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Relation between body weight, HR and CRF for participants with similar body size (weight and height) characteristics. a) Positive relation between V O 2 max and body weight disappears when participants with similar body size characteristics are considered. b) Negative relation between V O 2 max and HR while walking holds on a subset of participants with similar body size, and can potentially be used to discriminate CRF levels.
Source publication
Objective:
In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data.
Methods:
Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests....
Contexts in source publication
Context 1
... levels cannot be discriminated, as shown in Fig. 1. Submaximal tests have been developed to estimate V O 2 max during specific protocols while monitoring HR at predefined workloads [14]. Contextualized HR, e.g. HR while performing a specific activity in laboratory settings, is discriminative of CRF levels between individuals with similar characteristics, due to the inverse relation ...
Context 2
... have been developed to estimate V O 2 max during specific protocols while monitoring HR at predefined workloads [14]. Contextualized HR, e.g. HR while performing a specific activity in laboratory settings, is discriminative of CRF levels between individuals with similar characteristics, due to the inverse relation between HR 110 and CRF [26] (see Fig. 1). Commercial devices, for example some sport watches paired to HR monitors [27,28] (e.g. Garmin or Polar devices), provide CRF estimation using a regression model including HR at a predefined running speed as predictor. However, submaximal tests are still affected by limitations; the test should be re-performed every time CRF needs to ...
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Abstract: Exercise-based cardiac rehabilitation is a highly recommended intervention towards the advancement of the cardiovascular disease (CVD) patients’ health profile; though with low participation rates. Although home-based cardiac rehabilitation (HBCR) with the use of wearable sensors is proposed as a feasible alternative rehabilitation model,...
Citations
... Current algorithms used in smartwatches 23, 24 rely on accelerometer and distance measurements during separately tracked outdoor walking and running sessions. Existing publicly available algorithms 17,18,[20][21][22] utilize linear regression and were validated using laboratory protocols on 48, 46, 32, 41, and 50 participants, respectively. Having a larger cohort of 3894 participants allows for the training of a more expressive model than linear regression. ...
Predicting cardiorespiratory fitness levels can be useful for measuring progress in an exercise program as well as for stratifying cardiovascular risk in asymptomatic adults. This study proposes a model to predict fitness level in terms of maximal oxygen uptake using anthropometric, heart rate, and step count data. The model was trained on a diverse cohort of 3115 healthy subjects (1035 women and 2080 men) aged 42 ± 10.6 years and tested on a cohort of 779 healthy subjects (260 women and 519 men) aged 42 ± 10.18 years. The developed model is capable of making accurate and reliable predictions with the average test set error of 3.946 ml/kg/min. The maximal oxygen uptake labels were obtained using wearable devices (Apple Watch and Garmin) during recorded workout sessions. Additionally, the model was validated on a sample of 10 subjects with maximal oxygen uptake determined directly using a treadmill protocol in a laboratory setting and showed an error of 4.982 ml/kg/min. Unlike most other models, which use accelerometer readings as additional input data, the proposed model relies solely on heart rate and step counts—data readily available on the majority of fitness trackers. The proposed model provides a point estimation and a probabilistic prediction of cardiorespiratory fitness level, thus it can estimate the prediction’s uncertainty and construct confidence intervals.
... The used IMU was MPU-9250 (Figure 6a), being one of the most widely available low-cost commercial sensors. This chip is extensively employed in wearable sensors for health [2,52], fitness [4], and sports [20,56,72], motion-based game controllers [30], and portable gaming [8,57]. This IMU is operated to estimate motion by identifying the presence of acceleration vectors, rotational rates, and local magnetic field direction. ...
Computing research is increasingly addressing underwater environments and examining how computing can support diving and other activities. Unlike on land, where well-established positioning methods are widely available, underwater environments lack a common positioning mechanism, which is a prerequisite for many applications. Dead reckoning, the use of angle and distance estimates to track position changes from a known point of origin, is a promising candidate for underwater positioning as it does not rely on wireless signals (which decay rapidly in underwater environments) and as there is a wide range of literature and algorithms freely available. Yet, currently it is unclear whether the existing techniques can be adopted in underwater environments or whether the differences in medium and environment affect the performance of the dead reckoning techniques. We contribute by evaluating and systematically analyzing the performance and trade-offs associated with dead reckoning techniques in underwater environments. We present AEOLUS, a prototype unit comprising of a low-cost microcontroller and inertial measurement unit, to perform experiments on the ground and in underwater environments to assess how well the performance of different techniques translates from ground-based use cases to underwater environments. We benchmark 15 different algorithms and compare their performance in such environments to identify common patterns and dissimilarities, and identify root causes for these differences. The results show that displacement and turn errors can be estimated to within 5% error but that the best performing methods vary between land and underwater environments. We also show that the performance depends on the shape of the motion patterns with some algorithms performing better for hard turns whereas others perform better for gradual, more continuous turns.
