Noncontact Sensors for Vital Signs Measurement: A Narrative Review

Introduction

Vital signs are key physiological measurements that indicate essential functions of life systems [1]. These include heart rate (HR), respiratory rate (RR), body temperature (BT), blood pressure (BP), and arterial oxygen saturation (SpO2) [2]. Vital signs are crucial for assessing and monitoring the overall health of patients in clinical settings [3]. The degree or pattern of deviation from normal vital signs can predict emergencies, therapeutic outcomes, frequency of emergency room or hospital visits, and the utilization of medical resources [1]. Therefore, healthcare providers should be proficient in appropriately interpreting vital signs and understanding various pathological and physiological factors that can influence vital signs [4].

In clinical practice, vital signs are routinely monitored using devices that are in contact with the patient’s body. Electrocardiogram (ECG) recorders, finger pulse oximeters, respiratory belts, thermometers, and automatic BP cuffs are commonly used devices that touch the patient’s body [5,6]. Depending on the situations, healthcare providers can also manually measure HR using a stethoscope or visually observe chest movements to measure RR [5]. In 2002, Edmonds et al [7] reported high inter-rater variability when 2 independent assessors compared the auscultation results of heart and breath sounds and BP measured by auscultating Korotkoff sounds while viewing pressure measurements from a standard cuff and mercury manometer. In 2017, Keene et al [5] reported that manual measurement of vital signs was less accurate, attributing the main causes to a shortage of personnel and excessive workload for nurses. In 2021, Jones et al [8] reported that contact-based vital signs measurement devices can disrupt patient movement, sleep, and treatment. Bulky monitoring devices can cause emotional distress in both patients and caregivers. Additionally, contact-based devices currently used in clinical settings carry the risk of skin irritation due to some form of contact, such as wet ECG electrodes [8,9]. Therefore, there is a need to develop vital signs measurement devices that can reduce inter-rater variability and the risk of skin disorders, and improve patient convenience. Accordingly, interest in noncontact vital sign monitoring technology is increasing.

A noncontact sensor is a device that can detect the physical characteristics or state of a target object without directly touching it [10]. Noncontact sensors capable of measuring vital signs include red, green, blue (RGB), near-infrared (NIR), far-infrared (FIR), and radar. RGB cameras capture light that passes through the skin tissue during changes in optical skin properties owing to hemoglobin and melanin, capturing the light emitted after passing through the tissue [11]. The captured light can reflect heart-related parameters derived from changes in the pixel intensity [12]. Additionally, RGB cameras can detect periodic chest movements due to changes in lung volume during respiration, allowing the measurement of RR [12]. However, RGB cameras are challenging to apply in low-light or nighttime conditions [13]. NIR operates similarly to RGB cameras, but has an infrared lamp, making it suitable for use in dark conditions [14]. FIR is known as thermal imaging and can detect periodic blood movement from the heart to the head or continuously measure BT by detecting the infrared radiation emitted from the body [15–17]. Radar transmits electromagnetic waves and can detect chest movements through breathing and heartbeats. Analysis of the changing distances between the chest cavity and the antenna of the radar receiver represents the frequency of respiration and heartbeat [18]. Such noncontact vital signs monitoring technologies are noninvasive and inconspicuous and can alleviate patients’ discomfort while enhancing convenience [8,19]. Furthermore, automating the measurement and recording of vital signs can help reduce the workload of nursing staff [19]. Inter-rater reliability issues of contact sensors can be improved through repetitive monitoring with a single device and can significantly lower the risk of skin-related problems because there is no contact between the devices and the skin [20]. This article reviews the medical use of noncontact sensors for measuring vital signs and aimed to determine their potential clinical applicability.

Database Search for Identifying Related Articles

To identify relevant studies, we searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) database for papers published until September 30, 2023. The keywords used for the search were “vital sign,” “monitoring,” and “sensor.” The inclusion criteria for the studies were as follows: 1) measurement of vital signs using noncontact sensors, 2) clinical trials targeting patients with disease, 3) comparison of the performance of noncontact sensors with existing measurement technologies, and 4) written in English. The exclusion criteria were as follows: 1) participation of only healthy individuals; 2) involvement of wearable devices; and 3) technical development reports, reviews, case reports, letters to the editor, conference presentations, or other unidentified types of articles.

In total, 129 articles were identified, 119 of which were excluded after reviewing the titles and abstracts. Ten full texts were confirmed, and 1 technology development report and 6 articles focusing on wearable devices were excluded. Finally, 3 articles [21–23] that met the inclusion criteria were selected. The characteristics of the included studies are summarized in Table 1.

