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Activity graph. Each circle is 1 day. Angle around each circle represents the hour in the day (Zero at the top refers to midnight, and 12 refers to noon time). The innermost circle is the first day the patient became COVID negative, while the outermost circle is the last monitoring day for that patient. The different colors refer to different locations: bed in blue, chair in yellow, and outside the room in white. In total, there are 87 days for subject 1, 87 days for subject 2, and 99 days for subject 3 (Missing days are not visualized).
Source publication
Currently, there is a limited understanding of long-term outcomes of COVID-19, and a need for in-home measurements of patients through the whole course of their disease. We study a novel approach for monitoring the long-term trajectories of respiratory and behavioral symptoms of COVID-19 patients at home. We use a sensor that analyzes the radio sig...
Citations
... For example, devices emitting low-power radio signals can estimate respiration and heart rate by analyzing the signals reflected off the body [49]. Combining these data with AI could enable continuous vitals monitoring or symptom evaluation for COVID-19 [50], Parkinson's disease [51], and other conditions. Like wearable sensors, instrumented environments may offer significant economic advantages by reducing the need for regular clinic visits [52]. ...
Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient’s outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.
... The bedside radars use the doppler radar technique while the under-mattress devices employ several different technologies such as electromechanical films and pneumatic sensors [28,29]. Due to their inconspicuous nature and low maintenance, they do not pose any of the burdens imposed by wearables and are an ideal tool for continuous monitoring of vital signs, behavioural information, and sleep in community dwelling older adult populations, especially in PLWD [13,14,30,31]. ...
Introduction:
Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual and particularly so in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realise this potential, these technologies need to be evaluated against gold standard measures and in relevant populations.
Methods:
We evaluated the accuracy of heart rate and breathing rate measurements of three contactless technologies (two under-mattress trackers: Withings sleep analyser (WSA) and Emfit QS (Emfit) and a bedside radar: Somnofy) in a sleep laboratory environment and assessed their potential to capture vital signs (heart rate and breathing rate) in a real-world setting. Data were collected in 35 community dwelling older adults aged between 65 and 83 years (mean ± SD: 70.8 ± 4.9; 21 men) during a one-night clinical polysomnography (PSG) in a sleep laboratory, preceded by 7 to 14 days of data collection at-home. Several of the participants had health conditions including type-2 diabetes, hypertension, obesity, and arthritis and ≈ 49% (n = 17) had moderate to severe sleep apnea while ≈ 29% (n = 10) had periodic leg movement disorder. The under-mattress trackers provided estimates of both heart rate and breathing rate while the bedside radar provided only breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared to PSG electrocardiogram (ECG) derived heart rate (beats per minute, bpm) and respiratory inductance plethysmography thorax (RIP thorax) derived breathing rate (cycles per minute, cpm). We also evaluated breathing disturbance indices of snoring and the apnea-hypopnea index (AHI) available from the WSA.
Results:
All three contactless technologies provided acceptable accuracy in estimating heart rate [mean absolute error (MAE) < 2.2 bpm and mean absolute percentage error (MAPE) < 5%] and breathing rate (MAE ≤ 1.6 cpm and MAPE < 12%) at 1 minute resolution. All three contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared to PSG estimates (R-squared: WSA Snore: 0.76, p < 0.001; WSA AHI: 0.59, p < 0.001).
Conclusion:
Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community dwelling older adults at scale. They enable assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring which may provide insight into health trajectories.
... Sensors and commercial wearable devices have been widely used to monitor vital signs in COVID-19 patients over long periods of time (15)(16)(17)(18)(19). Studies utilizing data from fitbit devices have shown modification in the Resting Heart Rate (RHR) for up to 3 months following symptom onset with substantial intraindividual variability. ...
Background:
There is increasing evidence that COVID-19 survivors are at increased risk of experiencing a wide range of cardiovascular complications post infection; however, there are no validated models or clear guidelines for remotely monitoring the cardiac health of COVID-19 survivors.
Objective:
This study aims to test a virtual, in-home healthcare monitoring model of care for detection of clinical symptoms and impacts on COVID-19 survivors. It also aims to demonstrate system usability and feasibility.
Methods:
This open label, prospective, descriptive study was conducted in South Western Sydney. Included in the study were patients admitted to the hospital with the diagnosis of COVID-19 between June 2021 and November 2021. Eligible participants after consent were provided with a pulse oximeter to measure oxygen saturation and a S-Patch EX to monitor their electrocardiogram (ECG) for a duration of 3 months. Data was transmitted in real-time to a mobile phone via Bluetooth technology and results were sent to the study team via a cloud-based platform. All the data was reviewed in a timely manner by the investigator team, for post COVID-19 related symptoms, such as reduction in oxygen saturation and arrhythmia.
