoAbstract:IP Geolocation technology is widely used in network security, especially in attack tracing, cyberspace security situation analysis and display, which has attracked attentions both in academic research and commercial applications. At present, there are many commercial or open source IP Geolocation Databases on the Internet. These Databases have different Geolocation accuracy, can not provide consistent positioning results, and seriously affect the user’s confidence in these Databases. To create an accurate Geolocation Database, a delay step based Geolocation Data verification method was proposed to find suspicious Data in the Geolocation Database, and then an adaptive localization algorithm is introduced to verify the questionable Data and improve the accuracy of Geolocation Database. Experiments show that by this method nearly half of the Geolocation Data can be verified, and results on the test Data show the proposed method can efficiently improve the Geolocation Database.
Zhang Xiaoming - One of the best experts on this subject based on the ideXlab platform.
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ICCCS (2) - A Delay Step Based Geolocation Data Verification Method.
Cloud Computing and Security, 2018Co-Authors: Zhang Xiaoming, Wang Zhanfeng, Wei Xianglin, Zhuo Zihan, Hu ChaoAbstract:IP Geolocation technology is widely used in network security, especially in attack tracing, cyberspace security situation analysis and display, which has attracked attentions both in academic research and commercial applications. At present, there are many commercial or open source IP Geolocation Databases on the Internet. These Databases have different Geolocation accuracy, can not provide consistent positioning results, and seriously affect the user’s confidence in these Databases. To create an accurate Geolocation Database, a delay step based Geolocation Data verification method was proposed to find suspicious Data in the Geolocation Database, and then an adaptive localization algorithm is introduced to verify the questionable Data and improve the accuracy of Geolocation Database. Experiments show that by this method nearly half of the Geolocation Data can be verified, and results on the test Data show the proposed method can efficiently improve the Geolocation Database.
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a delay step based Geolocation Data verification method
International Conference on Cloud Computing, 2018Co-Authors: Zhang Xiaoming, Wang Zhanfeng, Wei Xianglin, Zhuo Zihan, Hu ChaoAbstract:IP Geolocation technology is widely used in network security, especially in attack tracing, cyberspace security situation analysis and display, which has attracked attentions both in academic research and commercial applications. At present, there are many commercial or open source IP Geolocation Databases on the Internet. These Databases have different Geolocation accuracy, can not provide consistent positioning results, and seriously affect the user’s confidence in these Databases. To create an accurate Geolocation Database, a delay step based Geolocation Data verification method was proposed to find suspicious Data in the Geolocation Database, and then an adaptive localization algorithm is introduced to verify the questionable Data and improve the accuracy of Geolocation Database. Experiments show that by this method nearly half of the Geolocation Data can be verified, and results on the test Data show the proposed method can efficiently improve the Geolocation Database.
Danya Bachir - One of the best experts on this subject based on the ideXlab platform.
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estimating urban mobility with mobile network Geolocation Data mining
2019Co-Authors: Danya BachirAbstract:In the upcoming decades, traffic and travel times are expected to skyrocket, following tremendous population growth in urban territories. The increasing congestion on transport networks threatens cities efficiency at several levels such as citizens well-being, health, economy, tourism and pollution. Thus, local and national authorities are urged to promote urban planning innovation by adopting supportive policies leading to effective and radical measures. Prior to decision making processes, it is crucial to estimate, analyze and understand daily urban mobility. Traditionally, the information on population movements has been gathered through national and local reports such as census and surveys. Still, such materials are constrained by their important cost, inducing extremely low-update frequency and lack of temporal variability. On the meantime, information and communications technologies are providing an unprecedented quantity of up-to-date mobility Data, across all categories of population. In particular, most individuals carry their mobile phone everywhere through their daily trips and activities. In this thesis, we estimate urban mobility by mining mobile network Data, which are collected in real-time by mobile phone providers at no extra-cost. Processing the raw Data is non-trivial as one must deal with temporal sparsity, coarse spatial precision and complex spatial noise. The thesis addresses two problematics through a weakly supervised learning scheme (i.e., using few labeled Data) combining several mobility Data sources. First, we estimate population densities and number of visitors over time, at fine spatio-temporal resolutions. Second, we derive Origin-Destination matrices representing total travel flows over time, per transport modes. All estimates are exhaustively validated against external mobility Data, with high correlations and small errors. Overall, the proposed models are robust to noise and sparse Data yet the performance highly depends on the choice of the spatial resolution. In addition, reaching optimal model performance requires extra-calibration specific to the case study region and to the transportation mode. This step is necessary to account for the bias induced by the joined effect of heterogeneous urban density and user behavior. Our work is the first successful attempt to characterize total road and rail passenger flows over time, at the intra-region level. Although additional in-depth validation is required to strengthen this statement, our findings highlight the huge potential of mobile network Data mining for urban planning applications.
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ECML/PKDD (3) - Combining Bayesian inference and clustering for transport mode detection from sparse and noisy Geolocation Data
Machine Learning and Knowledge Discovery in Databases, 2019Co-Authors: Danya Bachir, Ghazaleh Khodabandelou, Vincent Gauthier, Mounim El A Yacoubi, Eric VachonAbstract:Large-scale and real-time transport mode detection is an open challenge for smart transport research. Although massive mobility Data is collected from smartphones, mining mobile network Geolocation is non-trivial as it is a sparse, coarse and noisy Data for which real transport labels are unknown. In this study, we process billions of Call Detail Records from the Greater Paris and present the first method for transport mode detection of any traveling device. Cellphones trajectories, which are anonymized and aggregated, are constructed as sequences of visited locations, called sectors. Clustering and Bayesian inference are combined to estimate transport probabilities for each trajectory. First, we apply clustering on sectors. Features are constructed using spatial information from mobile networks and transport networks. Then, we extract a subset of \(15\%\) sectors, having road and rail labels (e.g., train stations), while remaining sectors are multi-modal. The proportion of labels per cluster is used to calculate transport probabilities given each visited sector. Thus, with Bayesian inference, each record updates the transport probability of the trajectory, without requiring the exact itinerary. For validation, we use the travel survey to compare daily average trips per user. With Pearson correlations reaching 0.96 for road and rail trips, the model appears performant and robust to noise and sparsity.
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combining bayesian inference and clustering for transport mode detection from sparse and noisy Geolocation Data
European Conference on Principles of Data Mining and Knowledge Discovery, 2018Co-Authors: Danya Bachir, Ghazaleh Khodabandelou, Vincent Gauthier, Mounim El A Yacoubi, Eric VachonAbstract:Large-scale and real-time transport mode detection is an open challenge for smart transport research. Although massive mobility Data is collected from smartphones, mining mobile network Geolocation is non-trivial as it is a sparse, coarse and noisy Data for which real transport labels are unknown. In this study, we process billions of Call Detail Records from the Greater Paris and present the first method for transport mode detection of any traveling device. Cellphones trajectories, which are anonymized and aggregated, are constructed as sequences of visited locations, called sectors. Clustering and Bayesian inference are combined to estimate transport probabilities for each trajectory. First, we apply clustering on sectors. Features are constructed using spatial information from mobile networks and transport networks. Then, we extract a subset of \(15\%\) sectors, having road and rail labels (e.g., train stations), while remaining sectors are multi-modal. The proportion of labels per cluster is used to calculate transport probabilities given each visited sector. Thus, with Bayesian inference, each record updates the transport probability of the trajectory, without requiring the exact itinerary. For validation, we use the travel survey to compare daily average trips per user. With Pearson correlations reaching 0.96 for road and rail trips, the model appears performant and robust to noise and sparsity.