Table 1.
Systematic review of relevant paper from 2010 up to date of contact less sensors measuring cardiorespiratory signals50
| References | Relevance | Study population (size) | Device | Parameter | Summary |
|---|---|---|---|---|---|
| Blood pressure | |||||
| Liu et al.42 | 2 | Healthy (128) | Cardiac vibration | High correct classification of blood pressure abnormalities | |
| Luo et al.43 | 2 | Healthy (1328) | Facial camera | Innovative machine learning approach to estimate blood pressure in normotensive adults based on facial camera | |
| Cardiac | |||||
| Yan et al.44 | 3 | Cardiologic patients (217) | Facial camera | AF | High sensibility and specificity for AF detection |
| Yan et al.45 | 2 | AF patients (20), healthy (24) | Facial camera | AF | High accuracy of AF detection with low cost approach |
| Benedetto et al.46 | 2 | Healthy (24) | Facial camera | HR | Poor accuracy compared to ECG, especially for low and high heart rate for consumer product (FaceReader™ by Noldus) |
| Brueser et al.47 | 2 | AF patients (10) | Cardiac vibration | AF | High sensitivity for AF detection |
| Brüser et al.48 | 2 | Healthy (8), insomnia patients (25) | Cardiac vibration | HR | Very low beat to beat interval error |
| Couderc et al.49 | 2 | AF patients (11) | Facial camera | AF | Abnormal pulse variability due to AF can be detected, but have to be improved |
| Hoog Antink et al.14 | 2 | Healthy (10), post-surgery patients (14) | Cardiac vibration | HR, HRV | Comparable accuracy in patients and healthy, lower accuracy than ECG |
| 2 | Healthy (21) | Doppler radar | HR | If combined with neural network high accuracy compared to ECG without heavy pre-processing | |
| McDuff et al.51 | 2 | Healthy (67) | Webcam | HR | Detection of daily patterns in cardiovascular signals |
| Paalasmaa et al.52 | 2 | Healthy (46) | Cardiac vibration | HR | Overall high accuracy compared to ECG, high interparticipant variability |
| Pino et al.53 | 2 | Healthy (54) | Cardiac vibration | HR | High accuracy compared to ECG in lying and sitting position |
| Pröll et al.54 | 2 | Hospital patients (42) | Cardiac vibration | HR | Acceptable accuracy in low quality data using EMFit Sensor |
| Sugita et al.55 | 2 | Healthy (39) | Facial camera (PPG) | HR | High accuracy while body movement |
| Wartzek et al.56 | 2 | Healthy (59) | EMF (cECG) | ECG | Robust and reliable heart rate estimation from capacitive ECG |
| Yan et al.57 | 2 | Healthy (40) | Facial camera (PPG) | HR | Acceptable accuracy compared to standard, impaired due to motion artefacts |
| Yu et al.58 | 2 | Healthy elderly (10), geriatric patients (10) | Facial camera (PPG) | HR, HRV | High accuracy compared to standard |
| Zink et al.59 | 2 | Patients with AF (22) | Cardiac vibration | HR | High accuracy compared to ECG during sinus rhythm and during AF |
| Respiration | |||||
| Hsu et al.60 | 3 | Patients with sleep disorders (63) | Cardiac vibration | High correlation of breathing parameters with PSG | |
| Castro et al.61 | 2 | Healthy (15) | EMF (cECG) | Sleep apnoea | High accuracy of respiration signals and apnoea detection derived from contactless ECG |
| Elphick et al.62 | 2 | Healthy adults (41), healthy children (20) | Facial thermal camera | High accuracy of respiration rate compared to the best standard | |
| Ermer et al.63 | 2 | Healthy (26) | Camera; cardiac vibration | Low respiration rates not reliably detectable | |
| Sleep | |||||
| Dafna et al.39 | 2 | PSG patients (150) | Audio recording | Innovative and reliably estimation of Sleep-wake activity and sleep quality parameters using audio recording | |
| Huysmans et al.64 | 2 | Sleep lab patients (114) | Cardiac vibration | Possible sensor for future sleep monitoring if correct synchronization with parameters from PSG | |
| Jung et al.65 | 2 | Healthy (10), OSA patients (10) | Cardiac vibration | Reliably detection of nocturnal awakening and sleep efficiency estimation | |
| Vital signs and other parameter | |||||
| Bennett et al.66 | 3 | Heart failure patients (29) | Cardiac vibration | Respiratory rate was the most important risk-adjusted associate of readmission for HF | |
| Negishi et al.67 | 3 | Healthy (22), influenza patients (28) | Facial thermal camera | Temperature | High accuracy compared to clinical standard |
| Saner et al.11 | 2 | Healthy Old (24) | PIR, cardiac vibration (EMFit) | Health status | Long-term monitoring using PIR and pressure-based sensors are well accepted and feasible for detection of health problems |
| Kang et al.68 | 2 | Healthy (6), patients with OSA (14) | UWB radar | High reliability and accuracy of heart and respiration rate compared to PSG in patients and healthy | |
| Tal et al.69 | 2 | Healthy (63) | Cardiac vibration | High accuracy compared to PSG | |
| Ben-Ari et al.70 | 2 | Healthy adults (16), healthy children (41) | Cardiac vibration | High accuracy compared to clinical standard | |
| Iozzia et al.71 | 2 | Healthy (26) | Facial camera (PPG) | High accuracy in sitting and standing position compared to standard | |
| Michler et al.72 | 2 | Healthy (30) | Radar (24 GHz system) | High accuracy compared to ECG | |
| Valenza et al.73 | 2 | Healthy (60) | Facial camera (PPG) | Rapid changes correctly detected | |
| Sun et al.74 | 2 | Healthy (22), influenza patients (16) | Facial thermal camera | Temperature | Higher sensitivity than conventional fever-based screening approaches |
| Diraco et al.75 | 2 | Healthy (30) | UWB radar | Falls | High accuracy compared to ECG during ADL |
If not other mentioned heart and breathing rate are combined with the term ‘vital signs’, if not other mentioned the participants were adults.
ADL, activities of daily living; AF, atrial fibrillation; cECG, capacitive electrocardiogram; ECG, electrocardiogram; EMF, electromagnetic field; HF, heart failure; HRV, heart rate variability; OSA, obstructive sleep apnoea; PIR, passive infrared sensor; PPG, photoplethysmography; PSG, polysomnography; UWB, ultra-wide band.