Outcome measure:
This study was designed for feasibility in real clinical setting implementation, enabling the study team to develop and utilise a virtual, in-home healthcare monitoring model of care to detect post COVID-19 clinical symptoms and impacts on COVID-19 survivors.
Results:
During the study period, 23 patients provided consent for participation. Out of which 19 patients commenced monitoring. Sixteen patients with 81 (73.6%) valid tests were included in the analysis and amongst them seven patients were detected by artificial intelligence to have cardiac arrhythmias but not clinically symptomatic. The patients with arrhythmias had a higher occurrence of supraventricular ectopy, and most of them took at least 2 tests before detection. Notably, patients with arrhythmia had significantly more tests than those without [t-test, t (13) = 2.29, p < 0.05].
Conclusions:
Preliminary observations have identified cardiac arrhythmias on prolonged cardiac monitoring in 7 out of the first 16 participants who completed their 3 months follow-up. This has allowed early escalation to their treating doctors for further investigations and early interventions.
... The majority of the papers were from the United States (n = 12) (12,13,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and Japan (n = 9) (30-38), and one each from Italy (39), Germany (14), France (40) (26,35,39,40), and one of a case series (21). ...
... Ten studies used UWB technology (12, 13, 20, 22-25, 27, 28, 41). Other technologies used included RFID (IC tag) (n = 8) (30,31,(33)(34)(35)(36)(37)(38), radio waves (Emerald R ) (n = 3) (21,26,29), Bluetooth (n = 3) (32, 39,43), and one each of GPS technology (42) passive IR (40) and an unspecified wireless mesh network (Wi-Fi) (14). ...
... They found that those with Alzheimer's dementia had longer distances walked (mean 575 meters) compared to those with vascular dementia (mean 312 meters). Zhang et al. (29) tracked the activity of three residents as they recovered from COVID-19. They were able to demonstrate that sleep and motor abnormalities persisted in these residents for months after recovery compared to the baseline data which was obtained at the start of monitoring. ...
Introduction
There has been growing interest in using real-time location systems (RTLS) in residential care settings. This technology has clinical applications for locating residents within a care unit and as a nurse call system, and can also be used to gather information about movement, location, and activity over time. RTLS thus provides health data to track markers of health and wellbeing and augment healthcare decisions. To date, no reviews have examined the potential use of RTLS data in caring for older adults with cognitive impairment living in a residential care setting.
Objective
This scoping review aims to explore the use of data from real-time locating systems (RTLS) technology to inform clinical measures and augment healthcare decision-making in the care of older adults with cognitive impairment who live in residential care settings.
Methods
Embase (Ovid), CINAHL (EBSCO), APA PsycINFO (Ovid) and IEEE Xplore databases were searched for published English-language articles that reported the results of studies that investigated RTLS technologies in persons aged 50 years or older with cognitive impairment who were living in a residential care setting. Included studies were summarized, compared and synthesized according to the study outcomes.
Results
A total of 27 studies were included. RTLS data were used to assess activity levels, characterization of wandering, cognition, social interaction, and to monitor a resident’s health and wellbeing. These RTLS-based measures were not consistently validated against clinical measurements or clinically important outcomes, and no studies have examined their effectiveness or impact on decision-making.
Conclusion
This scoping review describes how data from RTLS technology has been used to support clinical care of older adults with dementia. Research efforts have progressed from using the data to track activity levels to, most recently, using the data to inform clinical decision-making and as a predictor of delirium. Future studies are needed to validate RTLS-based health indices and examine how these indices can be used to inform decision-making.
... A fascinating proposal is to focus radar-based localization systems on gait analysis, vital signs, sleep monitoring, and fall prevention for older patients, especially in indoor settings. In Figure 2, the Emerald sensor is presented [61]. It has been validated for different clinical settings: it operates by transmitting very low power wireless signals and infers respiratory signals, gait speed, sleep patterns and time spent in different locations at home (activity graph) by the analysis of signal reflections due to human and inanimate objects [62,63]. ...
... A deeper investigation on people's everyday activities can provide different insights about behavioural phenotyping. For example, the absence of a regular routine could be a sign of subtle agitation and cognitive impairment [61]. ...
... Picture (A) shows the analogy with a Wi-Fi router. in the picture (B), recording of respiration, gait speed, sleep efficiency and daily activities patterns are collected. Finally, the picture (C) exhibits the possibility for qualified clinicians to remote acquire the recorded data (Reproduced under the terms and conditions of the Creative Commons Attribution (CC BY) license from[61]). ...
Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients’ movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.
... This can detect COVID-19 infection and relieve clinical symptoms. There are researchers working on optical biosensors [7], photoacoustic imaging [8 respiration monitoring [9], and neurostimulation [10]. However, combining all these as pects together to form a closed-loop approach to fight COVID-19 in all stages of diseas development is a novel idea. ...