... This information is used in multiple contexts, ranging from fitness tracking, health assessment and elderly care, to human-robot interaction [1][2][3][4]. In a clinical context, HAR can be used to outline the patients activities during hospitalization to improve, or enable, recovery/deterioration monitoring [5]; additionally, HAR allows for the contextualization of electrocardiographic patterns [6,7]. As the study from Brown et al. [8] stated, a low amount of dynamic activities may cause negative consequences in hospitalized patients; therefore, it appears relevant to recognize and quantify their activities to timely assist them. ...
Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.11 against a baseline model f1-score equal to 0.49±0.12.
... Although certain wearable devices show promise, they tend to rely on detailed physical activity intensity measurements, GPS-based speed monitoring, and require users to reach near-maximal HR values, which limits their use to fitter individuals 11 . Some studies attempt to estimate VO 2 max from data collected during free-living conditions, but these are typically from small-scale cohorts and use contextual data from treadmill activity, which restricts their application in population settings 12,13 . ...
Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO 2 m a x ), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates’ ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort ( N = 2675), and a third external cohort using the UK Biobank Validation Study ( N = 181) who underwent maximal VO 2 m a x testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample ( r = 0.82, 95CI 0.80–0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model’s latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests.
... Although certain wearable devices show promise, they tend to rely on detailed physical activity intensity measurements, GPS-based speed monitoring, and require users to reach near-maximal HR values, which limits their use to fitter individuals 9 . Some studies attempt to estimate VO 2 max from data collected during free-living conditions, but these are typically from small-scale cohorts and use contextual data from treadmill activity, which restricts their application in population settings 10 . ...
Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate response to a standard exercise test. However, such testing is costly and burdensome, limiting its utility and scalability. Fitness can also be approximated using resting heart rate and self-reported exercise habits but with lower accuracy. Modern wearables capture dynamic heart rate data which, in combination with machine learning models, could improve fitness prediction. In this work, we analyze movement and heart rate signals from wearable sensors in free-living conditions from 11,059 participants who also underwent a standard exercise test, along with a longitudinal repeat cohort of 2,675 participants. We design algorithms and models that convert raw sensor data into cardio-respiratory fitness estimates, and validate these estimates' ability to capture fitness profiles in a longitudinal cohort over time while subjects engaged in real-world (non-exercise) behaviour. Additionally, we validate our methods with a third external cohort of 181 participants who underwent maximal VO2max testing, which is considered the gold standard measurement because it requires reaching one's maximum heart rate and exhaustion level. Our results show that the developed models yield a high correlation (r = 0.82, 95CI 0.80-0.83), when compared to the ground truth in a holdout sample. These models outperform conventional non-exercise fitness models and traditional bio-markers using measurements of normal daily living without the need for a specific exercise test. Additionally, we show the adaptability and applicability of this approach for detecting fitness change over time in the longitudinal subsample that repeated measurements after 7 years.
... For a realistic data set, required for level 5 and progression to level 7 or 8, the observation period and recording duration were specifically important, as we found in 12 studies. Three studies used an observation period of 24 hours [23,32,64]; one for a week [17], one for 2 weeks [27], and one for 90 days [16]. Overall, 2 studies implied an observation period of months but did not explicitly report it [19,20]. ...
... One considered recordings of at least eight hours [19] and one reported an average recording duration of 11.3 hours [20]. Finally, only one [27] fully used the potential of wearables and reported a (near-) continuous recording duration. ...
... Another study predicted vascular age and 10-year cardiovascular disease risk [34]. The third assigned a cardiorespiratory fitness score [27]. Notably, only the first 2 studies constructed a prognostic model. ...
Background
Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention.
Objective
This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology.
Methods
We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as “wearables,” “machine learning,” and “cardiovascular disease.” Methodologies were categorized and analyzed according to machine learning–based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready).
