This is a literature review paper covering state-of-the-art sleep technologies to measure sleep and clinical sleep disorders. This paper addresses an interdisciplinary audience from a variety of subdomains in engineering and medicine. We reviewed 120 scientific papers, 15 commercial mobile apps, and 4 commercial devices. We selected the papers from scientific publishers including Institute of Electrical and Electronics Engineers (IEEE), Nature, Association for Computing Machinery (ACM), Proceedings of Machine Learning Research, Journal of Informatics in Health and Biomedicine, Plos One, PubMed, and Elsevier and Nature digital libraries. We used Google Scholar with keywords including "sleep monitoring", "sleep monitoring technologies", "non-contact sleep monitoring", "mobile apps for sleep monitoring", "AI in sleep technologies", and "automated sleep staging." The manuscript reviews sleep technologies, including sleep lab technologies such as polysomnography and consumer sleep technologies categorized as ambient room sensors, wearable sensors, bed sensors, mobile apps, and artificial intelligence. We primarily focused on validation and comparison studies of the reviewed technologies. The manuscript also provides an overview of several clinical datasets for sleep staging and taxonomizes the different learning methods. Finally, the manuscript offers our insights and recommendations about the application of the reviewed sleep technologies.

Parkinson’s disease (PD) is a neurological progressive movement disorder, affecting more than 10 million people globally. PD demands a longitudinal assessment of symptoms to monitor the disease progression and manage the treatments. Existing assessment methods require patients with PD (PwPD) to visit a clinic every 3–6 months to perform movement assessments conducted by trained clinicians. However, periodic visits pose barriers as PwPDs have limited mobility, and healthcare cost increases. Hence, there is a strong demand for using telemedicine technologies for assessing PwPDs in remote settings. In this work, we present an in-home telemedicine kit, named iTex (intelligent Textile), which is a patient-centered design to carry out accessible tele-assessments of movement symptoms in people with PD. iTex is composed of a pair of smart textile gloves connected to a customized embedded tablet. iTex gloves are integrated with flex sensors on the fingers and inertial measurement unit (IMU) and have an onboard microcontroller unit with IoT (Internet of Things) capabilities including data storage and wireless communication. The gloves acquire the sensor data wirelessly to monitor various hand movements such as finger tapping, hand opening and closing, and other movement tasks. The gloves are connected to a customized tablet computer acting as an IoT device, configured to host a wireless access point, and host an MQTT broker and a time-series database server. The tablet also employs a patient-centered interface to guide PwPDs through the movement exam protocol. The system was deployed in four PwPDs who used iTex at home independently for a week. They performed the test independently before and after medication intake. Later, we performed data analysis of the in-home study and created a feature set. The study findings reported that the iTex gloves were capable to collect movement-related data and distinguish between pre-medication and post-medication cases in a majority of the participants. The IoT infrastructure demonstrated robust performance in home settings and offered minimum barriers for the assessment exams and the data communication with a remote server. In the post-study survey, all four participants expressed that the system was easy to use and poses a minimum barrier to performing the test independently. The present findings indicate that the iTex glove system has the potential for periodic and objective assessment of PD motor symptoms in remote settings.

UNSTRUCTURED Recent growth of electronic health (e-health) is unprecedented, especially after the COVID-19 pandemic. Within e-health, wearable technology is increasingly adopted since it can offer the remote monitoring of chronic and acute conditions in daily life environments. Wearable technology may be used to monitor and track key indicators of physical and psychological stress in daily life settings, providing helpful information for clinicians. One of the key challenges is to present the extensive wearable data to clinicians in an easily interpretable way for making informed decisions. The purpose of the presented research was to design a webapp dashboard, named CarePortal, for analytic visualizations of wearable data that are meaningful to clinicians. The study was divided into two main research objectives (ROs): (RO1) Understand the needs of clinicians regarding wearable data interpretation and visualization. (RO2) Develop a system architecture of a web app to visualize wearable data and related analytics. We used a wearable dataset collected from 116 adolescent participants who experienced trauma. For two weeks, participants wore a Microsoft Band that logged physiological sensor data such as Heart Rate (HR). A total of 834 days of heart rate data was collected. To design the CarePortal Dashboard, we employed a participatory design approach, interacting directly with clinicians (stakeholders) with backgrounds in clinical psychology and neuropsychology. A total of eight clinicians were recruited from Rhode Island Hospital and University of Massachusetts Memorial Health. The study involved five stages of participatory workshops and began with understanding the needs of clinicians. A User Experience Questionnaire (UEQ) survey was used at the end of the study to quantitatively evaluate the user experience. Physiological metrics such as daily and hourly maximum, minimum, average and standard deviation of HR and HR variability (HRV), along with HR-based activity levels were identified. The study investigated various data visualization graphing methods for wearable data including radar chart, stacked bar plot, scatter plot combined with line plot, simple bar plot, and box plot. We created a CarePortal dashboard after understanding clinicians needs. Results from our Workshops indicate that overall clinicians preferred aggregate information such as daily heart rate instead of continuous heart rate and want to see trends in the wearable sensor data over a period (e.g., days). In the UEQ survey, a score of 1.4 was received which indicated that CarePortal was exciting to use (Q5), a similar score was received indicating CarePortal was leading edge (Q8). On an average, clinicians reported that CarePortal was supportive and can be useful to make informed decisions.

Background Dementia caregivers are at risk for negative health outcomes. Caregiver interventions to address this risk are limited by time and personnel constraints. Mobile technology is one means of reaching many caregivers while mitigating these constraints. The aim of the current project was to obtain initial data from a month‐long beta test of a new mobile health tablet App for dementia caregivers. Method A purposive sample of community‐dwelling dementia caregivers was given access to the CARE‐Well (Caregiver Assessment, Resources, and Education) App for one month. The App consisted of 6 primary sections: (1) Self‐Assessment of Stress and Care Recipient Behavioral Problems; (2) Psychoeducation; (3) Goal Diary; (4) Managing Behavior Problems; (5) Online Message Forum; and (6) Video Library. Feedback was collected from caregivers in a qualitative interview following study completion. Data were analyzed via a mixed‐methods approach. Result Caregivers (n = 10) were White, in their mid‐60s (M = 66.2, SD = 12.12), and well educated (M = 16.00 years, SD = 1.63). Care recipients had either mild (80%) or moderate dementia (20%), and 90% had probable Alzheimer’s disease. The average total time spent on the App across all participants was 754.29 minutes (SD = 1584.12). The most heavily used (in minutes) sections of the App were: Managing Behavior Problems (M = 592.10, SD = 1595.75), Video Library (M = 54.33, SD = 88.18) and Psychoeducation (M = 53.18, SD = 54.50). Qualitative feedback was predominantly positive. Caregivers reported Managing Behavior Problems to be the most helpful section followed by Psychoeducation and Online Message Forum. Caregivers reported difficulty with or disinterest in the Goal Diary section. In general, caregivers reported that the App would have been more helpful earlier in their care recipient’s illness. Conclusion Results indicated that caregivers regularly used the App and provided mostly positive responses to content. Findings highlighted the importance of user feedback and an iterative approach for developing digital health technology for dementia caregivers. Future interventions that are introduced earlier in the disease process may be well‐received and more beneficial to a wider range of dementia caregivers and their care recipients.

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