Unobtrusive Monitoring Research Articles

Requirements for Unobtrusive Monitoring to Support Home-Based Dementia Care: Qualitative Study Among Formal and Informal Caregivers.

BackgroundDue to a growing shortage in residential care, people with dementia will increasingly be encouraged to live at home for longer. Although people with dementia prefer extended independent living, this also puts more pressure on both their informal and formal care networks. To support (in)formal caregivers of people with dementia, there is growing interest in unobtrusive contactless in-home monitoring technologies that allow caregivers to remotely monitor the lifestyle, health, and safety of their care recipients. Despite their potential, these solutions will only be viable if they meet the expectations and needs of formal and informal caregivers of people with dementia.ObjectiveThe objective of this study was to explore the expected benefits, barriers, needs, and requirements toward unobtrusive in-home monitoring from the perspective of formal and informal caregivers of community-dwelling people with dementia.MethodsA combination of semistructured interviews and focus groups was used to collect data among informal (n=19) and formal (n=16) caregivers of people with dementia. Both sets of participants were presented with examples of unobtrusive in-home monitoring followed by questions addressing expected benefits, barriers, and needs. Relevant in-home monitoring goals were identified using a previously developed topic list. Interviews and focus groups were transcribed and inductively analyzed. Requirements for unobtrusive in-home monitoring were elicited based on the procedure of van Velsen and Bergvall-Kåreborn.ResultsFormal and informal caregivers saw unobtrusive in-home monitoring as a support tool that should particularly be used to monitor (the risk of) falls, day and night rhythm, personal hygiene, nocturnal restlessness, and eating and drinking behavior. Generally, (in)formal caregivers reported cross-checking self-care information, extended independent living, objective communication, prevention and proactive measures, emotional reassurance, and personalized and optimized care as the key benefits of unobtrusive in-home monitoring. Main concerns centered around privacy, information overload, and ethical concerns related to dehumanizing care. Furthermore, 16 requirements for unobtrusive in-home monitoring were generated that specified desired functions, how the technology should communicate with the user, which services surrounding the technology were seen as needed, and how the technology should be integrated into the existing work context.ConclusionsDespite the presence of barriers, formal and informal caregivers of people with dementia generally saw value in unobtrusive in-home monitoring, and felt that these systems could contribute to a shift from reactive to more proactive and less obtrusive care. However, the full potential of unobtrusive in-home monitoring can only unfold if relevant concerns are considered. Our requirements can inform the development of more acceptable and goal-directed in-home monitoring technologies to support home-based dementia care.

Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection.

With the arrival of the internet of things, smart environments are becoming increasingly ubiquitous in our everyday lives. Sensor data collected from smart home environments can provide unobtrusive, longitudinal time series data that are representative of the smart home resident's routine behavior and how this behavior changes over time. When longitudinal behavioral data are available from multiple smart home residents, differences between groups of subjects can be investigated. Group-level discrepancies may help isolate behaviors that manifest in daily routines due to a health concern or major lifestyle change. To acquire such insights, we propose an algorithmic framework based on change point detection called Behavior Change Detection for Groups (BCD-G). We hypothesize that, using BCD-G, we can quantify and characterize differences in behavior between groups of individual smart home residents. We evaluate our BCD-G framework using one month of continuous sensor data for each of fourteen smart home residents, divided into two groups. All subjects in the first group are diagnosed with cognitive impairment. The second group consists of cognitively healthy, age-matched controls. Using BCD-G, we identify differences between these two groups, such as how impairment affects patterns of performing activities of daily living and how clinically-relevant behavioral features, such as in-home walking speed, differ for cognitively-impaired individuals. With the unobtrusive monitoring of smart home environments, clinicians can use BCD-G for remote identification of behavior changes that are early indicators of health concerns.

Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor.

