ºÝºÝߣshows by User: marcoalt / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: marcoalt / Fri, 12 Mar 2021 12:13:07 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: marcoalt User generated data: a paradigm shift for research and data products /slideshow/user-generated-data-a-paradigm-shift-for-research-and-data-products/244290180 11-210312121308
In this deck I cover user-generated data, challenges and opportunities mostly in the context of sports science ]]>
In this deck I cover user-generated data, challenges and opportunities mostly in the context of sports science ]]> Fri, 12 Mar 2021 12:13:07 GMT /slideshow/user-generated-data-a-paradigm-shift-for-research-and-data-products/244290180 marcoalt@slideshare.net(marcoalt) User generated data: a paradigm shift for research and data products marcoalt In this deck I cover user-generated data, challenges and opportunities mostly in the context of sports science <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/11-210312121308-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this deck I cover user-generated data, challenges and opportunities mostly in the context of sports science
User generated data: a paradigm shift for research and data products from Marco Altini
]]> 489 0 https://cdn.slidesharecdn.com/ss_thumbnails/11-210312121308-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Heart Rate Variability (HRV) Analysis and HRV4Training /slideshow/heart-rate-variability-hrv-analysis-and-hrv4training/241490271 2021hrv4training-210118084028
> Physiological underpinnings of HRV > Technology for HRV (morning measurements, night measurements, camera-based, sensors, etc.) > Best practices for HRV data collection and analysis > Case studies > State of the art research for HRV-guided training > Practical examples with HRV4Training and Pro]]>
> Physiological underpinnings of HRV > Technology for HRV (morning measurements, night measurements, camera-based, sensors, etc.) > Best practices for HRV data collection and analysis > Case studies > State of the art research for HRV-guided training > Practical examples with HRV4Training and Pro]]> Mon, 18 Jan 2021 08:40:28 GMT /slideshow/heart-rate-variability-hrv-analysis-and-hrv4training/241490271 marcoalt@slideshare.net(marcoalt) Heart Rate Variability (HRV) Analysis and HRV4Training marcoalt > Physiological underpinnings of HRV > Technology for HRV (morning measurements, night measurements, camera-based, sensors, etc.) > Best practices for HRV data collection and analysis > Case studies > State of the art research for HRV-guided training > Practical examples with HRV4Training and Pro <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2021hrv4training-210118084028-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> &gt; Physiological underpinnings of HRV &gt; Technology for HRV (morning measurements, night measurements, camera-based, sensors, etc.) &gt; Best practices for HRV data collection and analysis &gt; Case studies &gt; State of the art research for HRV-guided training &gt; Practical examples with HRV4Training and Pro
Heart Rate Variability (HRV) Analysis and HRV4Training from Marco Altini
]]> 1448 0 https://cdn.slidesharecdn.com/ss_thumbnails/2021hrv4training-210118084028-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Players Guide | HRV4Training /slideshow/players-guide-hrv4training/238463094 hrv4traininghowtoplayers-200912092226
Short how-to guide for players of organizations relying on our platform to measure resting physiology (heart rate, heart rate variability). ]]>
Short how-to guide for players of organizations relying on our platform to measure resting physiology (heart rate, heart rate variability). ]]> Sat, 12 Sep 2020 09:22:26 GMT /slideshow/players-guide-hrv4training/238463094 marcoalt@slideshare.net(marcoalt) Players Guide | HRV4Training marcoalt Short how-to guide for players of organizations relying on our platform to measure resting physiology (heart rate, heart rate variability). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hrv4traininghowtoplayers-200912092226-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Short how-to guide for players of organizations relying on our platform to measure resting physiology (heart rate, heart rate variability).