ResultsAfter the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL
... Recent research studies on VO 2 max prediction have incorporated the use of technology and have gone as far as estimating VO 2 max without the need to perform predefined protocols. Cao et al. used objectively measured physical activity levels to predict VO 2 max, and Altini et al. used wearable sensors to identify context-specific intensity during 'freeliving' and used HR to predict VO 2 max [20,21]. Although such approaches are novel and offer easy means to predict VO 2 max, these methods are still being tested and require specialized equipment, making it difficult for the general population to use. ...
The aim of the study was to develop a simple submaximal walk test protocol and equation using heart rate (HR) response variables to predict maximal oxygen consumption (VO2max). A total of 60 healthy adults were recruited to test the validity of 3 min walk tests (3MWT). VO2max and HR responses during the 3MWTs were measured. Multiple regression analysis was used to develop prediction equations. As a result, HR response variables including resting HR and HR during walking and recovery at two different cadences were significantly correlated with VO2max. The equations developed using multiple regression analyses were able to predict VO2max values (r = 0.75-0.84; r2 = 0.57-0.70; standard error of estimate (SEE) = 4.80-5.25 mL/kg/min). The equation that predicted VO2max the best was at the cadence of 120 steps per minute, which included sex; age; height; weight; body mass index; resting HR; HR at 1 min, 2 min and 3 min; HR recovery at 1 min and 2 min; and other HR variables calculated based on these measured HR variables (r = 0.84; r2 = 0.70; SEE = 4.80 mL/kg/min). In conclusion, the 3MWT developed in this study is a safe and practical submaximal exercise protocol for healthy adults to predict VO2max accurately, even compared to the well-established submaximal exercise protocols, and merits further investigation.
... For heart health, a chest wearable to monitor physical and cardiac activity was developed where it involves ECG, accelerometers, sensors for temperature, a microcontroller, and a Bluetooth [34]. Other research estimated cardio-respiratory fitness via designing ML algorithms to explore data from heart rate, accelerometers, and Global Positioning System (GPS) [35]. For cognitive disorders, an intelligent ring with EDA, heart rate, accelerometers for movement, and sensors for dermal temperature was designed for monitoring the sympathetic nervous systems [36]. ...
Conventional health systems are aiming to diagnose first and treat diseases afterward. The growing precision health seeks early diagnosing and customized therapy for a health condition, by ongoing tracking of individual health and well‐being. Portable and implantable devices can dynamically track and evaluate health, supplying a broad view of human living and health. Growing techniques are promising in powering the effect of precision health. For instance, the emerging monitoring techniques would continuously gather data and engage the customer. Novel techniques are steadily developing, hence the need for improved insights into the scenery of the current and future techniques which could gather data for precision health. This chapter outlines the available and growing tracking and sensing techniques for precision health, focusing especially on the highly needed techniques like portable and mobile devices, implantable sensors, and wearables.
... Scaling fitness prediction. Despite some promising studies which attempt to infer VO 2 max from data collected during free-living conditions, these mostly stem from small-scale cohorts with less than 50 participants and use contextual data from treadmill activity, which again limits their application in real-world contexts (Altini et al., 2016). In this thesis, we employ data from the Fenland Study, the largest study of its kind, following more than 10,000 participants for a week and almost a decade later to assess the change of fitness. ...
... Due to the inability of algorithms back then to work directly on the raw pixels of an image (raw sensors in our case), researchers published inventive methods that were called feature descriptors. Seminal papers of that time like the Scale Invariant Feature Transform (SIFT) (Lowe, 2004), or the Histogram of Oriented Gradients (HOG) (Dalal and Triggs, 2005), are handcrafted algorithms that extract interest points from an image based on geometry 8 . The turning point was in 2012 when the Imagenet study (Krizhevsky et al., 2012) showed that better results are possible with deep learning that does not need all these extra hand-crafted features. ...
... Although certain commercial devices have shown stronger results than others, many tend to rely on detailed activity intensity measurements paired with speed monitoring through GPS and require users to reach heart rates that are close to their maximum capabilities, limiting the application to self-selecting, fitter individuals (Cooper and Shafer, 2019; Lucio et al., 2018). Despite some promising studies which attempt to infer VO 2 max from data collected during free-living conditions, these mostly stem from small-scale cohorts with less than 50 participants and use contextual data from treadmill activity, which again limits their application in real-world contexts (Altini et al., 2016). In this work, we use data from the largest study of it's kind, by over two orders of magnitude, and use purely free-living data to predict VO 2 max, with no requirement for context-awareness. ...