This work aimed to evaluate whether a radar sensor can distinguish sleep from wakefulness in real time. The sensor detects body movements without direct physical contact with the subject and can be embedded in the roof of a hospital room for completely unobtrusive monitoring. We conducted simultaneous recordings with polysomnography, actigraphy, and radar on two groups: healthy young adults (n = 12, four nights per participant) and patients referred to a sleep examination (n = 28, one night per participant). We developed models for sleep/wake classification based on principles commonly used by actigraphy, including real-time models, and tested them on both datasets. We estimated a set of commonly reported sleep parameters from these data, including total-sleep-time, sleep-onset-latency, sleep-efficiency, and wake-after-sleep-onset, and evaluated the inter-method reliability of these estimates. Classification results were on-par with, or exceeding, those often seen for actigraphy. For real-time models in healthy young adults, accuracies were above 92%, sensitivities above 95%, specificities above 83%, and all Cohen's kappa values were above 0.81 compared to polysomnography. For patients referred to a sleep examination, accuracies were above 81%, sensitivities about 89%, specificities above 53%, and Cohen's kappa values above 0.44. Sleep variable estimates showed no significant intermethod bias, but the limits of agreement were quite wide for the group of patients referred to a sleep examination. Our results indicate that the radar has the potential to offer the benefits of contact-free real-time monitoring of sleep, both for in-patients and for ambulatory home monitoring.

A Sleep Stage Classification Algorithm of Wearable System Based on Multiscale Residual Convolutional Neural Network

Sleep disorder is a serious public health problem. Unobtrusive home sleep quality monitoring system can better open the way of sleep disorder‐related diseases screening and health monitoring. In this work, a sleep stage classification algorithm based on multiscale residual convolutional neural network (MRCNN) was proposed to detect the characteristics of electroencephalogram (EEG) signals detected by wearable systems and classify sleep stages. EEG signals were analyzed in each epoch of every 30 seconds, and then 5‐class sleep stage classification, wake (W), rapid eye movement sleep (REM), and nonrapid eye movement sleep (NREM) including N1, N2, and N3 stages was outputted. Good results (accuracy rate of 92.06% and 91.13%, Cohen’s kappa of 0.7360 and 0.7001) were achieved with 5‐fold cross‐validation and independent subject cross‐validation, respectively, which performed on European Data Format (EDF) dataset containing 197 whole‐night polysomnographic sleep recordings. Compared with several representative deep learning methods, this method can easily obtain sleep stage information from single‐channel EEG signals without specialized feature extraction, which is closer to clinical application. Experiments based on CinC2018 dataset also proved that the method has a good performance on large dataset and can provide support for sleep disorder‐related diseases screening and health surveillance based on automatic sleep staging.

Support, Health, Activities, Resources, & Education (SHARE): New Program Innovations for Early-Stage Families

Persons living with a recent diagnosis of dementia experience great uncertainty and stress as they and their families try to adjust to the new reality of their lives and futures. One fruitful strategy for intervening with these families is to include both the person living with dementia and their family care partner in the program. Although dyadic approaches are rare among early-stage programs, promising examples exist. The SHARE Program (Support, Health, Activities, Resources, and Education) is one exception where dyadic materials address: 1) current and long- term needs of care partners, and 2) how the family can develop a realistic plan of care based on their care values and preferences. This symposium describes the development and positive outcomes of the original SHARE intervention and the promising adaptations that expand how and to whom the intervention is delivered. Presentations explore: 1) the original SHARE for Dementia program and strategies for expanding its reach into chronic conditions populations (Orsulic-Jeras & Whitlatch), 2) a group version translated into Spanish (“EPIC: Early-stage Partners in Care,” Dr. Coon), and 3) the development of a remote needs assessment and unobtrusive in-home monitoring technology platform that guides care planning and helps to maintain independence (“SHARE-sense,” Dr. Miller). Discussion will focus on the challenges, unique solutions, and positive outcomes when adapting SHARE to different settings and populations (Dr. Zarit).

Unobtrusive monitoring: Statistical dissemination latency estimation in Bitcoin's peer-to-peer network.

The cryptocurrency system Bitcoin uses a peer-to-peer network to distribute new transactions to all participants. For risk estimation and usability aspects of Bitcoin applications, it is necessary to know the time required to disseminate a transaction within the network. Unfortunately, this time is not immediately obvious and hard to acquire. Measuring the dissemination latency requires many connections into the Bitcoin network, wasting network resources. Some third parties operate that way and publish large scale measurements. Relying on these measurements introduces a dependency and requires additional trust. This work describes how to unobtrusively acquire reliable estimates of the dissemination latencies for transactions without involving a third party. The dissemination latency is modelled with a lognormal distribution, and we estimate their parameters using a Bayesian model that can be updated dynamically. Our approach provides reliable estimates even when using only eight connections, the minimum connection number used by the default Bitcoin client. We provide an implementation of our approach as well as datasets for modelling and evaluation. Our approach, while slightly underestimating the latency distribution, is largely congruent with observed dissemination latencies.

Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning.

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.

Alert system design based on experimental findings from long-term unobtrusive monitoring in COPD

An observational study using a cough count monitor was executed in the home of 30 patients with Chronic Obstructive Pulmonary Disease (COPD). The monitoring system was unobtrusive, allowing long-term monitoring. This paper illustrates the cough counts obtained from this monitoring using a personalized cough classifier. In particular, the data highlights a number of patient-specific parameters measured in baseline periods such as the average and mean cough count, and the pattern of the cough count distribution over the course of the day. Next to describing various specific patterns disclosed by the clinical study, the paper outlines the basic components in the design of an alert system compatible with the observed experimental findings. In particular, a novel cough count scale is introduced to facilitate the construction of an alert mechanism to detect worsening in respiratory condition.

Miniaturized Electronic Circuit Design Challenges for Ingestible Devices

The gastro-intestinal (GI) tract is a very important part of the human digestive system but remains largely inaccessible for unobtrusive monitoring. Ingestible electronic devices are a very interesting concept to address this need. This paper discusses the current state of ingestible electronic devices and explores the potential they hold to provide unprecedented insight by measuring several aspects of the complex bio-chemical processes that happen in the GI tract in real-time. This will require novel sensor technology capable of measuring relevant markers with adequate specificity and accuracy. In order to be able to scale this functionality down to a form factor of an easily digestible pill, ultra-low-power wireless interface electronics are needed. This poses certain challenges and innovation opportunities related to sensing, wireless communication and powering to enable the next generation of ingestible electronic devices. [2020-0164]

Proposal of a Lab Bench for the Unobtrusive Monitoring of the Bladder Fullness with Bioimpedance Measurements.

(1) Background: millions of people, from children to the elderly, suffer from bladder dysfunctions all over the world. Monitoring bladder fullness with appropriate miniaturized textile devices can improve, significantly, their daily life quality, or even cure them. Amongst the existing bladder sensing technologies, bioimpedance spectroscopy seems to be the most appropriate one to be integrated into textiles. (2) Methods: to assess the feasibility of monitoring the bladder fullness with textile-based bioimpedance spectroscopy; an innovative lab-bench has been designed and fabricated. As a step towards obtaining a more realistic pelvic phantom, ex vivo pig’s bladder and skin were used. The electrical properties of the fabricated pelvic phantom have been compared to those of two individuals with tetrapolar impedance measurements. The measurements’ reproducibility on the lab bench has been evaluated and discussed. Moreover, its suitability for the continuous monitoring of the bladder filling has been investigated. (3) Results: although the pelvic phantom failed in reproducing the frequency-dependent electrical properties of human tissues, it was found to be suitable at 5 kHz to record bladder volume change. The resistance variations recorded are proportional to the conductivity of the liquid filling the bladder. A 350 mL filling with artificial urine corresponds to a decrease in resistance of 7.2%, which was found to be in the same range as in humans. (4) Conclusions: based on that resistance variation; the instantaneous bladder fullness can be extrapolated. The presented lab-bench will be used to evaluate the ability of textiles electrodes to unobtrusively monitor the bladder volume.

Wearable Temperature Sensors with Enhanced Sensitivity by Engineering Microcrack Morphology in PEDOT:PSS-PDMS Sensors.