Players Guide | HRV4Training from Marco Altini
]]> 7010 0 https://cdn.slidesharecdn.com/ss_thumbnails/hrv4traininghowtoplayers-200912092226-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Decoding human physiology: a decade of research /slideshow/decoding-human-physiology-a-decade-of-research-227841205/227841205 altiniresearch-200213091231
From hardware development to large-scale user-generated data and insights]]>
From hardware development to large-scale user-generated data and insights]]> Thu, 13 Feb 2020 09:12:31 GMT /slideshow/decoding-human-physiology-a-decade-of-research-227841205/227841205 marcoalt@slideshare.net(marcoalt) Decoding human physiology: a decade of research marcoalt From hardware development to large-scale user-generated data and insights <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/altiniresearch-200213091231-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> From hardware development to large-scale user-generated data and insights
Decoding human physiology: a decade of research from Marco Altini
]]> 1004 1 https://cdn.slidesharecdn.com/ss_thumbnails/altiniresearch-200213091231-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Analisi della variabilità cardiaca (HRV) /slideshow/analisi-della-variabilit-cardiaca-hrv/119234943 201810du-181012183841
Analisi della variabilità cardiaca & HRV4Training: panoramica su fisiologia, tecnologia, consigli pratici per l'utilizzo degli strumenti disponibili, e analisi di dati fisiologici per professionisti interessati all'utilizzo di queste metriche in campo sportivo e non solo.]]>
Analisi della variabilità cardiaca & HRV4Training: panoramica su fisiologia, tecnologia, consigli pratici per l'utilizzo degli strumenti disponibili, e analisi di dati fisiologici per professionisti interessati all'utilizzo di queste metriche in campo sportivo e non solo.]]> Fri, 12 Oct 2018 18:38:41 GMT /slideshow/analisi-della-variabilit-cardiaca-hrv/119234943 marcoalt@slideshare.net(marcoalt) Analisi della variabilità cardiaca (HRV) marcoalt Analisi della variabilità cardiaca & HRV4Training: panoramica su fisiologia, tecnologia, consigli pratici per l'utilizzo degli strumenti disponibili, e analisi di dati fisiologici per professionisti interessati all'utilizzo di queste metriche in campo sportivo e non solo. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/201810du-181012183841-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Analisi della variabilità cardiaca &amp; HRV4Training: panoramica su fisiologia, tecnologia, consigli pratici per l&#39;utilizzo degli strumenti disponibili, e analisi di dati fisiologici per professionisti interessati all&#39;utilizzo di queste metriche in campo sportivo e non solo.
Analisi della variabilità cardiaca (HRV) from Marco Altini
]]> 5323 4 https://cdn.slidesharecdn.com/ss_thumbnails/201810du-181012183841-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living /slideshow/estimating-running-performance-combining-noninvasive-physiological-measurements-and-training-patterns-in-freeliving/106664061 frat163altinimarco-180720005921
ºÝºÝߣs of my presentation at EMBC 2018: more information on this research can be found here: https://www.researchgate.net/project/HRV4Training-using-mobile-technology-and-data-integration-to-study-physiology-in-large-populations?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3 ]]>
ºÝºÝߣs of my presentation at EMBC 2018: more information on this research can be found here: https://www.researchgate.net/project/HRV4Training-using-mobile-technology-and-data-integration-to-study-physiology-in-large-populations?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3 ]]> Fri, 20 Jul 2018 00:59:21 GMT /slideshow/estimating-running-performance-combining-noninvasive-physiological-measurements-and-training-patterns-in-freeliving/106664061 marcoalt@slideshare.net(marcoalt) Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living marcoalt ºÝºÝߣs of my presentation at EMBC 2018: more information on this research can be found here: https://www.researchgate.net/project/HRV4Training-using-mobile-technology-and-data-integration-to-study-physiology-in-large-populations?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/frat163altinimarco-180720005921-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs of my presentation at EMBC 2018: more information on this research can be found here: https://www.researchgate.net/project/HRV4Training-using-mobile-technology-and-data-integration-to-study-physiology-in-large-populations?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3
Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living from Marco Altini
]]> 314 6 https://cdn.slidesharecdn.com/ss_thumbnails/frat163altinimarco-180720005921-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Towards Non-invasive Labour Detection: A Free- Living Evaluation /slideshow/towards-noninvasive-labour-detection-a-free-living-evaluation/106663922 frat162altinimarco-180720005717
ºÝºÝߣs of my presentation at EMBC 2018, more info on this research is available here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3 ]]>
ºÝºÝߣs of my presentation at EMBC 2018, more info on this research is available here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3 ]]> Fri, 20 Jul 2018 00:57:17 GMT /slideshow/towards-noninvasive-labour-detection-a-free-living-evaluation/106663922 marcoalt@slideshare.net(marcoalt) Towards Non-invasive Labour Detection: A Free- Living Evaluation marcoalt ºÝºÝߣs of my presentation at EMBC 2018, more info on this research is available here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/frat162altinimarco-180720005717-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs of my presentation at EMBC 2018, more info on this research is available here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale?_sg=pbraocCDNc2lJd9v5GESvRhkmffW99OTeeNMkalglCirK5r-ZECp2XRy_5Otk-_B-_dlCalxvKUVtex9MkAHUPFKhHT56GfrO6h3
Towards Non-invasive Labour Detection: A Free- Living Evaluation from Marco Altini
]]> 278 5 https://cdn.slidesharecdn.com/ss_thumbnails/frat162altinimarco-180720005717-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Talk at the International Conference on Biomedical and Health Informatics (BHI 2017) /slideshow/talk-at-the-international-conference-on-biomedical-and-health-informatics-bhi-2017-72163828/72163828 altinivo2max-170215050137
Full text here: https://www.researchgate.net/publication/312121559_Relation_Between_Estimated_Cardiorespiratory_Fitness_and_Running_Performance_in_Free-Living_an_Analysis_of_HRV4Training_Data ]]>
Full text here: https://www.researchgate.net/publication/312121559_Relation_Between_Estimated_Cardiorespiratory_Fitness_and_Running_Performance_in_Free-Living_an_Analysis_of_HRV4Training_Data ]]> Wed, 15 Feb 2017 05:01:37 GMT /slideshow/talk-at-the-international-conference-on-biomedical-and-health-informatics-bhi-2017-72163828/72163828 marcoalt@slideshare.net(marcoalt) Talk at the International Conference on Biomedical and Health Informatics (BHI 2017) marcoalt Full text here: https://www.researchgate.net/publication/312121559_Relation_Between_Estimated_Cardiorespiratory_Fitness_and_Running_Performance_in_Free-Living_an_Analysis_of_HRV4Training_Data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/altinivo2max-170215050137-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Full text here: https://www.researchgate.net/publication/312121559_Relation_Between_Estimated_Cardiorespiratory_Fitness_and_Running_Performance_in_Free-Living_an_Analysis_of_HRV4Training_Data
Talk at the International Conference on Biomedical and Health Informatics (BHI 2017) from Marco Altini
]]> 752 5 https://cdn.slidesharecdn.com/ss_thumbnails/altinivo2max-170215050137-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Talk at the International Conference on Biomedical and Health Informatics (BHI 2017) /slideshow/talk-at-the-international-conference-on-biomedical-and-health-informatics-bhi-2017/72119587 bloomlifepresentation655u-170214053645
More details on our work here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale ]]>
More details on our work here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale ]]> Tue, 14 Feb 2017 05:36:45 GMT /slideshow/talk-at-the-international-conference-on-biomedical-and-health-informatics-bhi-2017/72119587 marcoalt@slideshare.net(marcoalt) Talk at the International Conference on Biomedical and Health Informatics (BHI 2017) marcoalt More details on our work here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bloomlifepresentation655u-170214053645-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> More details on our work here: https://www.researchgate.net/project/Bloomlife-improving-prenatal-health-through-longitudinal-physiological-monitoring-at-large-scale