Detection of Vital Signs Using Noncontact Sensors

In 2019, Kim et al [22] investigated the validity of a noncontact radar sensor for monitoring RRs in neonates. Six clinically stable neonates with a gestational age of 37 weeks or more, who were admitted to the neonatal intensive care unit (NICU), participated in the study. The neonates were in a supine position in open-air cribs, and the experiment was conducted when they were stable. An impulse radio ultrawideband radar (IR-UWB) sensor (X4M06; Xandar Kardian, Delaware, USA) was affixed to a tripod and positioned 35 cm orthogonal to the infant’s chest. Additionally, for comparison with IR-UWB, a commonly used impedance pneumography (IP) monitor (BSM-6501K; Nihon Kohden, Tokyo, Japan) was installed. During the experiment, active movements, such as vigorous movements, nursing interventions, crying, or hiccups, were considered as interfering factors in signal acquisition and evaluation. Active movements were classified into 3 states: 1) no or slight movement, 2) small movements of the face and limbs, and 3) large movements of the limbs and trunk. Furthermore, data were collected by excluding vigorous movement to ensure data acquisition in typical clinical situations. To compare the results of the IR-UWB radar and the IP, the measurements were updated every 10 s and continuously averaged. The respiratory signal waveforms of the IR-UWB radar and IP were synchronized on a periodic basis and exhibited a high degree of consistency even in irregular respiratory cycles or apnea periods (r=0.90). Despite the mild discrepancy in consistency between the measurements with increasing levels of neonate movement, there was sufficient agreement. This suggests that radar devices have significant potential for monitoring neonates, especially in the NICU environment, where there is a need for intervention or treatment in emergency situations by clinicians.

In 2020, Sadek et al [23] evaluated the detection performance and feasibility of the RR and HR using a microbend fiber-optic sensor (MFOS) in 10 patients with obstructive sleep apnea. The patients underwent polysomnography (PSG), during which the MFOS mat was placed under the mattress to simultaneously collect vital signs and respiratory data. The MFOS mat was positioned on the upper part of the bed to better recognize the patient’s movements during breathing. The MFOS mat has an integrated memory and a Wi-Fi module for transmitting the collected data. The MFOS mat obtained the data by recording the force exerted by the patient’s movements. The force on the sensor matrix was also generated by the movements of the chest wall due to respiration and cardioballistic effects. Movement was classified through changes in the amplitude of the acquired data; if it was lower than 5 mV, no activity was considered in the bed. Additionally, a pause in breathing lasting >10 s was considered abnormal. Compared with that using PSG, the detection accuracy of sleep apnea using the MFOS mat was approximately 50%. Furthermore, the normalized mean absolute error for RR estimation was 11.4%, the normalized root mean square error was 13.9%, and the mean absolute percentage error was 11.6%. For HR estimation, the normalized mean absolute error, normalized root mean square error, and mean absolute percentage error were 5.4%, 6.5%, and 5.4%, respectively. The noncontact sensor in detecting vital signs and sleep apnea showed potential concerning detection performance and feasibility in comparison with PSG results. However, RR detection exhibited inferior results compared with those of HR detection, indicating the need for improvement.

In 2023, Kagiyama et al [21] evaluated the performance of a noncontact sensor in measuring the vital signs of 16 patients with suspected or confirmed coronavirus disease (COVID-19). Medical staff manually measured the patients’ vital signs using a digital manometer (UA-651BLE, A&D Medical, Tokyo, Japan), digital thermometer (C217, TERUMO, Tokyo, Japan), and pulse oximeter (SP2, TERUMO, Tokyo, Japan) over a period of approximately 4 months. The authors also utilized a noncontact detection system with a mattress-type air pressure sensor (Kaigolog Med, Liquid Design Systems Inc., Tokyo, Japan), which was installed under the bed to measure the RR continuously and automatically. The mattress-type sensor was designed to analyze subtle pressure changes detected during a patient’s respiratory movements to assess the RR. The results from the noncontact sensor were observed and measured by the coordinators and compared with those detected by the manual measurements previously described. The agreement between the measurements provided by the medical staff and the noncontact sensor for systolic BP, diastolic BP, HR, SpO2, BTs, and RR was evaluated using intraclass correlation coefficients. The intraclass correlation coefficients for systolic BP, diastolic BP, HR, SpO2, BTs, and RR were 0.93, 0.86, 0.89, 0.92, 0.83, and 0.89, respectively. These results indicate that the noncontact sensor (mattress-type sensor) has a performance similar to that of conventional methods of measuring vital signs. Therefore, noncontact sensors could be a promising tool in clinical settings, especially in specific situations, such as an elevated risk of infection transmission, including COVID-19.

