Geriatric Care Management System Powered by the IoT and Computer Vision Techniques.

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Induced pain affects auricular and body biosignals: From cold stressor to deep breathing.

Pain affects every fifth adult worldwide and is a significant health problem. From a physiological perspective, pain is a protective reaction that restricts physical functions and causes responses in physiological systems. These responses are accessible for evaluation via recorded biosignals and can be favorably used as feedback in active pain therapy via auricular vagus nerve stimulation (aVNS). The aim of this study is to assess the significance of diverse parameters of biosignals with respect to their deflection from cold stressor to deep breathing and their suitability for use as biofeedback in aVNS stimulator. Seventy-eight volunteers participated in two cold pressors and one deep breathing test. Three targeted physiological parameters (RR interval of electrocardiogram, cardiac deflection magnitude Z AC of ear impedance signal, and cardiac deflection magnitude PPG AC of finger photoplethysmogram) and two reference parameters (systolic and diastolic blood pressures BP S and BP D) were derived and monitored. The results show that the cold water decreases the medians of targeted parameters (by 5.6, 9.3%, and 8.0% of RR, Z AC, and PPG AC, respectively) and increases the medians of reference parameters (by 7.1% and 6.1% of BP S and BP D, respectively), with opposite changes in deep breathing. Increasing pain level from relatively mild to moderate/strong with cold stressor varies the medians of targeted and reference parameters in the range from 0.5% to 6.0% (e.g., 2.9% for RR, Z AC and 6.0% for BP D). The physiological footprints of painful cold stressor and relaxing deep breathing were shown for auricular and non-auricular biosignals. The investigated targeted parameters can be used as biofeedback to close the loop in aVNS to personalize the pain therapy and increase its compliance.

High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF.

Background: Consumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases. Objective: DoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats. Methods and Results: We performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as "AF," "SR" or "Cannot be concluded." In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012). Conclusions: This is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.

Detection of Walk Tests in Free-Living Activities Using a Wrist-Worn Device.

Exercise testing to assess the response to physical rehabilitation or lifestyle interventions is administered in clinics thus at best can be repeated only few times a year. This study explores a novel approach to collecting information on functional performance through walk tests, e.g., a 6-min walk test (6MWT), unintentionally performed in free-living activities. Walk tests are detected in step data provided by a wrist-worn device. Only those events of minute-to-minute variation in walking cadence, which is equal or lower than the empirically determined maximal SD (e.g., 5-steps), are considered as walk test candidates. Out of detected walk tests within the non-overlapping sliding time interval (e.g., 1-week), the one with the largest number of steps is chosen as the most representative. This approach is studied on a cohort of 99 subjects, assigned to the groups of patients with cardiovascular disease (CVD) and healthy subjects below and over 40-years-old, who were asked to wear the device while maintaining their usual physical activity regimen. The total wear time was 8,864 subject-days after excluding the intervals of occasionally discontinued monitoring. About 82% (23/28) of patients with CVD and 88% (21/24) of healthy subjects over 40-years-old had at least a single 6MWT over the 1st month of monitoring. About 52% of patients with CVD (12/23) and 91% (19/21) of healthy subjects over 40-years-old exceeded 500 m. Patients with CVD, on average, walked 46 m shorter 6MWT distance (p = 0.04) compared to healthy subjects. Unintentional walk testing is feasible and could be valuable for repeated assessment of functional performance outside the clinical setting.

Exposure to total 36-hr sleep deprivation reduces physiological and psychological thermal strain to whole-body uncompensable passive heat stress in young adult men.