Talk at the International Conference on Biomedical and Health Informatics (BHI 2017) from Marco Altini
]]> 552 4 https://cdn.slidesharecdn.com/ss_thumbnails/bloomlifepresentation655u-170214053645-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 PhD defense slides /marcoalt/phd-defense-slides-56196659 defense-151216095727
ºÝºÝߣs for my PhD defense. Title: "Personalization of energy expenditure and cardiorespiratory fitness estimation using wearable sensors in supervised and unsupervised free-living conditions" - Full text: http://www.marcoaltini.com/uploads/1/3/2/3/13234002/20150919_thesis.pdf ]]>
ºÝºÝߣs for my PhD defense. Title: "Personalization of energy expenditure and cardiorespiratory fitness estimation using wearable sensors in supervised and unsupervised free-living conditions" - Full text: http://www.marcoaltini.com/uploads/1/3/2/3/13234002/20150919_thesis.pdf ]]> Wed, 16 Dec 2015 09:57:27 GMT /marcoalt/phd-defense-slides-56196659 marcoalt@slideshare.net(marcoalt) PhD defense slides marcoalt ºÝºÝߣs for my PhD defense. Title: "Personalization of energy expenditure and cardiorespiratory fitness estimation using wearable sensors in supervised and unsupervised free-living conditions" - Full text: http://www.marcoaltini.com/uploads/1/3/2/3/13234002/20150919_thesis.pdf <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/defense-151216095727-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs for my PhD defense. Title: &quot;Personalization of energy expenditure and cardiorespiratory fitness estimation using wearable sensors in supervised and unsupervised free-living conditions&quot; - Full text: http://www.marcoaltini.com/uploads/1/3/2/3/13234002/20150919_thesis.pdf
PhD defense slides from Marco Altini
]]> 1139 6 https://cdn.slidesharecdn.com/ss_thumbnails/defense-151216095727-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Heart Rate Variability Logger - Quick Start Guide /slideshow/heart-rate-variability-logger-quick-start-guide/29155892 hrv-20logger-131212140842-phpapp02
Quick start guide of the Heart Rate Variability Logger app, an app that lets you record, plot and export time and frequency domain Heart Rate Variability Features (includes experience sampling, rr-interval correction, comparison between recordings, activity monitoring & step counting, location tracking). iPhone app: https://itunes.apple.com/us/app/heart-rate-variability-logger/id683984776?ls=1&mt=8 android app: https://play.google.com/store/apps/details?id=ma.hrvlogger&hl=en More information and implementation details can be found here: http://www.marcoaltini.com/2/post/2013/12/heart-rate-variability-logger-app-details.html]]>
Quick start guide of the Heart Rate Variability Logger app, an app that lets you record, plot and export time and frequency domain Heart Rate Variability Features (includes experience sampling, rr-interval correction, comparison between recordings, activity monitoring & step counting, location tracking). iPhone app: https://itunes.apple.com/us/app/heart-rate-variability-logger/id683984776?ls=1&mt=8 android app: https://play.google.com/store/apps/details?id=ma.hrvlogger&hl=en More information and implementation details can be found here: http://www.marcoaltini.com/2/post/2013/12/heart-rate-variability-logger-app-details.html]]> Thu, 12 Dec 2013 14:08:42 GMT /slideshow/heart-rate-variability-logger-quick-start-guide/29155892 marcoalt@slideshare.net(marcoalt) Heart Rate Variability Logger - Quick Start Guide marcoalt Quick start guide of the Heart Rate Variability Logger app, an app that lets you record, plot and export time and frequency domain Heart Rate Variability Features (includes experience sampling, rr-interval correction, comparison between recordings, activity monitoring & step counting, location tracking). iPhone app: https://itunes.apple.com/us/app/heart-rate-variability-logger/id683984776?ls=1&mt=8 android app: https://play.google.com/store/apps/details?id=ma.hrvlogger&hl=en More information and implementation details can be found here: http://www.marcoaltini.com/2/post/2013/12/heart-rate-variability-logger-app-details.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hrv-20logger-131212140842-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Quick start guide of the Heart Rate Variability Logger app, an app that lets you record, plot and export time and frequency domain Heart Rate Variability Features (includes experience sampling, rr-interval correction, comparison between recordings, activity monitoring &amp; step counting, location tracking). iPhone app: https://itunes.apple.com/us/app/heart-rate-variability-logger/id683984776?ls=1&amp;mt=8 android app: https://play.google.com/store/apps/details?id=ma.hrvlogger&amp;hl=en More information and implementation details can be found here: http://www.marcoaltini.com/2/post/2013/12/heart-rate-variability-logger-app-details.html
