Marco Altini

HRV4Training

We introduce an approach to personalize energy expenditure (EE) estimates in free living. First we use Topic Models (TM) 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 wer...

Accurate estimation of Energy Expenditure (EE) and cardiorespiratory fitness (CRF) is a key element in determining the causal relation between aspects of human behavior related to physical activity and health. In this paper we estimate CRF without requiring laboratory protocols and personalize energy expenditure (EE) estimation models that rely on...

We describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmog-raphy (PPG) and derives HR and HRV features. HRV4Trainin...

Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart rate sensor in the measurement of heart rate variability (HRV). Methods: 29 healthy subjects were measured at rest during 5 min of guided breathing (GB) and normal breathing (NB) using Smartphone PPG, heart rate chest strap and electrocardiography (ECG). The ro...

In modern soccer, fitness and fatigue monitoring tools tend to be focused on noninvasive, time‐efficient and player‐friendly measures. Heart rate variability (HRV) has been suggested as an effective method for monitoring training response and readiness to perform. However, there is still a lack of consensus on HRV monitoring when it comes to soccer...

Heart rate variability (HRV) is a proxy of physiological stress, capturing autonomic nervous system responses to training and other stressors. As such, a vast body of literature has investigated the impact of acute and chronic stressors on HRV. Technological advancements such as mobile apps and wearables able to capture HRV data in more practical s...

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-gene...

The aim of this study was to investigate the relationship between heart rate and heart rate variability (HRV) with respect to individual characteristics and acute stressors. In particular, the relationship between heart rate, HRV, age, sex, body mass index (BMI), and physical activity level was analyzed cross-sectionally in a large sample of 28,175...

Purpose: to analyze the training structure following a heart rate variability (HRV) -guided training or traditional training protocol, determining their effects on the cardiovascular performance of professional endurance runners, and describing the vagal modulation interaction. Methods: This was an 8-week cluster-randomized controlled trial. Twelve...

The commercial explosion of wearable sensing devices in the early 2010s forever changed the landscape of wearable computing. In a few short years, wrist-mounted devices such as wristbands and smart watches dominated the market.1 In 2017, this department featured an article titled “What will we wear after smartphones?” highlighting potential pathway...

Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, a...

A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing...

Purpose: First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes...

Heart rate variability-training adaptation-triathlon-altitude training-resting heart rate Headline T raining camps at altitude aim at improving performance by spending time in a hypoxic environment, which typically increases red blood cells and improves oxygen carrying capacity. Despite these clear pathways, athletes often respond differently and n...

1 Summary Objectives: To determine if changes in resting heart rate (HR) and heart rate variability (HRV) during the beginning (first 10 days) of a three-week training camp at altitude are representative of the athlete's training adaptation at the end of the training camp. Methods: Four elite triathletes (2 male and 2 female), spent 23 days in Nami...

Title* Early labour detection in laboratory and free-living conditions using combined electrohysterography and heart rate data Objective * Detection and management of complications such as preterm birth, could be improved by early labour detection. In our previous work, we showed that specific patterns in physiological data such as electrohysterogr...

Detection and management of complications such as preterm birth, could be improved by early labour detection. In our previous work, we showed that specific patterns in physiological data such as electrohysterography (EHG) and heart rate (HR) could be used to build predictive statistical models able to detect labour. In this work we highlight how ph...

In this study, we present a hidden Markov model approach to pre-eclampsia (PE) diagnosis using the Viterbi algorithm. We aim at identifying PE in high-risk pregnancies monitored in hospital settings. The proposed model uses daily blood pressure measurements collected using commercially available sensors, starting at 20 weeks of gestational age. An...

Labor is the physiological process during which the fetus is expelled from the uterus and is normally clinically diagnosed. However, the process leading to labor, typically involving an increase in contractions frequency and intensity can be monitored non-invasively using electrohysterography (EHG) and maternal heart rate (HR). Despite recent techn...

In this paper we show early evidence of the feasibility of detecting labour during pregnancy, non-invasively and in free-living. In particular, we present machine learning models aiming at dealing with the challenges of unsupervised, free-living data collection, such as identifying periods of high quality data and detecting physiological changes as...

In this work, we use data acquired longitudinally, in free-living, to provide accurate estimates of running performance. In particular, we used the HRV4Training app and integrated APIs (e.g. Strava and TrainingPeaks) to acquire different sets of parameters, either via user input, morning measurements of resting physiology, or running workouts to es...

Heart rate variability (HRV) is a popular tool for monitoring training adaptation and readiness in athletes, but it also has the potential to indicate early signs of somatic tissue overload prior to the onset of pain or fully developed injury. Therefore, the aim of this study was to investigate the interaction between HRV, workloads, and risk of ov...

In this work, we propose to use anthropometrics and physiological data to estimate cardiorespiratory fitness (CRF) in free-living and analyze the relation between estimated CRF and running performance. In particular, we use the ratio between running speed and heart rate (HR) as predictor for CRF estimation in free-living. The ratio is representativ...

In this paper we propose a method combining electrohysterography (EHG) and heart rate (HR) data to detect labour. Labour detection may be helpful in providing just in time care and avoiding unnecessary antenatal visits. Given specific changes in physiological data such as EHG and HR highlighted from previous literature in correspondence of uterine...

In this paper, we propose a method to improve accuracy of fetal kicks detection during pregnancy using a single wearable device placed on the abdomen. Monitoring fetal wellbeing is key in modern obstetrics as it is routinely used as a proxy to fetal movement. However, accurate, nonin-vasive, long-term monitoring of fetal movement is challenging, es...

Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote,...

Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote,...

Altini M, Casale P, Penders J, ten Velde G, Plasqui G, Amft O. Cardiorespiratory fitness estimation using wearable sensors: Laboratory and free-living analysis of context-specific submaximal heart rates..—In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at...

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....

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 use context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (VO2max). Par...

We investigate the process of transferring the activity recognition models within the nodes of a body sensor network (BSN). In particular, we propose a methodology that supports and makes the transferring possible. Based on a collaborative training strategy, classifier ensembles of randomized trees are used to create activity recognition models tha...

Maternal and infant health is a global healthcare problem affecting developing and developed countries alike. Pregnancy complications increase the risk of maternal and infant death, and are associated with adverse outcomes such as miscarriage, stillbirth, and preterm birth. Lifestyle modifications before and during pregnancy have been shown to redu...

In this paper, we present a method to estimate oxygen uptake (VO2) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of non-steady-state VO2. Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-speci...

Introduction: This article is part of the focus theme of Methods of Information in Medicine on "Pervasive Intelligent Technologies for Health". Background: Energy Expenditure (EE) estimation algorithms using Heart Rate (HR) or a combination of accelerometer and HR data suffer from large error due to inter-person differences in the relation betwe...

In this paper we propose a generic approach to reduce inter-individual variability of different physiological signals (HR, GSR and respiration) by automatically estimating normalization parameters (e.g. baseline and range). The proposed normalization procedure does not require a dedicated personal calibration during system setup. On the other hand,...

In this work we investigate the process of transferring the activity recognition models of the nodes of a Body Sensor Network and we proposed a methodology that supports and makes the transferring possible. The methodology, based on a collaborative training strategy, makes use of classifier ensembles of randomised trees that allow to generate activ...

Several methods to estimate Energy Expenditure (EE) using body-worn sensors exist, however quantifications of the differences in estimation error are missing. In this paper, we compare three prevalent EE estimation methods and five body locations to provide a basis for selecting among methods, sensors number and positioning. We considered (a) count...

Activity recognition for human behavior monitoring is an important research topic in the field of mHealth, especially for aspects of physical activity linked to fitness and disease progress, such as walking and walking speed. Sensors embedded into smartphones recently enabled new opportunities for non invasive activity and walking speed inference....

Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily Physical Activity (PA) patterns affect health. Mobile phones and wearable sensors (e.g. accelerometers (ACC) and heart rate (HR) monitors) have been widely used to monitor PA. In this paper we present a real-time implementation of activity-specific EE estimat...

Physical Activity (PA) is one of the most important determinants of health. Wearable sensors have great potential for accurate assessment of PA (activity type and Energy Expenditure (EE)) in daily life. In this paper we investigate the benefit of multiple physiological signals (Heart Rate (HR), respiration rate, Galvanic Skin Response (GSR), skin h...

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 affe...

Body sensor networks (BSNs) have provided the opportunity to monitor energy expenditure (EE) in daily life and with that information help reduce sedentary behavior and ultimately improve human health. Current approaches for EE estimation using BSNs require tedious annotation of activity types and multiple body sensor nodes during data collection an...

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 hav...

Ambulatory monitoring of the electrocardiogram (ECG) is a highly relevant topic in personal healthcare. A key technical challenge is overcoming artifacts from motion in order to produce ECG signals capable of being used in clinical diagnosis by a cardiologist. An electrode-tissue impedance is a signal of significant interest in reducing the motion...

Accurate estimation of Energy Expenditure (EE) in ambulatory settings is a key element in determining the causal relation between aspects of human behavior related to physical activity and health. We present a new methodology for activity-specific EE algorithms. The proposed methodology models activity clusters using specific parameters that captur...

In this paper, an optimized R peak detection algorithm with a high level of accuracy that can be implemented using very low power consumption is proposed. The accuracy of the algorithm is evaluated against the MIT-BIH arrhythmia database, giving an average sensitivity of 99.22% and positive predictivity of 99.86%, as well as against imec's database...

This paper presents the development of an ECG patch aiming at long term patient monitoring. The use of the recently standardized Bluetooth Low Energy (BLE) technology, together with a customized ultra-low-power ECG System on Chip (ECG SoC). including Digital Signal Processing (DSP) capabilities, enables the design of ultra low power systems able to...

Monitoring patients' physiological signals during their daily activities in the home environment is one of the challenge of the health care. New ultra-low-power wireless technologies could help to achieve this goal. In this paper we present a low-power, multi-modal, wearable sensor platform for the simultaneous recording of activity and physiologic...

This paper describes the development of an indoor vibrotactile navigation system for the visually impaired people. We aimed at realizing a wearable, low-cost, and effective system able to help blind users in unknown indoor environments that they might visit occasionally, such as hospitals, airports, museums, etc. The designed system implements a Bl...

This paper presents a Body Area Network (BAN) gateway to Android mobile phones for mobile health applications. The proposed approach is based on a Secure Digital Input Output (SDIO) interface, which allows for long-term monitoring since the mobile phone hardware can be extended in order to operate with ultra low-power radios. The software architect...

Over the last years, many different methods have been proposed for indoor localization and navigation services based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI). The accuracy achieved with such systems is typically low, mainly due to the variability of RSSI values, unsuitable for classic localization methods (e.g....