Personalized physical activity monitoring on the move

Altini, Marco; Penders, Julien; Vullers, Ruud; Amft, Oliver

doi:10.1145/2534088.2534092

Cited by 2 publications

(4 citation statements)

References 3 publications

(3 reference statements)

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“…The proposed system learns automatically from the user over time, collecting accelerometer, HR and GPS data while performing activities of daily living unsupervisedly. Recent developments in wearable and mobile technology provided sensors and phones able to collect and process data continuously and unobtrusively [26]. Our methodology, could be applied to such systems to determine the HR normalization parameter, a coefficient representative of the fitness level of an individual.…”

Section: Discussionmentioning

confidence: 99%

Personalization of Energy Expenditure Estimation in Free Living Using Topic Models

Altini¹,

Casale

Penders

et al. 2015

IEEE J. Biomed. Health Inform.

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We introduce an approach to personalize energy expenditure (EE) estimates in free living. First, we use topic models to discover activity composites from recognized activity primitives and stay regions in daily living data. Subsequently, we determine activity composites that are relevant to contextualize heart rate (HR). Activity composites were ranked and analyzed to optimize the correlation to HR normalization parameters. Finally, individual-specific HR normalization parameters were used to normalize HR. Normalized HR was then included in activity-specific regression models to estimate EE. Our HR normalization minimizes the effect of individual fitness differences from entering in EE regression models. By estimating HR normalization parameters in free living, our approach avoids dedicated individual calibration or laboratory tests. In a combined free-living and laboratory study dataset, including 34 healthy volunteers, we show that HR normalization in 14-day free-living data improves accuracy compared to no normalization and normalization based on activity primitives only ( 29.4% and 19.8 % error reduction against lab reference). Based on acceleration and HR, both recorded from a necklace, and GPS acquired from a smartphone, EE estimation error was reduced by 10.7 % in a leave-one-participant-out analysis.

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Section: Discussionmentioning

confidence: 99%

Personalization of Energy Expenditure Estimation in Free Living Using Topic Models

Altini¹,

Casale

Penders

et al. 2015

IEEE J. Biomed. Health Inform.

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show abstract

“…Activities were derived using pattern recognition methods, in particular a Support Vector Machine (SVM). SVMs are classifiers that showed good results in classifying activities in our previous research (1,2,3,4). The principle behind using pattern recognition methods and accelerometer data for activity classification is that different activity clusters (e.g., lying down, walking) result in different accelerometer patterns as collected by on-body sensors.…”

Section: Data Processingmentioning

confidence: 98%

“…Such measures are becoming more and more widespread due to mainstream availability of wearable technology, including combined accelerometer and HR monitors. Similarly, the processing capabilities of modern mobile phones are sufficient for practical deployment of machine learning methods (4).…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…Thus, CRF is a marker of training status that can be considered one of the most important determinants of health and wellbeing. While recent developments in wearable sensor technologies improved the accuracy of physical activity monitoring devices in daily life, almost all solutions focus on behavioral aspects such as steps, activity type, and energy expenditure (EE) (4,6). Steps or EE are relevant markers of an individual's health; however, they mainly reflect the individual's behavior instead of the individual's health status.…”

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confidence: 99%

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Cardiorespiratory fitness estimation using wearable sensors: Laboratory and free-living analysis of context-specific submaximal heart rates

Altini

Casale

Penders

et al. 2016

Journal of Applied Physiology

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In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free living, and using context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (V̇o2 max). Participants wore a combined accelerometer and HR monitor during a laboratory-based simulation of activities of daily living and for 2 wk in free living. Anthropometrics, HR while lying down, and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R(2)) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73-0.78 when including fat-free mass). Next, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e., lying down and walking) in free living. Context-specific HR in free living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R(2) for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e., HR while walking at 5.5 km/h). R(2) varied between 0.73 and 0.80 when including fat-free mass among the predictors. Root mean-square error was reduced from 354.7 to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking.

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Personalized physical activity monitoring on the move

Cited by 2 publications

References 3 publications

Personalization of Energy Expenditure Estimation in Free Living Using Topic Models

Personalization of Energy Expenditure Estimation in Free Living Using Topic Models

Cardiorespiratory fitness estimation using wearable sensors: Laboratory and free-living analysis of context-specific submaximal heart rates

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