Wearable temperature sensors with high sensitivity, linearity, and flexibility are required to meet the increasing demands for unobtrusive monitoring of temperature changes indicative of the onset of infections and diseases. Herein, we present a new method for engineering highly sensitive and flexible temperature sensors made by sandwiching a poly(3,4-ethylenedioxythiophene):polystyrene (PEDOT:PSS) sensing film between two poly(dimethylsiloxane) (PDMS) substrates. Pre-stretching the sensor to a certain strain can create stable microcracks in the sensing layer that bestow high senstivity and linearity. The average length and density of the microcracks, which dictate the sensor's temperature sensitivity, can be engineered by controlling three key processing parameters, incuding (a) pre-stretching strain, (b) sulfuric acid treatment time, and (c) surface roughness of the substrate. For a given acid treatment time and surface roughness condition, the density and average length of the microcracks increase pre-stretching strain. A smooth PDMS substrate tends to yield long and straight cracks in the PEDOT:PSS film, compared to shorter microcracks with higher density on rough surfaces. Crack density can be further increased via sulfuric acid treatment with an optimum duration of approximately 3 h. Prolonged treatment would result in weak adhesion between the PEDOT:PSS film and the PDMS substrate, which in turn reduces the microcrack density but increases the crack length. By optimizing the three design parameters we have designed a high performance PEDOT:PSS-PDMS sensor that provides a combined high temperature sensitivity of 0.042 °C-1 with an excellent linearity of 0.998 (from 30 to 55 °C), better than the highest temperature sensitivity of PEDOT:PSS based sensors reported in the literature. With a good optical transparency, high temperature sensitivity, excellent linearity, and high flexibility, this microcrack-based sensor is a very promising wearable temperature-sensing solution.

Stretchable screen-printed PEDOT:PSS electrodes for upper-arm surface electromyography.

In the past ten years, wearable electronics underwent tremendous growth. Undoubtedly, one of the fields that led this trend is represented by biomedical applications. In this field, wearable technologies can provide unique features such as the unobtrusive monitoring of biopotentials. Polymerbased electrodes developed for this purpose can take advantage of their seamless integration in the garments. However, the available solutions exhibit fragility in relation with the stretchability of the fabric, causing significant performance degradation.In this work, this problem is tackled by a novel deposition approach based on screen-printing technology. The electrodes are deposited onto the pre-stretched fabric to ensure the full functionality during common operating conditions. To this aim, a novel PEDOT:PSS conductive ink formulation and printing procedure were conceived. In order to prove the electrode performance for surface electromyography, we printed the electrodes directly onto a commercial stretchable polyester sleeve for sport applications. The electrodes allowed to reliably record the muscular activity of the forearm with performance comparable to that of commercial gelled Ag/AgCl electrodes. The obtained results suggest that the proposed approach can be valuably used in health and fitness applications.

Investigation of a Ballistocardiogram-Based Technique for Unobtrusive Monitoring of Fluid Accumulation in the Body.

We introduce a novel monitoring solution for fluid accumulation in the human body (e.g. internal bleeding), based on observation of a selected energy-describing feature of the Ballistocardiogram (BCG) signal. It is hypothesized that, because of additional damping generated by the fluid, BCG signal energy decreases as compared to its baseline value. Data were collected from 15 human volunteers via accelerometers attached to the participants' body, and an electromechanical-film (EMFi) sensor-equipped bed. Fluid accumulation along the gastrointestinal (GI) tract was induced by means of water intake by the participants, and the BCG signal was recorded before and after intake. Based on performance evaluation, we selected a suitable energy feature and sensing channel amongst the ones investigated. The chosen feature showed a significant decrease in signal energy from baseline to after-intake condition (p-value<0.001), and identified the presence of fluid accumulation with high sensitivity (90% in bed-based, and 100% in standing-position monitoring).

Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial

BackgroundMobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely.ObjectiveWe explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities.MethodsPD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored.ResultsOf the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor.ConclusionsWe found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care.Trial RegistrationISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879International Registered Report Identifier (IRRID)RR2-10.1186/s13063-018-2767-4

Detection of Artifacts in Ambulatory Electrodermal Activity Data

Recent wearable devices enable continuous and unobtrusive monitoring of human's physiological parameters, like e.g., electrodermal activity and heart rate, over long periods of time in everyday life settings. Continuous monitoring of these parameters enables the creation of systems able to predict affective states and stress with the goal of providing feedback to improve them. Deployment of such systems in everyday life settings is still complex and prone to errors due to the low quality of the collected data impacted by the presence of artifacts. In this paper we present an automatic approach to detect artifacts in electrodermal activity (EDA) signals collected in-the-wild over long periods of time. To this end we first perform a systematic literature review and compile a set of guidelines for human annotators to label artifacts manually and we use these labels as ground-truth to test our automatic approach. To evaluate our approach, we collect physiological data from 13 participants in-the-wild and two human annotators label 107.56 hours of this data set. We make the data set publicly available to other researchers upon request. Our model achieves a recall of 98% for clean and shape artifacts classification on data collected in-the-wild using leave-one-subject-out cross-validation, which is 42 percentage points higher than the baseline. We show that state of the art approaches do not generalize well when tested with completely in-the-wild data and identify only 17% of the artifacts present in our data set, even after manual adaption. We further test the robustness of our approach over time using leave-one-day-out and achieve very similar performance. We then introduce a new metric to evaluate the quality of EDA segments that considers the impact of not only artifacts in the shape of EDA but also artifacts generated by environmental temperature changes or user's high intensity movement. Our results imply that we can eliminate the need for human annotators or significantly reduce the time they need to label data. Also, our approach can be used in an online manner to automatically detect artifacts in EDA signals.