Total sleep deprivation (TSD) is associated with endothelial dysfunction and a consequent decrease in vascular reactivity and increase in peripheral vascular resistance. These effectors compromise the body's ability to thermoregulate in hot and cold stress conditions. We investigated heat-unacclimated young adult men (26 ± 2 years) to determine whether 36 hr of TSD compared to an 8 or 4-hr sleep condition, would suppress the responses of the autonomic system (body rectal temperature [Tre ], heart rate [HR], root mean square of successive interbeat intervals, physiological strain, blood pressure [BP], circulating blood catecholamines, sweating rate and subjective sensations) to whole-body uncompensable passive heat stress in traditional Finnish sauna heat (Tair  = 80-90°C, rh = 30%). Sauna bathing that induced whole-body hyperthermia had a residual effect on reducing BP in the 8-hr and 4-hr sleep per night conditions according to BP measurements. By contrast, 36 hr of total wakefulness led to an increase in BP. These observed sleep deprivation-dependent differences in BP modifications were not accompanied by changes in the blood plasma epinephrine and norepinephrine concentrations. However, during sauna bathing, an increase in BP following 36 hr of TSD was accompanied by significant decreases in body Tre , HR and physiological strain, together with a diminished sweating rate, enhanced vagus-mediated autonomic control of HR variability, and improved thermal perception by the subjects. Our results suggest the impaired ability of the body to accumulate external heat in the body's core under uncompensable passive heat conditions following 36 hr of TSD, because of the TSD-attenuated autonomic system response to acute heat stress.

Estimation of Heart Rate Recovery after StairClimbing Using aWrist-Worn Device.

Heart rate recovery (HRR) after physical exercise is a convenient method to assesscardiovascular autonomic function. Since stair climbing is a common daily activity, usually followedby a slow walking or rest, this type of activity can be considered as an alternative HRR test.The present study explores the feasibility to estimate HRR parameters after stair climbing usinga wrist-worn device with embedded photoplethysmography and barometric pressure sensors.A custom-made wrist-worn device, capable of acquiring heart rate and altitude, was used to estimatethe time-constant of exponential decay t, the short-term time constant S, and the decay of heart ratein 1 min D. Fifty-four healthy volunteers were instructed to climb the stairs at three different climbingrates. When compared to the reference electrocardiogram, the absolute and percentage errors werefound to be ≤ 21.0 s ( 52.7%) for τ, ≤ 0.14 (≤ 19.2%) for S, and ≤ 7.16 bpm (≤ 20.7%) for D in 75%of recovery phases available for analysis. The proposed approach to monitoring HRR parameters inan unobtrusive way may complement information provided by personal health monitoring devices(e.g., weight loss, physical activity), as well as have clinical relevance when evaluating the efficiencyof cardiac rehabilitation program outside the clinical setting.

Towards pulse rate parametrization during free-living activities using smart wristband.

OBJECTIVE: The growing interest to integrate consumer smart wristbands in eHealth applications spawns the need for novel approaches of data parametrization which account for the technology-specific constraints. The present study aims to investigate the feasibility of a consumer smart wristband to be used for computing pulse rate parameters during free-living activities. APPROACH: The feasibility of computing pulse rate variability (PRV) as well as pulse rate and physical activity-related parameters using the smart wristband was investigated, having an electrocardiogram as a reference. The parameters were studied on the pulse rate and step data from 54 participants, diagnosed with various cardiovascular diseases. The data were acquired during free-living activities with no user lifestyle intervention. MAIN RESULTS: The comparison results show that the smart wristband is well-suited for computing the mean interbeat interval and the standard deviation of the averaged interbeat intervals. However, it is less reliable when estimating frequency domain and nonlinear parameters. Heart recovery time, estimated by fitting an exponential model to the events, satisfying the conditions of the 3 min step test, showed satisfactory agreement (relative error <20%) with the reference ECG in one-third of all cases. On the other hand, the heart's adaptation to physical workload, expressed as the slope of the linear regression curve, was underestimated in most cases. SIGNIFICANCE: The present study demonstrates that pulse rate parametrization using a consumer smart wristband is in principle feasible. The results show that the smart wristband is well suited for computing basic PRV parameters which have been reported to be associated with poorer health outcomes. In addition, the study introduces a methodology for the estimation of post-exercise heart recovery time and the heart's adaptation to physical workload during free-living activities.