Noninvasive Continuous Blood Pressure Estimation From Pulse Transit Time: A Review of the Calibration Models.

Noninvasive continuous blood pressure estimation is a promising alternative to minimally invasive blood pressure measurement using cuff and invasive catheter measurement, because it opens the way to both long-term and continuous blood pressure monitoring in ecological situation. The most current estimation algorithm is based on pulse transit time measurement where at least two measured signals need to be acquired. From the pulse transit time values, it is possible to estimate the continuous blood pressure for each cardiac cycle. This measurement highly depends on arterial properties which are not easily accessible with common measurement techniques; but these properties are needed as input for the estimation algorithm. With every change of input arterial properties, the error in the blood pressure estimation rises, thus a periodic calibration procedure is needed for error minimization. Recent research is focused on simplified constant arterial properties which are not constant over time and uses only linear model based on initial measurement. The elaboration of continuous calibration procedures, independent of recalibration measurement, is the key to improving the accuracy and robustness of noninvasive continuous blood pressure estimation. However, most models in literature are based on linear approximation and we discuss here the need for more complete calibration models.

Identification of new behavioral parameters to assess odorant hedonic value in humans: A naturalistic approach.

BACKGROUND: When you smell an odorant, your first reaction will certainly be either I like it or I dislike it. This primary reaction is a reflection of what is called the "hedonic value" of the odor. Very often, this hedonic value dominates the olfactory percept, more than olfactory identification or intensity. This component of olfactory perception is of primary importance for guiding behavior: avoiding danger (the smell of smoke, gas, etc.), consuming food, or seduction. Olfactory hedonics can be assessed using a large number of methods in humans, including psychophysical measures, autonomic responses, measurement of facial expressions or peripheral nervous activity. All of these techniques have their limitations: subjectivity, invasiveness, need for expertise, etc. A NEW METHOD: The olfactory system is closely linked to the reward system, the role of which is to mediate motivated behavior. In this context, we propose that the capacity odorants have of recruiting the reward system and thus inducing motivated behavior can be used to identify new behavioral parameters to assess odor hedonic value in humans. RESULTS: We recorded freely moving human participants exploring odors emanating from flasks, and showed that five parameters linked to motivated behavior were closely linked to odor hedonics: speed of approach to the nose and withdrawal of the flask containing the odorant, distance between flask and nose, number of samplings, and withdrawal distance (maximal distance between nose and flask after odor sampling). CONCLUSIONS: We highlighted new non-verbal and non-invasive parameters to evaluate olfactory hedonics in humans based on the assessment of odor-motivated behavior.

Regional lung viscoelastic properties in supine and prone position in a porcine model of acute respiratory distress syndrome.

Regional viscoelastic properties of thoracic tissues in acute respiratory distress syndrome (ARDS) and their change with position and positive end-expiratory pressure (PEEP) are unknown. In an experimental porcine ARDS, dorsal and ventral lung (R2,L and E2,L) and chest wall (R2,cw and E2,cw) viscoelastic resistive (R) and elastic (E) parameters were measured at 20, 15, 10, and 5 cmH2O PEEP in supine and prone position. E2 and R2 were obtained by fitting the decay of pressure after end-inspiratory occlusion to the equation: Pviscmax (t) =R2 e-t/τ2, where t is the length of occlusion and τ2 time constant. E2 was equal to R2/τ2. R2,cw and E2,cw were measured from esophageal, dorsal, and ventral pleural pressures. Global R2,L and E2,L were obtained from the global transpulmonary pressure (airway pressure-esophageal pressure), and regional R2,L and E2,L from the dorsal and ventral airway pressure-pleural pressure difference. Lung ventilation was measured by electrical impedance tomography (EIT). Global R2,cw and E2,cw did not change with PEEP or position. Global R2,L [median(Q1-Q3)] was 37.1 (11.0-65.1), 5.1 (4.3-5.5), 12.1 (8.4-19.5), and 41.0 (26.6-53.5) cmH2O/L/s in supine, and 15.3 (9.1-41.9), 7.9 (5.7-11.0), 8.0 (5.1-12.1), and 12.9 (6.4-19.4) cmH2O/L in prone from 20 to 5 cmH2O PEEP (P = 0.06 for PEEP and P = 0.06 for position). Dorsal R2,L significantly and positively correlated with the amount of collapse measured with EIT. Global and regional lung and chest wall viscoelastic parameters can be described by a simple rheological model. Regional E2 and R2 were uninfluenced by PEEP and position except for PEEP on dorsal E2,L and position on dorsal E2,cw.NEW & NOTEWORTHY In a porcine model of acute respiratory distress syndrome, data were successfully fitted to a rheological model of the nonlinear behavior of viscoelastic properties of lung and chest wall at different positive end-expiratory pressure (PEEP) in the supine and prone position. Prone position tended to decrease lung viscoelastic resistive component. PEEP had a significant effect on dorsal lung viscoelastic elastance. Finally, lung viscoelastic resistance correlated with the amount of lung collapse assessed by electrical impedance tomography.

