Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

Background: Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings. Objective: This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor. Methods: We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices. Results: Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. Conclusions: The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests.

Background: Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines. Objective: This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. Methods: We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable. Results: We found significant correlations between the ring’s and actigraphy’s TST (r=0.86; P<.001), WASO (r=0.41; P<.001), and SE (r=0.47; P<.001). Comparing the watch with actigraphy showed a significant correlation in TST (r=0.59; P<.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (P<.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (P<.001) between female and male groups. Conclusions: In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.

Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the Intensive Care Unit and minors. In this paper, a wearable device with a bio-sensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real-time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system act as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.