Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments

Spathis, Dimitris; Perez-Pozuelo, Ignacio; Gonzales, Tomas I.; Wu, Yu; Brage, Sören; Wareham, Nicholas J.; Mascolo, Cecilia

doi:10.1038/s41746-022-00719-1

Cited by 7 publications

(14 citation statements)

References 30 publications

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“…Data generated from a combination of mobile devices and WS that had a common platform (Apple Health; Achievement) extracted similar variables [ 87 , 88 ]. Particularly amongst large-scale research with many participants, these pre-calculated features were utilised for analysis rather than raw data [ 88 , 89 ]. Some research reported utilising pedometers as their WS to collect patient data; these pedometers all included accelerometers rather than a traditional step counter allowing them to represent the intensity of movement [ 90 ].…”

Section: Hardware/sensing Technologiesmentioning

confidence: 99%

“…One large cohort study used the Actiheart wearable ECG, which places two leads on the sternum from which three papers analysed the HR data [ 66 , 89 , 114 ]. Other research utilised an ECG ‘necklace’, which involved placing electrodes in the II lead configuration on the chest (see Figure 6 c) whilst a further project included an ECG sensor that was integrated into a smart shirt (Hexoskin), see Figure 6 b [ 115 , 116 ].…”

Section: Hardware/sensing Technologiesmentioning

confidence: 99%

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Wearable Sensors as a Preoperative Assessment Tool: A Review

Syversen,

Dosis,

Jayne

et al. 2024

Sensors

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Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

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Section: Hardware/sensing Technologiesmentioning

confidence: 99%

Section: Hardware/sensing Technologiesmentioning

confidence: 99%

Wearable Sensors as a Preoperative Assessment Tool: A Review

Syversen,

Dosis,

Jayne

et al. 2024

Sensors

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“…In healthy individuals, HR responses to activity are defined by an increase in HR that is concurrent to the increasing intensity of the activity [134]. More recently, the Apple Watch has introduced cardio-respiratory fitness (CRF) features [135] while large population-scale studies showed that these HR responses can predict VO 2 max [136].…”

Section: Ai For Mobile Health—case Studiesmentioning

confidence: 99%

“…Wearable devices, such as activity trackers and smartwatches, increasingly provide opportunities for non-intrusive objective monitoring of biological signals such as HR and movement during free-living, potentially enabling more precise prediction of VO 2 max without the need to conduct a specific exercise test [140]. A recent large-scale analysis used data from 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 max testing, to 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, 95normal% CI: 0.80–0.83) [136]. It also detects fitness change over time (e.g.…”

Section: Ai For Mobile Health—case Studiesmentioning

confidence: 99%

“…Most mobile health apps are validated only using a limited number of participants. For example, Apple’s CRF feature is publicly validated on 221 participants [135] while non-commercial methods use population-scale data from more than 11 000 participants [136]. Considering the large user bases of such products, we need to find better methods to leverage free-living with small-scale laboratory-collected data.…”

Section: Implications and Limitationsmentioning

confidence: 99%

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Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Dang,

Spathis,

Ghosh

et al. 2023

R. Soc. open sci.

Self Cite

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Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.

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Highly Sensitive Biomimetic Crack Pressure Sensor with Selective Frequency Response

Li,

Zhang,

et al. 2024

ACS Sens.

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High-sensitivity sensors in practical applications face the issue of environmental noise interference, requiring additional noise reduction circuits or filtering algorithms to improve the signal-to-noise ratio (SNR). To address this issue, this study proposes a biomimetic crack pressure sensor with selective frequency response based on hydrogel dampers. The core of this research is to construct a biomimetic crack pressure sensor with selective frequency response using the high-pass filtering characteristics of gelatin–chitosan hydrogels. This design, inspired by the slit sensilla and stratum corneum structure of spider legs, delves into the material properties and principles of hydrogel dampers, exploring their application in biomimetic crack pressure sensors, including parameter selection, structural design, and performance optimization. By delving into the nuanced characteristics and working principles of hydrogel dampers, their integration in biomimetic crack pressure sensors is examined, focusing on aspects like parameter selection, structural engineering, and performance enhancement to selectively sieve out low-frequency noise and transmit target vibrational signals. Experimental results demonstrate that this innovative sensor, while suppressing low-frequency vibration signals, can selectively detect high-frequency signals with high sensitivity. At different vibration frequencies, the relative change in resistance exceeds 200%, and the sensor sensitivity is 7 × 104 kPa–1. Furthermore, this sensor was applied to human voice detection, and the corresponding results verified its frequency-selective performance evidently. This study not only proposes a new design for pressure sensors but also offers fresh insights into the application of biomimetic crack pressure sensors in intricate environments.

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Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments

Cited by 7 publications

References 30 publications

Wearable Sensors as a Preoperative Assessment Tool: A Review

Wearable Sensors as a Preoperative Assessment Tool: A Review

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Highly Sensitive Biomimetic Crack Pressure Sensor with Selective Frequency Response

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