... The mode couplin between resonant microcavity and LSPR provides high sensitivity toward virus bindin on biosensor surfaces [6]. The high-quality resonant feature of the resonant microcavit makes it optimal to resolve small shifts caused by low virus concentration in the specimen There are researchers working on optical biosensors [7], photoacoustic imaging [8], respiration monitoring [9], and neurostimulation [10]. However, combining all these aspects together to form a closed-loop approach to fight COVID-19 in all stages of disease development is a novel idea. ...
The recent COVID-19 pandemic has caused tremendous damage to the social economy and people’s health. Some major issues fighting COVID-19 include early and accurate diagnosis and the shortage of ventilator machines for critical patients. In this manuscript, we describe a novel solution to deal with COVID-19: portable biosensing and wearable photoacoustic imaging for early and accurate diagnosis of infection and magnetic neuromodulation or minimally invasive electrical stimulation to replace traditional ventilation. The solution is a closed-loop system in that the three modules are integrated together and form a loop to cover all-phase strategies for fighting COVID-19. The proposed technique can guarantee ubiquitous and onsite detection, and an electrical hypoglossal stimulator can be more effective in helping severe patients and reducing complications caused by ventilators.
... The majority of the papers were from the United States (n = 12) (12,13,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and Japan (n = 9) (30-38), and one each from Italy (39), Germany (14), France (40) (26,35,39,40), and one of a case series (21). ...
... Ten studies used UWB technology (12, 13, 20, 22-25, 27, 28, 41). Other technologies used included RFID (IC tag) (n = 8) (30,31,(33)(34)(35)(36)(37)(38), radio waves (Emerald R ) (n = 3) (21,26,29), Bluetooth (n = 3) (32, 39,43), and one each of GPS technology (42) passive IR (40) and an unspecified wireless mesh network (Wi-Fi) (14). ...
... They found that those with Alzheimer's dementia had longer distances walked (mean 575 meters) compared to those with vascular dementia (mean 312 meters). Zhang et al. (29) tracked the activity of three residents as they recovered from COVID-19. They were able to demonstrate that sleep and motor abnormalities persisted in these residents for months after recovery compared to the baseline data which was obtained at the start of monitoring. ...
Various technologies have been considered for use in older adults measuring variables such as detection of agitation, falls risk, gait analysis, and physical activity levels. Real-time location systems (RTLS) are a specific technology that provide the ability to track individuals and equipment real time. RTLS has been increasingly used across hospital and
residential care home settings. Healthcare providers can use mobility data to characterize an individual's movement and behaviour patterns. Thus, there is potential to use RTLS health indices data to augment healthcare decisions and measure clinical outcomes. This review aims to map out and describe the available evidence reporting clinical use or validation of clinical measures and decision-making tools using this technology with older adults with cognitive impairment living in residential care.
... Such technology could facilitate home-based interventions that target mobility and cognition, such as virtual exercise programs for cancer survivors. 6,24,25 ...
... That is, the collection of longitudinal sleep data on a large scale can boost epidemiological studies that examine the influence of sleep on health and disease [11]. There are also less cumbersome approaches to sleep monitoring owing to the advancement, adoption, and integration of technology into healthcare in the form of non-contact systems, wearables, and mobile systems [11][12][13][14][15]. These systems capitalize on the strong correlation between bio-vital signs and sleep. ...
Polysomnography is the gold-standard method for measuring sleep but is inconvenient and limited to a laboratory or a hospital setting. As a result, the vast majority of patients do not receive a proper diagnosis. In an attempt to solve this issue, sleep experts are continually looking for unobtrusive and affordable alternatives that can provide longitudinal sleep tracking. Collecting longitudinal data on sleep can accelerate epidemiological studies exploring the effect of sleep on health and disease. These alternatives can be in the form of wearables (e.g., actigraphs) or nonwearable (e.g., under-mattress sleep trackers). To this end, this paper aims to review the several attempts made by researchers toward unobtrusive sleep monitoring, specifically sleep cycle. We have performed a literature search between 2016 and 2021 and the following databases were used for retrieving related articles to unobtrusive sleep cycle monitoring: IEEE, Google Scholar, Journal of Clinical Sleep Medicine (JCSM), and PubMed Central (PMC). Following our survey, although existing devices showed promising results, most of the studies are restricted to a small sample of healthy individuals. Therefore, a broader scope of participants should be taken into consideration during future proposals and assessments of sleep cycle tracking systems. This is because factors such as gender, age, profession, and social class can largely affect sleep quality. Furthermore, a combination of sensors, e.g., smartwatches and under-mattress sleep trackers, are necessary to achieve reliable results. That is, wearables and nonwearable devices are complementary to each other, and so both are needed to boost the field of at-home sleep monitoring.