We reviewed 3 clinical trials [21–23] that utilized noncontact sensors to detect vital signs. According to the included studies [21–23], noncontact sensors demonstrated high validity and reliability in measuring vital signs, and it is anticipated that they can be effectively utilized in clinical settings.

Characteristics of the Introduced Noncontact Sensors

The noncontact sensors used in the included studies were IR-UWB [22], MFOS [23], and mat-type air pressure sensors [21] (Figure 1). IR-UWB detects and measures the distance to objects by transmitting and receiving radio frequency bands [24]. IR-UWB can avoid multipath interference issues using an extensive frequency bandwidth [25]. It reduces the power consumption by employing short pulse widths and high bandwidths [26]. Additionally, IR-UWB can penetrate patients’ clothing or blankets, extracting high-resolution respiratory signals [22,27]. It can be used in dark environments or at night because it is not affected by ambient lighting conditions or skin color [22,24]. The IR-UWB sensor is simple in structure and compact, allowing easy installation and mobility of the devices [27]. The radar waveforms of IR-UWB indicate diaphragmatic activity and chest wall dynamics [28]. Therefore, radar waveforms can contain information about heartbeat, apnea, irregular breathing, and the amount of movement [22,29]. Kim et al [22] demonstrated accurate RR measurements in neonates in the NICU using IR-UWB. However, the movements of the neonates slightly affected the accuracy of the radar measurements. Therefore, it is necessary to adjust the signal noise based on the degree of movement to enhance the sensing accuracy.

In MFOS, microbending is an increase in attenuation caused by high-frequency longitudinal perturbations to the waveguide [30]. Microbend sensors use multimode optical fibers and are characterized by a decrease in light intensity with mechanical bending [31]. An optical fiber is a flexible fiber that can transmit light [32]. Changes in optical fibers indirectly modulate the internal optical waves, allowing the sensor to detect and perceive changes in the external environment [33]. Optical fiber sensors leverage the properties of optical fibers to detect various physical or environmental changes, such as pressure, temperature, and acceleration [34]. Optical fiber sensors are considered excellent for monitoring key physiological parameters, such as HR and RR. This is attributed to the resistance of optical fibers to electrical interference or electromagnetic noise and the high sensitivity of optical fiber sensors to environmental conditions [35]. Furthermore, integrated into optical fiber cables, these sensors are compact, lightweight, and easily adaptable to various household items, including cushions, chairs, and beds [23,35]. Sadek et al [23] measured the HR and RR using MFOS by monitoring the chest and abdominal movements of patients with sleep apnea and successfully detected sleep apnea events. Although the MFOS demonstrates satisfactory results in measuring vital signs and detecting sleep apnea, the accuracy of respiratory detection decreases with the severity of obstructive sleep apnea. The current performance of the MFOS is considered suitable for early detection of obstructive sleep apnea and prevention of further complications, emphasizing the need for further research to enhance sensor performance.

A mat-type air pressure sensor is integrated into surfaces, such as a mattress, to detect user movements, pressure distribution corresponding to movements, or heartbeats [36]. Air mattress sensors continuously measure subtle vibrations generated by a patient’s body, classify them into respiratory and heartbeat frequency components, and demonstrate high validity and accuracy [37,38]. As the user naturally applies pressure to the mat-type sensor while lying down, vital signs can be detected comfortably without interruption [39]. Kagiyama et al [21] evaluated the accuracy of a mat-type air pressure sensor for measuring vital signs in patients with COVID-19. The mat-type air pressure sensor automatically analyzes subtle pressure changes corresponding to the patient’s respiratory movements and provides reliable results.

Comparison of Traditional Devices and Noncontact Sensors for Vital Signs Measurement