MobiEye: turning your smartphones into a ubiquitous unobtrusive vital sign monitoring system

Recent advances in mobile and wearable technologies have stimulated significantly growing demands for more affordable, user-friendly, pervasive healthcare solutions that can be adopted by the public to proactively manage their health conditions and alleviate the burdens of hospitalization. This study seeks to propose a personalized ubiquitous health monitoring system that can unobtrusively monitor individuals’ vital signs, anywhere at any time. The proposed MobiEye framework makes use of the regular camera available on any smartphones or tablets to record various most important physiological signals without the need for acquiring extra specialized medical devices or attaching any sensor to the body. Through recording the reflected light intensities corresponding to the subtle blood flow changes with blood volume pulses, the proposed technique accurately extracts blood volume pulses from the facial videos recorded in real-world scenarios with the designed protocol. Experiments show that the proposed system achieved $$96 \, \% \,$$ accuracy on average (with the standard deviations of $$\pm 1.2$$ ) for the heart rate estimation and higher correlations between the pulse transit time and the reference systolic blood pressure $$(mean \, r = 0.89,\, SE= 0.05)$$ .

Smart Electronic Eyedrop Bottle for Unobtrusive Monitoring of Glaucoma Medication Adherence.

Glaucoma, the leading cause of irreversible blindness, affects >70 million people worldwide. Lowering intraocular pressure via topical administration of eye drops is the most common first-line therapy for glaucoma. This treatment paradigm has notoriously high non-adherence rates: ranging from 30% to 80%. The advent of smart phone enabled technologies creates promise for improving eyedrop adherence. However, previous eyedrop electronic monitoring solutions had awkward medication bottle adjuncts and crude software for monitoring the administration of a drop that adversely affected their ability to foster sustainable improvements in adherence. The current work begins to address this unmet need for wireless technology by creating a “smart drop” bottle. This medication bottle is instrumented with sensing electronics that enable detection of each eyedrop administered while maintaining the shape and size of the bottle. This is achieved by a thin electronic force sensor wrapped around the bottle and underneath the label, interfaced with a thin electronic circuit underneath the bottle that allows for detection and wireless transmission to a smart-phone application. We demonstrate 100% success rate of wireless communication over 75 feet with <1% false positive and false negative rates of single drop deliveries, thus providing a viable solution for eyedrop monitoring for glaucoma patients.

Multimodal Monitoring of Cardiovascular Responses to Postural Changes.