The widespread adoption of smartphones and wearables has led to the accumulation of rich datasets, which could aid the understanding of behavior and health in unprecedented detail. At the same time, machine learning and specifically deep learning have reached impressive performance in a variety of prediction tasks, but their use on time-series data appears challenging. Existing models struggle to learn from this unique type of data due to noise, sparsity, long-tailed distributions of behaviors, lack of labels, and multimodality. This dissertation addresses these challenges by developing new models that leverage multi-task learning for accurate forecasting, multimodal fusion for improved population subtyping, and self-supervision for learning generalized representations. We apply our proposed methods to challenging real-world tasks of predicting mental health and cardio-respiratory fitness through sensor data. First, we study the relationship of passive data as collected from smartphones (movement and background audio) to momentary mood levels. Our new training pipeline, which combines different sensor data into a low-dimensional embedding and clusters longitudinal user trajectories as outcome, outperforms traditional approaches based solely on psychology questionnaires. Second, motivated by mood instability as a predictor of poor mental health, we propose encoder-decoder models for time-series forecasting which exploit the bi-modality of mood with multi-task learning. Next, motivated by the success of general-purpose models in vision and language tasks, we propose a self-supervised neural network ready-to-use as a feature extractor for wearable data. To this end, we set the heart rate responses as the supervisory signal for activity data, leveraging their underlying physiological relationship and show that the resulting task-agnostic embeddings can generalize in predicting structurally different downstream outcomes through transfer learning (e.g. BMI, age, energy expenditure), outperforming unsupervised autoencoders and biomarkers. Finally, acknowledging fitness as a strong predictor of overall health, which, however, can only be measured with expensive instruments (e.g., a VO2max test), we develop models that enable accurate prediction of fine-grained fitness levels with wearables in the present, and more importantly, its direction and magnitude almost a decade later. All proposed methods are evaluated on large longitudinal datasets with tens of thousands of participants in the wild. The models developed and the insights drawn in this dissertation provide evidence for a better understanding of high-dimensional behavioral and physiological data with implications for large-scale health and lifestyle monitoring.
... For a realistic data set, required for level 5 and progression to level 7 or 8, the observation period and recording duration were specifically important, as we found in 12 studies. Three studies used an observation period of 24 hours [23,32,64]; one for a week [17], one for 2 weeks [27], and one for 90 days [16]. Overall, 2 studies implied an observation period of months but did not explicitly report it [19,20]. ...
... One considered recordings of at least eight hours [19] and one reported an average recording duration of 11.3 hours [20]. Finally, only one [27] fully used the potential of wearables and reported a (near-) continuous recording duration. ...
... Another study predicted vascular age and 10-year cardiovascular disease risk [34]. The third assigned a cardiorespiratory fitness score [27]. Notably, only the first 2 studies constructed a prognostic model. ...
BACKGROUND
Wearable technology has the potential to improve cardiovascular health monitoring using machine learning. It enables remote health monitoring and allows for diagnosis and prevention. In addition to detection of cardiovascular disease, it can exclude a diagnosis in symptomatic patients, preventing unnecessary hospital visits. Furthermore, early warning systems can aid the cardiologist in timely treatment and prevention.
OBJECTIVE
We systematically assessed literature on detecting and predicting outcomes of cardiovascular disease with data obtained from wearables to gain insights in the current challenges and limitations.
METHODS
We searched PubMed, Scopus and IEEE Xplore on September 26, 2020 with no restrictions on the publication date using keywords: wearables, machine learning and cardiovascular disease. Methodologies were categorized and analyzed according to machine learning based technology readiness levels (TRLs) that score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready).
RESULTS
After removal of duplicates, applying exclusion criteria, and full-text screening, 55 eligible studies remained for analysis, covering a variety of cardiovascular diseases. None of the studies were integrated into a health care system (TRL < 6), prospectively phase 2 and 3 trials were absent (TRL < 7 and 8) and group cross-validation was rarely used, limiting to demonstrate their effectiveness. Furthermore, there seems to be no agreement on the sample size needed to train these models, the size of the observation window used to make predictions, how long subjects should be observed and the type of machine learning model that is suitable for predicting cardiovascular outcomes.
CONCLUSIONS
Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic and/or prognostic cardiovascular clinical tool is hampered by the lack of using a realistic dataset and a proper systematic and prospective evaluation.