Neural processing of the reward value of pleasant odorants.

Pleasant odorants are represented in the posterior olfactory bulb (pOB) in mice. How does this hedonic information generate odor-motivated behaviors? Using optogenetics, we report here that stimulating the representation of pleasant odorants in a sensory structure, the pOB, can be rewarding, self-motivating, and is accompanied by ventral tegmental area activation. To explore the underlying neural circuitry downstream of the olfactory bulb (OB), we use 3D high-resolution imaging and optogenetics and determine that the pOB preferentially projects to the olfactory tubercle, whose increased activity is related to odorant attraction. We further show that attractive odorants act as reinforcers in dopamine-dependent place preference learning. Finally, we extend those findings to humans, who exhibit place preference learning and an increase BOLD signal in the olfactory tubercle in response to attractive odorants. Thus, strong and persistent attraction induced by some odorants is due to a direct gateway from the pOB to the reward system.

Biomechanical analysis of a fall: velocities at impact.

There are numerous research works focusing on falls of the elderly, proposing various technologies for its detection; however, few research works attempted to describe the mechanical phenomenon itself. In this study, we analyzed the kine(ma)tics of human falls with a simplified biomechanical model of human segments. We present some results based on data found in the literature and in our own laboratory experiments, to conclude that fall velocities at impact can reach values from 3.78 m.s-1 (center of mass) to 6.59 m.s-1 (head).

Analysis and experimental simulation of blood pressure signal distortions observed on intensive care patients monitored by a catheter in radial artery.

The follow-up of patients with hemodynamic instability in intensive care units most often requires blood pressure measurement using a fluid-filled catheter in the radial artery and a fluid-filled tubing connected to a pressure transducer. However, in this usual setup major distortions frequently occur that may alter the pressure signal. Underdamping and overdamping have been well described whereas other types of distortions, including attenuations and varied envelopes of pulse pressure, have been less studied. This study proposes 1) a classification of signal distortions observed on intensive care patients, and 2) several experimental procedures modifying the catheter lumen and the fluid-filled tubing in order to generate a large variety of pressure distortions to more closely mimic the clinical observations.

Estimation of walked distance using inertial sensors under real conditions.

This paper focuses on the comparison of the three most cited algorithms for calculation of walked distance in human, from accelerations measured by inertial sensors placed near the center of mass of human body. The algorithms were tested in real conditions during short walk travels, up to 10 meters, which are typical distances inside the flat. We used two different sensors-one included in a smart phone and our own developed sensor. Results show the differences between tested algorithms in real conditions and their possibilities in real applications, and show better results obtained with the model of the inverted pendulum.

Detection of collaborative activity with Kinect depth cameras.

The health status of elderly subjects is highly correlated to their activities together with their social interactions. Thus, the long term monitoring in home of their health status, shall also address the analysis of collaborative activities. This paper proposes a preliminary approach of such a system which can detect the simultaneous presence of several subjects in a common area using Kinect depth cameras. Most areas in home being dedicated to specific tasks, the localization enables the classification of tasks, whether collaborative or not. A scenario of a 24 hours day shrunk into 24 minutes was used to validate our approach. It pointed out the need of artifacts removal to reach high specificity and good sensitivity.

Characterization of a multi-user indoor positioning system based on low cost depth vision (Kinect) for monitoring human activity in a smart home.

An increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community.

Characterization of physical activity in COPD patients: validation of a robust algorithm for actigraphic measurements in living situations.

We have developed robust embedded algorithms for the real-time classification of activity detected by our wearable inertial device. We collected 224 h of accelerometric signals from 28 subjects [22 suffering from chronic obstructive pulmonary disease (COPD)] to develop and then evaluate our algorithms. We describe the process for determining the most robust parameters of the algorithms. Our results with COPD patients show the feasibility of conducting real-time classification of their activities in everyday situations, with high fidelity.

Ambient intelligence might support increased longevity.

Several technologies entered our homes to change our lives. First electricity brought light and comfort, now communication technologies are transforming our living place into a connected place allowing new services to be invented, comfort, security, wellness and health services. The ICTs in homes can now help prolonge our longevity.

Objective evaluation of body displacements during activities using the wearable inertial system ActimedARM.

This paper presents an algorithm for the objective assessment of the motion of a body during health-evaluation physical tests using our inertial sensor, namely the ActimedARM. With the orientation quaternions provided by the sensor and integrating twice the calibrated acceleration measurements, we are able to compute the displacement of the sensor worn by a patient. To validate our data we have made measurements with both our sensor and a reference optical system. The displacement curves provided by our algorithm were correlated to the gold-standard system with a mean rate of 94.96%.

ActimedARM - design of a wearable system to monitor daily actimetry.