Traditional methods for measuring vital signs are invasive or involve attaching sensors, such as electrode sensors, for direct contact with the body [40]. Invasive procedures can cause fear in patients, and require clean equipment. Contact-based vital sign measurement methods pose potential risks for skin conditions (such as allergic reactions to acrylic adhesives in disposable conductive hydrogel-based ECG electrodes) [41]. Additionally, the periodic replacement of sensors attached to the skin can cause discomfort to patients and increase the workload of healthcare providers [42,43]. Noncontact sensors, primarily wireless or optical technologies, collect vital signs without physical contact with the body [44]. Most noncontact sensors are small, lightweight, and unobtrusive, which can enhance user convenience [45,46]. Therefore, noncontact sensors are a suitable alternative to cumbersome traditional methods of vital signs measurement that involve physical contact with the body [47]. Moreover, noncontact sensors allow for rapid measurements on a large scale and are beneficial for real-time monitoring [48–50]. This is particularly effective for measuring the vital signs of patients with infectious diseases, helping healthcare providers avoid the risk of infection [51]. In addition, contactless sensors can be embedded with Wi-Fi modules that can transmit acquired data to healthcare providers in real time, thus reducing the workload for healthcare providers who would otherwise be using traditional methods of recording vital signs measurements [21–23,52,53]. Despite these advantages, noncontact sensors have not been widely used in clinical settings. Some noncontact sensors are sensitive to environmental conditions, particularly when measuring the BT [54,55]. Additionally, random body movements during measurements can lead to stronger reflected signals than essential signals, which requires continuous research on random body movements mitigation [28]. For sensors measuring both HR and RR, the technology should be advanced to separate the 2 signals perfectly. This is because the human heartbeat frequency is 1–3 Hz and the breathing frequency is 0.1–0.9 Hz, and the heartbeat signal has a smaller amplitude compared with that of the breathing signal, which can be easily corrupted by the harmonics of the breathing signal [28]. Considering the diversity of patients, it is important to develop and advance technologies for acquiring and processing their vital signs. Furthermore, the importance of heart rate variability (HRV) in measuring vital signs should be considered. HRV indicates the variation in the time intervals between consecutive cardiac cycles, and it is known to index the activity of the vagus nerve, a main nerve of the parasympathetic nervous system [56–58]. HRV has been recognized as a promising biomarker for various conditions, including cardiovascular mortality, post-infarction mortality, early detection of diabetic neuropathy, and identification of psychological stress levels [59,60]. The development of accurate measurement and analysis technology for HRV using noncontact sensors is expected to serve as the foundation for expanding the application of noncontact sensors to various diseases.

Limitations of the Current Study

This study has several limitations. First, the included studies had small sample sizes. To generalize the performance of noncontact sensors for vital sign measurements and apply them to diverse clinical environments, high-quality studies with more extensive and varied samples encompassing a range of characteristics are needed. Second, the experimental settings in the included studies mostly involved environments without external interventions and the patients were in stable positions or lying down. In actual clinical settings, patients may move dynamically, and medical staff can intervene for treatment. However, the current sensing performance of noncontact sensors can have reduced accuracy with increased patient movement, and the presence of people in the vicinity may interfere with accurate detection of the target’s vital signs. Therefore, future research should focus on improving the accuracy of vital signs measurements under various ranges of movement, and technologies that can accurately distinguish between the target patient and others, such as medical staff or caregivers, need further investigation.

Future Directions

There have been increasing attempts to develop accurate and practical noncontact sensing devices to overcome the limitations of wired devices in measuring vital signs and enable more active and flexible monitoring. However, there are still areas that require clear definitions before these technologies can be applied in practical clinical settings. To ensure that they can fully replace the criterion standard monitoring, a new understanding of how vital signs are measured without contact sensors is necessary to guarantee their accuracy. Can they replace methods for diagnosing arrhythmias using heart or chest wall pressure instead of an ECG? Is it possible to predict CO2 concentrations using breath temperatures from the nose or mouth without pulse oximetry? To answer these questions, the accumulation of clinical evidence and developments in noncontact sensor technology are essential. Furthermore, there is ongoing discussion regarding the appropriate level of accuracy required in various clinical situations. The acceptable level of accuracy differs between intensive care units and general wards, which can significantly affect patient care and prognosis. Therefore, the same technology may need to be applied differently in different clinical settings. This necessitates the use of contact sensors in conjunction with other methods to maximize patient benefits.

As healthcare resources are predicted to become increasingly scarce, noncontact sensor technologies to thoroughly assess patients’ vital signs are crucial. It is important to ensure that advances in technology are accompanied by the accumulation of clinical evidence to ensure safety and benefits to patients and healthcare providers.

Conclusions

Noncontact sensors for vital sign measurement require further development from a technical standpoint, although they offer solutions to several issues associated with traditional measurement devices. These issues include the risk of potential skin disorders, increased workload for healthcare providers, and psychological pressure on patients. Furthermore, noncontact sensors have demonstrated performance levels in vital signs measurement comparable to those of traditional measurement devices. Therefore, noncontact sensors hold significant potential for application in clinical settings.