BackgroundIn the poorly understood relationship between orthostatic hypotension and falls, next to blood pressure (BP), baroreflex sensitivity (BRS) and cerebral autoregulation (CAR) may be key measures. The posture- and movement dependency of orthostatic hypotension requires continuous and unobtrusive monitoring. This may be possible using simultaneous photoplethysmography (PPG), electrocardiography (ECG), and near-infrared spectroscopy (NIRS) signal recordings, from which pulse wave velocity (PWV; potentially useful for BP estimation), BRS and CAR can be derived. The PPG, NIRS and PWV signal correlation with BP and BRS/CAR reliability and validity need to be addressed.MethodsIn 34 healthy adults (mean age 25 years, inter quartile range 22–45; 10 female), wrist and finger PPG, ECG, bifrontal NIRS (oxygenated and deoxygenated hemoglobin) and continuous BP were recorded during sit to stand and supine to stand movements. Sixteen participants performed slow and rapid supine to stand movements; eighteen other participants performed a 1-min squat movement. Pulse wave velocity (PWV) was defined as the inverse of the ECG R-peak to PPG pulse delay; PPG, NIRS and PWV signal correlation with BP as their Pearson correlations with mean arterial pressure (MAP) within 30 s after the postural changes; BRS as inter beat interval drop divided by systolic BP (SBP) drop during the postural changes; CAR as oxygenated hemoglobin drop divided by MAP drop. BRS and CAR were separately computed using measured and estimated (linear regression) BP. BRS/CAR reliability was defined by the intra class correlation between repeats of the same postural change; validity as the Pearson correlation between BRS/CAR values based on measured and estimated BP.ResultsThe highest correlation with MAP was found for finger PPG and oxygenated hemoglobin, ranging from 0.75–0.79 (sit to stand), 0.66–0.88 (supine to stand), and 0.82–0.94 (1-min squat). BRS and CAR reliability was highest during the different supine to stand movements, ranging from 0.17 – 0.49 (BRS) and 0.42-0.75 (CAR); validity was highest during rapid supine to stand movements, 0.54 and 0.79 respectively.ConclusionPPG-ECG-NIRS recordings showed high correlation with BP and enabled computation of reliable and valid BRS and CAR estimates, suggesting their potential for continuous unobtrusive monitoring of orthostatic hypotension key measures.

Circadian Rhythms in the Telephone Calls of Older Adults: Observational Descriptive Study.

BackgroundRecent studies have thoughtfully and convincingly demonstrated the possibility of estimating the circadian rhythms of young adults’ social activity by analyzing their telephone call-detail records (CDRs). In the field of health monitoring, this development may offer new opportunities for supervising a patient’s health status by collecting objective, unobtrusive data about their daily social interactions. However, before considering this future perspective, whether and how similar results could be observed in other populations, including older ones, should be established.ObjectiveThis study was designed specifically to address the circadian rhythms in the telephone calls of older adults.MethodsA longitudinal, 12-month dataset combining CDRs and questionnaire data from 26 volunteers aged 65 years or older was used to examine individual differences in the daily rhythms of telephone call activity. The study used outgoing CDRs only and worked with three specific telecommunication parameters: (1) call recipient (alter), (2) time of day, and (3) call duration. As did the studies involving young adults, we analyzed three issues: (1) the existence of circadian rhythms in the telephone call activity of older adults, (2) their persistence over time, and (3) the alter-specificity of calls by calculating relative entropy.ResultsWe discovered that older adults had their own specific circadian rhythms of outgoing telephone call activity whose salient features and preferences varied across individuals, from morning until night. We demonstrated that rhythms were consistent, as reflected by their persistence over time. Finally, results suggested that the circadian rhythms of outgoing telephone call activity were partly structured by how older adults allocated their communication time across their social network.ConclusionsOverall, these results are the first to have demonstrated the existence, persistence, and alter-specificity of the circadian rhythms of the outgoing telephone call activity of older adults. These findings suggest an opportunity to consider modern telephone technologies as potential sensors of daily activity. From a health care perspective, these sensors could be harnessed for unobtrusive monitoring purposes.

Ballistocardiography Can Estimate Beat-to-Beat Heart Rate Accurately at Night in Patients After Vascular Intervention.

While bed-integrated ballistocardiography (BCG) has potential clinical applications such as unobtrusive monitoring of patients staying in the general hospital ward, it has so far mainly gained interest in the wellness domain. In this article, the potential of BCG to monitor hospitalized patients after surgical intervention was assessed. Long-term BCG recordings (mean duration 17.7h) of 14 patients were performed with an EMFit QS bed sensor. In addition, ten healthy subjects were recorded during sleep (mean duration 7.8h). Using an iterative algorithm, beat-to-beat intervals (BBIs) and the ultra-short-term heart-rate-variability (HRV) parameters standard deviation of NN intervals (SDNN) and root mean square of successive differences (RMSSD) were estimated and compared to an ECG reference in terms of average estimation error and temporal coverage. While the absolute BBI estimation error was found to be higher when full-day patient data was used (16.5ms), no significant difference between healthy subjects (12.7ms) and patient nighttime data (11.0ms) was observed. Nevertheless, temporal coverage of BBI estimation was significantly lower in patients (39.3% overall, 51.7% at night) compared to the healthy sleepers (73.2%). This resulted in reduced HRV estimation coverage (9.7% vs. 37.2%) at comparable estimation error levels.