Heart Rate Variability Logger - Quick Start Guide from Marco Altini
]]> 36242 16 https://cdn.slidesharecdn.com/ss_thumbnails/hrv-20logger-131212140842-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Demonstration paper - Personalized Physical Activity Monitoring on the Move /marcoalt/altini-demo altinidemo-131105140006-phpapp01
This work is an implementation of the methodology introduced in "M. Altini, J. Penders, O. Amft. "Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate". In: Pervasive Health 2013.". Papers available at this link: www.marcoaltini.com/research--publications.html]]>
This work is an implementation of the methodology introduced in "M. Altini, J. Penders, O. Amft. "Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate". In: Pervasive Health 2013.". Papers available at this link: www.marcoaltini.com/research--publications.html]]> Tue, 05 Nov 2013 14:00:05 GMT /marcoalt/altini-demo marcoalt@slideshare.net(marcoalt) Demonstration paper - Personalized Physical Activity Monitoring on the Move marcoalt This work is an implementation of the methodology introduced in "M. Altini, J. Penders, O. Amft. "Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate". In: Pervasive Health 2013.". Papers available at this link: www.marcoaltini.com/research--publications.html <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/altinidemo-131105140006-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This work is an implementation of the methodology introduced in &quot;M. Altini, J. Penders, O. Amft. &quot;Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate&quot;. In: Pervasive Health 2013.&quot;. Papers available at this link: www.marcoaltini.com/research--publications.html
Demonstration paper - Personalized Physical Activity Monitoring on the Move from Marco Altini
]]> 1219 5 https://cdn.slidesharecdn.com/ss_thumbnails/altinidemo-131105140006-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Body Weight-Normalized Energy Expenditure Estimation Using Combined Activity and Allometric Scaling Clustering /slideshow/body-weightnormalized-energy-expenditure-estimation-using-combined-activity-clusteringallometric-scaling/23907250 online-130704081028-phpapp02
Presentation for EMBC 2013 — Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person’s body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithm- based clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons be- tween individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate.]]>
Presentation for EMBC 2013 — Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person’s body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithm- based clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons be- tween individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate.]]> Thu, 04 Jul 2013 08:10:28 GMT /slideshow/body-weightnormalized-energy-expenditure-estimation-using-combined-activity-clusteringallometric-scaling/23907250 marcoalt@slideshare.net(marcoalt) Body Weight-Normalized Energy Expenditure Estimation Using Combined Activity and Allometric Scaling Clustering marcoalt Presentation for EMBC 2013 — Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person’s body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithm- based clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons be- tween individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/online-130704081028-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation for EMBC 2013 — Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person’s body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithm- based clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons be- tween individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate.