We developed a low power kinematic sensor, ActimedARM, incorporating three-axis accelerometer and magnetometer, a microcontroller ARM3, a ZigBee wireless communication and µSD memory storage. With embedded algorithms it can detect in real time the postures of the subject. A preliminary assessment conducted on 12 subjects reached a 97% correct classification rate. The device exhibits 32 days of autonomy on a 3600 mAh capacity battery, which makes it convenient for field experiments in true daily life.

Building an index of activity of inhabitants from their activity on the residential electrical power line.

In the framework of context awareness within the home, our team is currently assessing the unobtrusive detection of inhabitants' activity through the monitoring of their use and consumption of electricity. The objective is to develop a system for the remote monitoring of large populations of elderly people living independently at home. To be readily deployable on the field, such a system must be minimally intrusive both for the home environment and for the field professionals (paramedics and social workers) visiting the patients at home. We carried out two successive field experiments to evaluate and to improve our system designed to deliver a single index of daily activity. The first experiment involved 13 elderly persons over a nine-month period (84,240 h data recorded) and the second one 12 elderly over six months (51,840 h). We evaluated both the relevance of the index and the acceptability of the system as a whole. We discovered that electrical activity is a kind of unique "signature" of each person's activity. Moreover, this profile provides unexpected information on the health status of the subject. We confirmed that the system was unobtrusive and well accepted both by the subjects and by the professionals involved. Our unique index of activity, and its trend over time, can provide timely information to the professionals on the patient.

SVM-based multimodal classification of activities of daily living in Health Smart Homes: sensors, algorithms, and first experimental results.

By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.

Evaluation of the autonomic nervous system for fall detection.

Studies show that the proportion of elderly will reach 30% of the total population by 2050 in developed countries, such as France. The elderly live generally alone, thus many health problems related to age are under reported. Falling is one of these problems and several devices have been developed recently, based on accelerometers, in order to detect it and alert carers. In order to improve the detection success of these devices, we propose quantifying autonomic nervous system activity (ANS) using a wearable ambulatory device developed for this purpose. We studied the A.N.S's response on 7 adult subjects during simulated falls and standing-lying transitions. We implemented a classification method using the Support Vector Machine in order to classify these two situations using measured heart rate variability and electrodermal response. Good results (sensibility = 70.37%, specificity = 80%, positive predictor = 73.8%) were obtained using a Polynomial kernel (p = 5) for the support vector machine implementation.

Supervised classification of Activities of Daily Living in Health Smart Homes using SVM.

By 2050, about a third of the French population will be over 65. To face this modification of the population, the current studies of our laboratory focus on the monitoring of elderly people at home. This aims at detect, as early as possible, a loss of autonomy by objectivizing criterions such as the international ADL or the French AGGIR scales implementing automatic classification of the different Activities of Daily Living. A Health Smart Home is used to achieve this goal. This flat includes different sensors. The data from the various sensors were used to classify each temporal frame into one of the activities of daily living that has been previously learnt (seven activities: hygiene, toilets, eating, resting, sleeping, communication and dressing/undressing). This is done using Support Vector Machines. We performed an experimentation with 13 young and healthy subjects to learn the model of activities and then we tested the classification algorithm (cross-validation) on real data.

Health smart home for elders - a tool for automatic recognition of activities of daily living.

Elders live preferently in their own home, but with aging comes the loss of autonomy and associated risks. In order to help them live longer in safe conditions, we need a tool to automatically detect their loss of autonomy by assessing the degree of performance of activities of daily living. This article presents an approach enabling the activities recognition of an elder living alone in a home equipped with noninvasive sensors.

User-based motion sensing and fuzzy logic for automated fall detection in older adults.

More than one third of community-dwelling older adults and up to 60% of nursing home residents fall each year, with 10-15% of fallers sustaining a serious injury. Reliable automated fall detection can increase confidence in people with fear of falling, promote active safe living for older adults, and reduce complications from falls. The performance of a 2-stage fall detection algorithm using impact magnitudes and changes in trunk angles derived from user-based motion sensors was evaluated under laboratory conditions. Ten healthy participants were instrumented on the front and side of the trunk with 3D accelerometers. Participants simulated 9 fall conditions and 6 common activities of daily living. Fall conditions were simulated on a protective mattress. The experimental data set comprised 750 events (45 fall events and 30 nonfall events per participant) that were classified by the fall detection algorithm as either a fall or a nonfall using inputs from 3D accelerometers. Significant differences for impacts recorded, trunk angle changes (p<0.01), and detection performances (p<0.05) were found between fall and nonfall conditions. The proposed algorithm detected fall events during simulated fall conditions with a success rate of 93% and a false-positive rate of 29% during nonfall conditions. Despite a slightly superior identification performance for the accelerometer located on the front of the trunk, no significant differences were found between the two motion sensor locations. Automated detection of fall events based on user-based motion sensing and fuzzy logic shows promising results. Additional rules and optimization of the algorithm will be needed to decrease the false-positive rate.