Body Weight-Normalized Energy Expenditure Estimation Using Combined Activity and Allometric Scaling Clustering from Marco Altini
]]> 1812 6 https://cdn.slidesharecdn.com/ss_thumbnails/online-130704081028-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate /slideshow/personalizing-energy-expenditure-estimation-using-a-cardiorespiratory-fitness-predicate/20494294 slideshare-130503130822-phpapp02
Presentation for Pervasive Health 2013. Paper Abstract: Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily physical activity (PA) patterns affect health, especially in today’s sedentary society. Wearable accelerometers (ACC) and heart rate (HR) sensors have been widely used to monitor physical activity and estimate EE. However, current EE estimation algorithms have not taken into account a person’s cardiorespiratory fitness (CRF), even though CRF is the main cause of inter-individual variation in HR during exercise. In this paper we propose a new algorithm, which is able to significantly reduce EE estimate error and inter-individual variability, by automatically modeling CRF, without requiring users to perform specific fitness tests. Results show a decrease in Root Mean Square Error (RMSE) between 28 and 33% for walking, running and biking activities, compared to state of the art activity-specific EE algorithms combining ACC and HR.]]>
Presentation for Pervasive Health 2013. Paper Abstract: Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily physical activity (PA) patterns affect health, especially in today’s sedentary society. Wearable accelerometers (ACC) and heart rate (HR) sensors have been widely used to monitor physical activity and estimate EE. However, current EE estimation algorithms have not taken into account a person’s cardiorespiratory fitness (CRF), even though CRF is the main cause of inter-individual variation in HR during exercise. In this paper we propose a new algorithm, which is able to significantly reduce EE estimate error and inter-individual variability, by automatically modeling CRF, without requiring users to perform specific fitness tests. Results show a decrease in Root Mean Square Error (RMSE) between 28 and 33% for walking, running and biking activities, compared to state of the art activity-specific EE algorithms combining ACC and HR.]]> Fri, 03 May 2013 13:08:22 GMT /slideshow/personalizing-energy-expenditure-estimation-using-a-cardiorespiratory-fitness-predicate/20494294 marcoalt@slideshare.net(marcoalt) Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate marcoalt Presentation for Pervasive Health 2013. Paper Abstract: Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily physical activity (PA) patterns affect health, especially in today’s sedentary society. Wearable accelerometers (ACC) and heart rate (HR) sensors have been widely used to monitor physical activity and estimate EE. However, current EE estimation algorithms have not taken into account a person’s cardiorespiratory fitness (CRF), even though CRF is the main cause of inter-individual variation in HR during exercise. In this paper we propose a new algorithm, which is able to significantly reduce EE estimate error and inter-individual variability, by automatically modeling CRF, without requiring users to perform specific fitness tests. Results show a decrease in Root Mean Square Error (RMSE) between 28 and 33% for walking, running and biking activities, compared to state of the art activity-specific EE algorithms combining ACC and HR. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slideshare-130503130822-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation for Pervasive Health 2013. Paper Abstract: Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily physical activity (PA) patterns affect health, especially in today’s sedentary society. Wearable accelerometers (ACC) and heart rate (HR) sensors have been widely used to monitor physical activity and estimate EE. However, current EE estimation algorithms have not taken into account a person’s cardiorespiratory fitness (CRF), even though CRF is the main cause of inter-individual variation in HR during exercise. In this paper we propose a new algorithm, which is able to significantly reduce EE estimate error and inter-individual variability, by automatically modeling CRF, without requiring users to perform specific fitness tests. Results show a decrease in Root Mean Square Error (RMSE) between 28 and 33% for walking, running and biking activities, compared to state of the art activity-specific EE algorithms combining ACC and HR.
Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate from Marco Altini
]]> 1658 4 https://cdn.slidesharecdn.com/ss_thumbnails/slideshare-130503130822-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-marcoalt-48x48.jpg?cb=1627107831 Founder of HRV4Training.com, Data Science Advisor at Oura, Guest Lecturer at VU Amsterdam. PhD in Machine Learning, 2x MSc: Sport Science, Computer Science Engineering. Runner www.marcoaltini.com/ https://cdn.slidesharecdn.com/ss_thumbnails/11-210312121308-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/user-generated-data-a-paradigm-shift-for-research-and-data-products/244290180 User generated data: a... https://cdn.slidesharecdn.com/ss_thumbnails/2021hrv4training-210118084028-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/heart-rate-variability-hrv-analysis-and-hrv4training/241490271 Heart Rate Variability... https://cdn.slidesharecdn.com/ss_thumbnails/hrv4traininghowtoplayers-200912092226-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/players-guide-hrv4training/238463094 Players Guide | HRV4Tr...